Aktuelle wissenschaftliche Veröffentlichungen nach Forschungsgebiet

MINT: Multimodal Imaging-to-Speech Knowledge Transfer for Early Alzheimer's Screening

Alzheimer's disease is a progressive neurodegenerative disorder in which mild cognitive impairment (MCI) marks a critical transition between aging and dementia. Neuroimaging modalities, such as structural MRI, provide biomarkers of this transition; however, their high costs and infrastructure needs limit their deployment at a population scale. Speech analysis offers a non-invasive alternative, but speech-only classifiers are developed independently of neuroimaging, leaving decision boundaries biologically ungrounded and limiting reliability on the subtle CN-versus-MCI distinction. We propose MINT (Multimodal Imaging-to-Speech Knowledge Transfer), a three-stage cross-modal framework that transfers biomarker structure from MRI into a speech encoder at training time. An MRI teacher, trained on 1,228 subjects, defines a compact neuroimaging embedding space for CN-versus-MCI classification. A residual projection head aligns speech representations to this frozen imaging manifold via a combined geometric loss, adapting speech to the learned biomarker space while preserving imaging encoder fidelity. The frozen MRI classifier, which is never exposed to speech, is applied to aligned embeddings at inference and requires no scanner. Evaluation on ADNI-4 shows aligned speech achieves performance comparable to speech-only baselines (AUC 0.720 vs 0.711) while requiring no imaging at inference, demonstrating that MRI-derived decision boundaries can ground speech representations. Multimodal fusion improves over MRI alone (0.973 vs 0.958). Ablation studies identify dropout regularization and self-supervised pretraining as critical design decisions. To our knowledge, this is the first demonstration of MRI-to-speech knowledge transfer for early Alzheimer's screening, establishing a biologically grounded pathway for population-level cognitive triage without neuroimaging at inference.

Potential link of high fat diet and mRNA expression of Alzheimer's disease-related genes in the enteric mucosa of a rat model of Alzheimer's disease

BACKGROUND: High-fat diet (HFD) consumption is linked to Alzheimer's disease (AD). Identifying changes in the mRNA expression due to the ingestion of HFD in the intestine—often called the second brain due to its dense enteric neurons—could offer insights into AD development and progression. OBJECTIVE: This study assesses whether the introduction of HFD at adult-age influence expression of AD-related genes in the intestines of Wild-type (WT) or the amyloid precursor protein/presenilin1 (APP/PS1)-overexpressing Transgenic (TG) rats. METHODS: Twelve-month-old WT and TG rats (male and female) were fed a control diet (CD; 8% energy from fat), or HFD (45% energy from fat) for six months. Ileal tissues were assessed for the mRNA expression of genes responsible for development/progression of AD. RESULTS: The WT HFD-fed rats (compared to CD-fed rats) showed increased mRNA expression of genes involved in the development of AD. In contrast, the TG HFD-fed female group, showed a higher number of upregulated genes compared to their respective CD-fed TG group. In TG HFD-fed rats there was higher mRNA expression of genes crucial for synaptic transmission such as Brain-derived neurotrophic factor in females and Choline acetyltransferase in males. Expression of Plasminogen was higher in HFD-fed TG female rats and HFD-fed WT male rats. Overall, the HFD-fed WT male showed mRNA expression of genes involved in the development of AD. However, HFD-fed TG females were more vulnerable for the progression of AD. It is likely that the enteric Plasminogen plays a major role in gut-brain axis for the development of AD in WT male, and progression of AD in TG female during the consumption of HFD. CONCLUSIONS: The consumption of HFD perturbed the expression of enteric genes known to be involved in amyloid-β generation, clearance, and degradation, in a sex-dependent manner.

Leveraging LLMs for Early Alzheimer's Prediction

We present a connectome-informed LLM framework that encodes dynamic fMRI connectivity as temporal sequences, applies robust normalization, and maps these data into a representation suitable for a frozen pre-trained LLM for clinical prediction. Applied to early Alzheimer's detection, our method achieves sensitive prediction with error rates well below clinically recognized margins, with implications for timely Alzheimer's intervention.

Alzheimer's disease detection based on large language model prompt engineering

In light of the growing proportion of older individuals in our society, the timely diagnosis of Alzheimer's disease has become a crucial aspect of healthcare. In this paper, we propose a non-invasive and cost-effective detection method based on speech technology. The method employs a pre-trained language model in conjunction with techniques such as prompt fine-tuning and conditional learning, thereby enhancing the accuracy and efficiency of the detection process. To address the issue of limited computational resources, this study employs the efficient LORA fine-tuning method to construct the classification model. Following multiple rounds of training and rigorous 10-fold cross-validation, the prompt fine-tuning strategy based on the LLAMA2 model demonstrated an accuracy of 81.31\%, representing a 4.46\% improvement over the control group employing the BERT model. This study offers a novel technical approach for the early diagnosis of Alzheimer's disease and provides valuable insights into model optimization and resource utilization under similar conditions. It is anticipated that this method will prove beneficial in clinical practice and applied research, facilitating more accurate and efficient screening and diagnosis of Alzheimer's disease.

Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker studies. Whether colored fundus photography (CFP) contains retinal structural signatures corresponding to these AD-related risk domains remains unclear. To determine whether deep learning (DL) models can predict 12 AD-related risk factors from CFP and to characterize the retinal structures underlying these predictions, thereby assessing whether CFP reflects pathways to AD vulnerability. Using 62,876 CFPs from 44,501 unique participants from the UK Biobank, DL models were trained to predict 12 factors linked to AD incidence: 6 categorical (sex, smoking, sleeplessness, economic status, alcohol use, depression) and 6 continuous (age, age at completing education, BMI, systolic, diastolic blood pressure, HbA1c). Model performance, model saliency, and saliency-derived scores (CAM-Score) were evaluated and compared to retinal morphometry. The scores were also compared between incident-AD cases (average 8.55 years before onset) and matched controls. Performance of DL ranged from AUROC= 0.5654-0.9480 for categorical and R2=-0.0291-0.7620 for continuous factors, outperforming most of the morphometry-machine learning models. Saliency-based score consistently highlighted biologically meaningful regions, particularly the optic nerve head and retinal vasculature. It also aligned with present morphometric variations. Several saliency-based scores differed significantly between incident AD and matched controls, suggesting potential overlap between retinal correlates of risk factors and preclinical AD-associated changes. CFP encodes retinal signatures linked to AD risk factors. Although not diagnostic, DL-derived retinal representations may uncover biologically meaningful risk-related structural changes mirroring the potential AD vulnerability. ;Accepted to the "Journal of Alzheimer's Disease" for publication

A whole-brain model of amyloid beta accumulation and cerebral hypoperfusion in Alzheimer's disease

Accumulation of amyloid beta proteins is a defining feature of Alzheimer's disease, and is usually accompanied by cerebrovascular pathology. Evidence suggests that amyloid beta and cerebrovascular pathology are mutually reinforcing; in particular, amyloid beta suppresses perfusion by constricting capillaries, and hypoperfusion promotes the production of amyloid beta. Here, we propose a whole-brain model coupling amyloid beta and blood vessel through a hybrid model consisting of a reaction-diffusion system for the protein dynamics and porous-medium model of blood flow within and between vascular networks: arterial, capillary and venous. We discretize the resulting parabolic--elliptic system of PDEs by means of a high-order discontinuous Galerkin method in space and an implicit Euler scheme in time. Simulations in realistic brain geometries demonstrate the emergence of multistability, implying that a sufficiently large pathogenic protein seeds is necessary to trigger disease outbreak. Motivated by the "two-hit vascular hypothesis" of Alzheimer's disease that hypoperfusive vascular damage triggers amyloid beta pathology, we also demonstrate that localized hypoperfusion, in response to injury, can destabilize the healthy steady state and trigger brain-wide disease outbreak.

Four-Stage Alzheimer's Disease Classification from MRI Using Topological Feature Extraction, Feature Selection, and Ensemble Learning

Accurate and efficient classification of Alzheimer's disease (AD) severity from brain magnetic resonance imaging (MRI) remains a critical challenge, particularly when limited data and model interpretability are of concern. In this work, we propose TDA-Alz, a novel framework for four-stage Alzheimer's disease severity classification (non-demented, moderate dementia, mild, and very mild) using topological data analysis (TDA) and ensemble learning. Instead of relying on deep convolutional architectures or extensive data augmentation, our approach extracts topological descriptors that capture intrinsic structural patterns of brain MRI, followed by feature selection to retain the most discriminative topological features. These features are then classified using an ensemble learning strategy to achieve robust multiclass discrimination. Experiments conducted on the OASIS-1 MRI dataset demonstrate that the proposed method achieves an accuracy of 98.19% and an AUC of 99.75%, outperforming or matching state-of-the-art deep learning--based methods reported on OASIS and OASIS-derived datasets. Notably, the proposed framework does not require data augmentation, pretrained networks, or large-scale computational resources, making it computationally efficient and fast compared to deep neural network approaches. Furthermore, the use of topological descriptors provides greater interpretability, as the extracted features are directly linked to the underlying structural characteristics of brain MRI rather than opaque latent representations. These results indicate that TDA-Alz offers a powerful, lightweight, and interpretable alternative to deep learning models for MRI-based Alzheimer's disease severity classification, with strong potential for real-world clinical decision-support systems. ;15 pages, 7 figures

3D MRI-Based Alzheimer's Disease Classification Using Multi-Modal 3D CNN with Leakage-Aware Subject-Level Evaluation

Deep learning has become an important tool for Alzheimer's disease (AD) classification from structural MRI. Many existing studies analyze individual 2D slices extracted from MRI volumes, while clinical neuroimaging practice typically relies on the full three dimensional structure of the brain. From this perspective, volumetric analysis may better capture spatial relationships among brain regions that are relevant to disease progression. Motivated by this idea, this work proposes a multimodal 3D convolutional neural network for AD classification using raw OASIS 1 MRI volumes. The model combines structural T1 information with gray matter, white matter, and cerebrospinal fluid probability maps obtained through FSL FAST segmentation in order to capture complementary neuroanatomical information. The proposed approach is evaluated on the clinically labelled OASIS 1 cohort using 5 fold subject level cross validation, achieving a mean accuracy of 72.34% plus or minus 4.66% and a ROC AUC of 0.7781 plus or minus 0.0365. GradCAM visualizations further indicate that the model focuses on anatomically meaningful regions, including the medial temporal lobe and ventricular areas that are known to be associated with Alzheimer's related structural changes. To better understand how data representation and evaluation strategies may influence reported performance, additional diagnostic experiments were conducted on a slice based version of the dataset under both slice level and subject level protocols. These observations help provide context for the volumetric results. Overall, the proposed multimodal 3D framework establishes a reproducible subject level benchmark and highlights the potential benefits of volumetric MRI analysis for Alzheimer's disease classification. ;5 tables, 6 figures, Submitted to International Conference on Power, Electronics, Communications, Computing, and Intelligent Infrastructure 2026

Addressing accuracy and hallucination of LLMs in Alzheimer's disease research through knowledge graphs

In the past two years, large language model (LLM)-based chatbots, such as ChatGPT, have revolutionized various domains by enabling diverse task completion and question-answering capabilities. However, their application in scientific research remains constrained by challenges such as hallucinations, limited domain-specific knowledge, and lack of explainability or traceability for the response. Graph-based Retrieval-Augmented Generation (GraphRAG) has emerged as a promising approach to improving chatbot reliability by integrating domain-specific contextual information before response generation, addressing some limitations of standard LLMs. Despite its potential, there are only limited studies that evaluate GraphRAG on specific domains that require intensive knowledge, like Alzheimer's disease or other biomedical domains. In this paper, we assess the quality and traceability of two popular GraphRAG systems. We compile a database of 50 papers and 70 expert questions related to Alzheimer's disease, construct a GraphRAG knowledge base, and employ GPT-4o as the LLM for answering queries. We then compare the quality of responses generated by GraphRAG with those from a standard GPT-4o model. Additionally, we discuss and evaluate the traceability of several Retrieval-Augmented Generation (RAG) and GraphRAG systems. Finally, we provide an easy-to-use interface with a pre-built Alzheimer's disease database for researchers to test the performance of both standard RAG and GraphRAG.

Leveraging Video Vision Transformer for Alzheimer's Disease Diagnosis from 3D Brain MRI

Alzheimer's disease (AD) is a neurodegenerative disorder affecting millions worldwide, necessitating early and accurate diagnosis for optimal patient management. In recent years, advancements in deep learning have shown remarkable potential in medical image analysis. Methods In this study, we present "ViTranZheimer," an AD diagnosis approach which leverages video vision transformers to analyze 3D brain MRI data. By treating the 3D MRI volumes as videos, we exploit the temporal dependencies between slices to capture intricate structural relationships. The video vision transformer's self-attention mechanisms enable the model to learn long-range dependencies and identify subtle patterns that may indicate AD progression. Our proposed deep learning framework seeks to enhance the accuracy and sensitivity of AD diagnosis, empowering clinicians with a tool for early detection and intervention. We validate the performance of the video vision transformer using the ADNI dataset and conduct comparative analyses with other relevant models. Results The proposed ViTranZheimer model is compared with two hybrid models, CNN-BiLSTM and ViT-BiLSTM. CNN-BiLSTM is the combination of a convolutional neural network (CNN) and a bidirectional long-short-term memory network (BiLSTM), while ViT-BiLSTM is the combination of a vision transformer (ViT) with BiLSTM. The accuracy levels achieved in the ViTranZheimer, CNN-BiLSTM, and ViT-BiLSTM models are 98.6%, 96.479%, and 97.465%, respectively. ViTranZheimer demonstrated the highest accuracy at 98.6%, outperforming other models in this evaluation metric, indicating its superior performance in this specific evaluation metric. Conclusion This research advances the understanding of applying deep learning techniques in neuroimaging and Alzheimer's disease research, paving the way for earlier and less invasive clinical diagnosis.

Transcript-Free Lightweight Detection of Alzheimer's Disease from Spontaneous Speech Using Handcrafted MFCC-Dominant Acoustic Biomarkers

It is still hard to find Alzheimer's disease (AD) early, especially when neuroimaging is expensive or tools that depend on language are not available. Spontaneous speech provides a non-invasive signal; however, numerous current methodologies depend on transcripts/ASR or computationally intensive deep models. We offer a simple, audio-only baseline for detecting AD using 176 Cookie Theft recordings from the DementiaBank Pitt corpus (88 AD, 88 controls). WebRTC voice activity detection (VAD) is used to separate speech from non-speech. We take out 99 hand-crafted acoustic-temporal features, including pause and fluency statistics, spectral/prosodic descriptors, and MFCC summaries with Δ and ΔΔ. Evaluation is performed using a stringent speaker-independent GroupShuffleSplit,documenting performance across 30 iterations. A lightweight SVM with an RBF kernel gets an average AUC of 0.674 across runs. For example, a single split has an AUC of 0.742 and an accuracy of 0.657. We also present an exploratory compact-feature analysis utilizing a Top-20 subset ranked by Random Forest importance; since selection is not nested within training splits, these results may be overly optimistic and are not employed for primary conclusions (AUC 0.719). The results indicate that transcript-free spectro-temporal and fluency-related cues can facilitate speaker-independent Alzheimer's disease screening from raw audio, establishing a practical foundation for deployment-oriented research. ;10 pages, 5 figures, 40 references. Submitted for peer review

Neuropsychological and clinical indicators of Lewy body and Alzheimer's pathology

BACKGROUND: Clinical distinction between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) poses significant challenges due to pathological comorbidity. Similar ages of onset and overlapping cognitive and psychiatric symptoms can lead to diagnostic inaccuracy and inappropriate treatment recommendations. OBJECTIVE: Identify the best combination of clinical and neuropsychological predictors of AD, DLB, and mixed DLB/AD neuropathology in dementia patients. METHODS: Using the National Alzheimer's Coordinating Center dataset, we selected either pure AD (n = 189), DLB (n = 21), or mixed DLB/AD (n = 42) patients on autopsy. Neuropsychological and clinical predictors, including core clinical features of DLB, were entered into multivariable logistic regressions. RESULTS: Gait disturbances (odds ratio (OR) = 19.32; p = 0.01), visual-spatial complaints (OR = 6.06; p = 0.03), and visual hallucinations (OR = 31.06; p = 0.002) predicted DLB compared to AD, along with better memory (OR = 3.42; p = 0.003), naming (OR = 3.35; p = 0.002), and worse processing speed (OR = 0.51; p = 0.01). When comparing DLB to DLB/AD, gait disturbances (OR = 6.33; p = 0.01), increased depressive symptoms (OR = 1.44; p = 0.03), and better memory (OR = 3.01; p = 0.004) predicted DLB. Finally, rapid eye movement sleep behavior disorder (RBD) (OR = 6.44; p = 0.004), parkinsonism severity (OR = 1.07; p = 0.02), and lower depressive symptoms (OR = 0.70; p = 0.006) and memory impairment (OR = 0.57; p = 0.02) distinguished DLB/AD from AD. CONCLUSIONS: Our study converges with prior research suggesting specific neuropsychological and clinical features can help distinguish DLB from AD. Neuropsychological differentiation becomes more challenging among mixed pathologies and in advanced cognitive impairment, although the presence of RBD and parkinsonism distinguished DLB. Earlier clinical assessment and incorporation of in vivo and postmortem biomarkers should enhance diagnostic accuracy and understanding of disease characteristics, offering significant relevance for disease-modifying treatments.

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework

Early and reliable detection of Alzheimer's disease (AD) is crucial for timely clinical intervention and improved patient management. It also supports the evaluation of emerging therapeutic strategies. In this paper, we propose a Low-Rank Mixture of Experts (LoRA-MoE) deep learning framework for Alzheimer's disease diagnosis based on handwriting analysis. Handwriting signals provide a non-invasive and scalable digital biomarker that captures subtle cognitive-motor impairments associated with early AD progression. The proposed architecture allows multiple experts to specialize in different handwriting patterns while sharing a common base network. This design enables efficient learning of general representations while reducing interference between experts. Each expert is equipped with lightweight low-rank adapters. This mechanism significantly reduces the number of trainable parameters compared with standard Mixture of Experts (MoE) models and improves training stability. The proposed framework is evaluated on the Diagnosis AlzheimeR WIth haNdwriting (DARWIN) dataset. Extensive experiments are conducted, including ablation studies on key architectural parameters such as hidden dimension size, number of experts, and LoRA rank. The method is compared with multilayer perceptron (MLP) and conventional MoE architectures. In addition, stacking ensemble strategies (StackMean and StackMax) are investigated to improve robustness and predictive performance. Experimental results show that the LoRA-MoE framework achieves powerful diagnostic performance while activating significantly fewer parameters during inference. These results highlight the potential of the proposed approach as an accurate and computationally efficient solution for handwriting-based Alzheimer's disease screening and digital health applications. ;26 pages, 6 figures, and 17 tables

Executive functioning and processing speed as predictors of global cognitive decline in Alzheimer's disease

BACKGROUND: Better cognitive tools to predict disease progression in mild cognitive impairment (MCI) and Alzheimer's disease (AD) are needed. OBJECTIVE: In this prospective longitudinal cohort, we are testing if changes in the cognitive domains of executive functioning and processing speed can predict global cognitive decline. METHODS: We assessed patients with MCI, AD, and cognitively healthy controls (cHC) using NIH toolbox assessments for processing speed and executive functioning and overall cognitive decline by the Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog). RESULTS: Among 184 participants over a median follow-up of 540 days, both between- and within-subjects variance in NIH toolbox and ADAS-Cog assessments increased from cHC to MCI to AD patients. Among patients with AD (n = 24), pattern comparison processing speed (PCPS) and dimensional change card sort tests (DCCS) declined at 3 and 6 months prior to global cognitive decline (p = 0.008 and 0.0012). A 5-point decrease in either PCPS or DCCS increased risk of global cognitive decline (HR 1.32 (1.08–1.60) and 1.62 (1.16–2.26)). CONCLUSIONS: Testing for cognitive domains of processing speed and executive functioning may predict subsequent global cognitive.

Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits. We propose a probabilistic framework that combines ordinal diagnosis prediction, multi-horizon trajectory generation, and decomposed uncertainty estimation. A Temporal Fusion Transformer encoder is adapted with a CORAL ordinal output layer, asymmetric loss weighting, and converter oversampling to respect disease-stage ordering and improve sensitivity to MCI-to-dementia transitions. Conditioned on the learned patient-context representation, an autoregressive Mixture Density Network generates five-year probabilistic trajectories for diagnosis state, CDR Sum of Boxes, MMSE orientation, and hippocampal volume. On ADNI, the model outperforms linear, recurrent, and transformer baselines for next-visit diagnosis prediction, with the strongest gains on MCI-versus-dementia discrimination. Generated trajectories achieve near-nominal 90% credible interval coverage, widening uncertainty across the forecast horizon, and biomarker dynamics consistent with expected Alzheimer's disease progression. We further separate aleatoric from epistemic uncertainty using analytic mixture variance and a five-member bootstrap ensemble, which provides the strongest encoder diversity and output-level epistemic signal. Epistemic uncertainty is higher for rare progression archetypes, MCI and dementia patients, and under external evaluation on OASIS-3, where it increases alongside prediction error.

AD-CDO: A Lightweight Ontology for Representing Eligibility Criteria in Alzheimer's Disease Clinical Trials

Objective This study introduces the Alzheimer's Disease Common Data Element Ontology for Clinical Trials (AD-CDO), a lightweight, semantically enriched ontology designed to represent and standardize key eligibility criteria concepts in Alzheimer's disease (AD) clinical trials. Materials and Methods We extracted high-frequency concepts from more than 1,500 AD clinical trials on ClinicalTrials.gov and organized them into seven semantic categories: Disease, Medication, Diagnostic Test, Procedure, Social Determinants of Health, Rating Criteria, and Fertility. Each concept was annotated with standard biomedical vocabularies, including the UMLS, OMOP Standardized Vocabularies, DrugBank, NDC, and NLM VSAC value sets. To balance coverage and manageability, we applied the Jenks Natural Breaks method to identify an optimal set of representative concepts. Results The optimized AD-CDO achieved over 63% coverage of extracted trial concepts while maintaining interpretability and compactness. The ontology effectively captured the most frequent and clinically meaningful entities used in AD eligibility criteria. We demonstrated AD-CDO's practical utility through two use cases: (a) an ontology-driven trial simulation system for formal modeling and virtual execution of clinical trials, and (b) an entity normalization task mapping raw clinical text to ontology-aligned terms, enabling consistency and integration with EHR data. Discussion AD-CDO bridges the gap between broad biomedical ontologies and task-specific trial modeling needs. It supports multiple downstream applications, including phenotyping algorithm development, cohort identification, and structured data integration. Conclusion By harmonizing essential eligibility entities and aligning them with standardized vocabularies, AD-CDO provides a versatile foundation for ontology-driven AD clinical trial research.

ADAM: An AI Reasoning and Bioinformatics Model for Alzheimer's Disease Detection and Microbiome-Clinical Data Integration

Alzheimer's Disease Analysis Model (ADAM) is a multi-agent reasoning large language model (LLM) framework designed to integrate and analyze multimodal data, including microbiome profiles, clinical datasets, and external knowledge bases, to enhance the understanding and classification of Alzheimer's disease (AD). By leveraging the agentic system with LLM, ADAM produces insights from diverse data sources and contextualizes the findings with literature-driven evidence. A comparative evaluation with XGBoost revealed a significantly improved mean F1 score and significantly reduced variance for ADAM, highlighting its robustness and consistency, particularly when utilizing human biological data. Although currently tailored for binary classification tasks with two data modalities, future iterations will aim to incorporate additional data types, such as neuroimaging and peripheral biomarkers, and expand them to predict disease progression, thereby broadening ADAM's scalability and applicability in AD research and diagnostic applications. ;12 pages, 7 figures

Multimodal Visual Surrogate Compression for Alzheimer's Disease Classification

High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation learning rely on 3D architectures (e.g., 3D CNNs), slice-wise feature extraction with late aggregation, or apply training-free feature extractions using 2D foundation models (e.g., DINO). However, these three paradigms suffer from high computational cost, loss of cross-slice relations, and limited ability to extract discriminative features, respectively. To address these challenges, we propose Multimodal Visual Surrogate Compression (MVSC). It learns to compress and adapt large 3D sMRI volumes into compact 2D features, termed as visual surrogates, which are better aligned with frozen 2D foundation models to extract powerful representations for final AD classification. MVSC has two key components: a Volume Context Encoder that captures global cross-slice context under textual guidance, and an Adaptive Slice Fusion module that aggregates slice-level information in a text-enhanced, patch-wise manner. Extensive experiments on three large-scale Alzheimer's disease benchmarks demonstrate our MVSC performs favourably on both binary and multi-class classification tasks compared against state-of-the-art methods.

Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection

Imaging and genomic data offer distinct and rich features, and their integration can unveil new insights into the complex landscape of diseases. In this study, we present a novel approach utilizing radiogenomic data including structural MRI images and gene expression data, for Alzheimer's disease detection. Our framework introduces a novel heterogeneous bipartite graph representation learning featuring two distinct node types: genes and images. The network can effectively classify Alzheimer's disease (AD) into three distinct stages:AD, Mild Cognitive Impairment (MCI), and Cognitive Normal (CN) classes, utilizing a small dataset. Additionally, it identified which genes play a significant role in each of these classification groups. We evaluate the performance of our approach using metrics including classification accuracy, recall, precision, and F1 score. The proposed technique holds potential for extending to radiogenomic-based classification to other diseases. ;Comment: 11 pages

Deep Learning-Based Regional White Matter Hyperintensity Mapping as a Robust Biomarker for Alzheimer's Disease

White matter hyperintensities (WMH) are key imaging markers in cognitive aging, Alzheimer's disease (AD), and related dementias. Although automated methods for WMH segmentation have advanced, most provide only global lesion load and overlook their spatial distribution across distinct white matter regions. We propose a deep learning framework for robust WMH segmentation and localization, evaluated across public datasets and an independent Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort. Our results show that the predicted lesion loads are in line with the reference WMH estimates, confirming the robustness to variations in lesion load, acquisition, and demographics. Beyond accurate segmentation, we quantify WMH load within anatomically defined regions and combine these measures with brain structure volumes to assess diagnostic value. Regional WMH volumes consistently outperform global lesion burden for disease classification, and integration with brain atrophy metrics further improves performance, reaching area under the curve (AUC) values up to 0.97. Several spatially distinct regions, particularly within anterior white matter tracts, are reproducibly associated with diagnostic status, indicating localized vulnerability in AD. These results highlight the added value of regional WMH quantification. Incorporating localized lesion metrics alongside atrophy markers may enhance early diagnosis and stratification in neurodegenerative disorders. ;Accepted at SPIE - Medical Imaging Conference 2026

PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis

Deep learning models in medical imaging typically operate as parametric memory, diagnosing patients by recalling fixed knowledge learned during training. This contrasts sharply with clinical practice, where physicians employ analogical reasoning to diagnose new cases by referencing similar records from past exemplars. While In-Context Learning (ICL) frameworks such as Tabular Prior-Fitted Networks (TabPFN) offer a promising diagnosis-by-reference paradigm, they are designed with tabular-specific inductive priors and rely on non-differentiable preprocessing pipelines, leading to manifold mismatch and gradient fracture when applied to heterogeneous multimodal data. To address these limitations, we propose PromptDx, a novel diagnosis-by-reference framework that leverages a pre-trained TabPFN as an ICL engine while enabling seamless integration with multimodal representations. Our core contribution is a Differentiable Prompt Tuning (DPT) mechanism that aligns a Masked Multimodal Modeling module with the pre-trained ICL engine. By training a lightweight adapter as a differentiable surrogate for the engine's non-differentiable preprocessors, we enable an end-to-end optimization of multimodal prompts within the ICL paradigm. We validate our method on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using 3D MRI and tabular biomarkers. Experiments demonstrate that our approach outperforms traditional parametric baselines. Notably, our method achieves superior performance using only 1% context samples compared to 30% in standard ICL, demonstrating exceptional manifold condensation ability. We further validate the generalizability of our DPT framework across six tabular datasets with diverse scales. Overall, our method offers a more data-efficient and clinically aligned paradigm for Alzheimer's Disease diagnosis.

Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification

Objectives: High classification accuracy of Alzheimer's disease (AD) from structural MRI has been achieved using deep neural networks, yet the specific image features contributing to these decisions remain unclear. In this study, the contributions of T1-weighted (T1w) gray-white matter texture, volumetric information, and preprocessing -- particularly skull-stripping -- were systematically assessed. Methods: A dataset of 990 matched T1w MRIs from AD patients and cognitively normal controls from the ADNI database were used. Preprocessing was varied through skull-stripping and intensity binarization to isolate texture and shape contributions. A 3D convolutional neural network was trained on each configuration, and classification performance was compared using exact McNemar tests with discrete Bonferroni-Holm correction. Feature relevance was analyzed using Layer-wise Relevance Propagation, image similarity metrics, and spectral clustering of relevance maps. Results: Despite substantial differences in image content, classification accuracy, sensitivity, and specificity remained stable across preprocessing conditions. Models trained on binarized images preserved performance, indicating minimal reliance on gray-white matter texture. Instead, volumetric features -- particularly brain contours introduced through skull-stripping -- were consistently used by the models. Conclusions: This behavior reflects a shortcut learning phenomenon, where preprocessing artifacts act as potentially unintended cues. The resulting Clever Hans effect emphasizes the critical importance of interpretability tools to reveal hidden biases and to ensure robust and trustworthy deep learning in medical imaging.

Individualized multi-horizon MRI trajectory prediction for Alzheimer's Disease

Neurodegeneration as measured through magnetic resonance imaging (MRI) is recognized as a potential biomarker for diagnosing Alzheimer's disease (AD), but is generally considered less specific than amyloid or tau based biomarkers. Due to a large amount of variability in brain anatomy between different individuals, we hypothesize that leveraging MRI time series can help improve specificity, by treating each patient as their own baseline. Here we turn to conditional variational autoencoders to generate individualized MRI predictions given the subject's age, disease status and one previous scan. Using serial imaging data from the Alzheimer's Disease Neuroimaging Initiative, we train a novel architecture to build a latent space distribution which can be sampled from to generate future predictions of changing anatomy. This enables us to extrapolate beyond the dataset and predict MRIs up to 10 years. We evaluated the model on a held-out set from ADNI and an independent dataset (from Open Access Series of Imaging Studies). By comparing to several alternatives, we show that our model produces more individualized images with higher resolution. Further, if an individual already has a follow-up MRI, we demonstrate a usage of our model to compute a likelihood ratio classifier for disease status. In practice, the model may be able to assist in early diagnosis of AD and provide a counterfactual baseline trajectory for treatment effect estimation. Furthermore, it generates a synthetic dataset that can potentially be used for downstream tasks such as anomaly detection and classification. ;Comment: MICCAI 2024 LDTM workshop

Benchmarking Foundation Speech and Language Models for Alzheimer's Disease and Related Dementia Detection from Spontaneous Speech

Background: Alzheimer's disease and related dementias (ADRD) are progressive neurodegenerative conditions where early detection is vital for timely intervention and care. Spontaneous speech contains rich acoustic and linguistic markers that may serve as non-invasive biomarkers for cognitive decline. Foundation models, pre-trained on large-scale audio or text data, produce high-dimensional embeddings encoding contextual and acoustic features. Methods: We used the PREPARE Challenge dataset, which includes audio recordings from over 1,600 participants with three cognitive statuses: healthy control (HC), mild cognitive impairment (MCI), and Alzheimer's Disease (AD). We excluded non-English, non-spontaneous, or poor-quality recordings. The final dataset included 703 (59.13%) HC, 81 (6.81%) MCI, and 405 (34.06%) AD cases. We benchmarked a range of open-source foundation speech and language models to classify cognitive status into the three categories. Results: The Whisper-medium model achieved the highest performance among speech models (accuracy = 0.731, AUC = 0.802). Among language models, BERT with pause annotation performed best (accuracy = 0.662, AUC = 0.744). ADRD detection using state-of-the-art automatic speech recognition (ASR) model-generated audio embeddings outperformed others. Including non-semantic features like pause patterns consistently improved text-based classification. Conclusion: This study introduces a benchmarking framework using foundation models and a clinically relevant dataset. Acoustic-based approaches -- particularly ASR-derived embeddings -- demonstrate strong potential for scalable, non-invasive, and cost-effective early detection of ADRD.

Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model

Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease. ;preprint

Nonpharmacological intervention therapies for dementia: potential break-even intervention price and savings for selected risk factors in the European healthcare system

Background New effective treatments for dementia are lacking, and early prevention focusing on risk factors of dementia is important. Non-pharmacological intervention therapies aimed at these factors may provide a valuable tool for reducing the incidence of dementia. This study focused on the development of a mathematical model to predict the number of individuals with neurodegenerative diseases, specifically Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis. Scenarios for non-pharmacological intervention therapies based on risk factor reduction were also assessed. The estimated total costs and potential cost savings from societal were included. Methods Based on demographic and financial data from the EU, a mathematical model was developed to predict the prevalence and resulting care costs of neurodegenerative diseases in the population. Each disease (Alzheimer’s disease, Parkinson’s disease, vascular dementia, and amyotrophic lateral sclerosis) used parameters that included prevalence, incidence, and death risk ratio, and the simulation is related to the age of the cohort and the disease stage. Results A replicable simulation for predicting the prevalence and resulting cost of care for neurodegenerative diseases in the population exhibited an increase in treatment costs from 267 billion EUR in 2021 to 528 billion EUR by 2050 in the EU alone. Scenarios related to the reduction of the prevalence of dementia by up to 20% per decade led to total discounted treatment cost savings of up to 558 billion EUR. Conclusion The model indicates the magnitude of the financial burden placed on EU healthcare systems due to the growth in the population prevalence of neurodegenerative diseases in the coming decades. Lifestyle interventions based on reducing the most common risk factors could serve as a prevention strategy to reduce the incidence of dementia with substantial cost-savings potential. These findings could support the implementation of public health approaches throughout life to ultimately prevent premature mortality and promote a healthier and more active lifestyle in older individuals.

Genetic factors for survival in amyotrophic lateral sclerosis: an integrated approach combining a systematic review, pairwise and network meta-analysis

Background The time of survival in patients with amyotrophic lateral sclerosis (ALS) varies greatly, and the genetic factors that contribute to the survival of ALS are not well studied. There is a lack of a comprehensive study to elucidate the role of genetic factors in the survival of ALS. Methods The published studies were systematically searched and obtained from PubMed, EMBASE, and the Cochrane Library without any language restrictions from inception to Oct 27, 2021. A network meta-analysis for ALS causative/risk genes and a systematic review and pairwise meta-analysis for other genetic modifiers were conducted. The PROSPERO registration number: CRD42022311646. Results A total of 29,764 potentially relevant references were identified, and 71 papers were eligible for analysis based on pre-decided criteria, including 35 articles in network meta-analysis for 9 ALS causative/risk genes, 17 articles in pairwise meta-analysis for four genetic modifiers, and 19 articles described in the systematic review. Variants in three genes, including ATXN2 (HR: 3.6), C9orf72 (HR: 1.6), and FUS (HR:1.8), were associated with short survival of ALS, but such association was not identified in SOD1 , TARDBP , TBK1 , NEK1 , UBQLN2 , and CCNF . In addition, UNC13A rs12608932 CC genotype and ZNF521B rs2275294 C allele also caused a shorter survival of ALS; however, APOE ε4 allele and KIFAP3 rs1541160 did not be found to have any effect on the survival of ALS. Conclusions Our study summarized and contrasted evidence for prognostic genetic factors in ALS and would help to understand ALS pathogenesis and guide clinical trials and drug development.

Eye movement alterations in presymptomatic C9orf72 expansion gene carriers

Objective The clinical manifestation of amyotrophic lateral sclerosis (ALS) is characterized by motor neuron degeneration, whereas frontotemporal dementia (FTD) patients show alterations of behavior and cognition. Both share repeat expansions in C9orf72 as the most prevalent genetic cause. Before disease-defining symptoms onset, structural and functional changes at cortical level may emerge in C9orf72 carriers. Here, we characterized oculomotor parameters and their association to neuropsychological domains in apparently asymptomatic individuals with mutations in ALS/FTD genes. Patients and methods Forty-eight carriers of ALS genes, without any clinical symptoms underwent video-oculographic examination, including 22 subjects with C9orf72 mutation, 17 with SOD1 , and 9 with other ALS associated gene mutations ( n  = 3 KIF5A ; n  = 3 FUS/FUS  +  TBK1 ; n  = 1 NEK1 ; n  = 1 SETX ; n  = 1 TDP43 ). A total of 17 subjects underwent a follow-up measurement. Data were compared to 54 age- and gender-matched healthy controls. Additionally, mutation carriers performed a neuropsychological assessment. Results In comparison to controls, the presymptomatic subjects performed significantly worse in executive oculomotor tasks such as the ability to perform correct anti-saccades. A gene mutation subgroup analysis showed that dysfunctions in C9orf72 carriers were much more pronounced than in SOD1 carriers. The anti-saccade error rate of ALS mutation carriers was associated with cognitive deficits: this correlation was increased in subjects with C9orf72 mutation, whereas SOD1 carriers showed no associations. Conclusion In C9orf72 carriers, executive eye movement dysfunctions, especially the increased anti-saccade error rate, were associated with cognitive impairment and unrelated to time. These oculomotor impairments are in support of developmental deficits in these mutations, especially in prefrontal areas.

Cognitive and behavioral involvement in ALS has been known for more than a century

Background Among clinicians and researchers, it is common knowledge that, in ALS, cognitive and behavioral involvement within the spectrum of frontotemporal degenerations (FTDs) begun to be regarded as a fact in the late 1990s of the twentieth century. By contrast, a considerable body of evidence on cognitive/behavioral changes in ALS can be traced in the literature dating from the late nineteenth century. Methods Worldwide reports on cognitive/behavioral involvement in ALS dating from 1886 to 1981 were retrieved thanks to Biblioteca di Area Medica “Adolfo Ferrate,” Sistema Bibliotecario di Ateneo, University of Pavia, Pavia, Italy and qualitatively synthetized. Results One-hundred and seventy-four cases of ALS with co-occurring FTD-like cognitive/behavioral changes, described in Europe, America, and Asia, were detected. Neuropsychological phenotypes were consistent with the revised Strong et al.’s consensus criteria. Clinical observations were not infrequently supported by histopathological, post-mortem verifications of extra-motor, cortical/sub-cortical alterations, as well as by in vivo instrumental exams—i.e., assessments of brain morphology/physiology and psychometric testing. In this regard, as earlier as 1907, the notion of motor and cognitive/behavioral features in ALS yielding from the same underlying pathology was acknowledged. Hereditary occurrences of ALS with cognitive/behavioral dysfunctions were reported, as well as familial associations with ALS-unrelated brain disorders. Neuropsychological symptoms often occurred before motor ones. Bulbar involvement was at times acknowledged as a risk factor for cognitive/behavioral changes in ALS. Discussion Historical observations herewith delivered can be regarded as the antecedents of current knowledge on cognitive/behavioral impairment in the ALS-FTD spectrum.

Disease duration of progression is helpful in identifying isolated bulbar palsy of amyotrophic lateral sclerosis

Background Compared with typical bulbar onset amyotrophic lateral sclerosis (ALS), isolated bulbar palsy (IBP), an often under-understood variant of ALS, is characterized by symptoms confined to bulbar region for extended periods and relative preservation of limb and ventilation function. To find a cutoff value of disease duration that can distinguish IBP from typical bulbar onset ALS well, the association of survival with disease progression in bulbar onset ALS patients was analyzed. Methods Clinical data of bulbar onset ALS patients were collected from January 2009 to December 2013. The duration from bulbar onset to first significant limb involvement was analyzed by a cutoff point analysis with maximally selected log-rank statistics and dichotomized to categorize patient outcomes. The patients were divided into two groups, the IBP and typical bulbar onset ALS groups, according to the cutoff value. Clinical features were compared. Results 115 bulbar onset ALS patients were recruited, and the duration from bulbar onset to first significant limb involvement was associated with survival ( P  < 0.001). The cutoff duration was 20 months. 19 patients were identified as IBP and 96 patients as typical bulbar onset ALS using 20 months as the cutoff duration. Female was more common, limb weakness was less frequent and pure upper motor neuron (UMN) bulbar signs were more frequent in the IBP group than in the typical bulbar onset ALS group ( P  = 0.047; P  = 0.004; P  = 0.031). The median survival time of the IBP group was significantly longer than that of the typical bulbar onset ALS group (64 months and 26 months, respectively; P  < 0.001). Conclusions A cutoff duration of 20 months from bulbar onset to first significant limb involvement may be used to specifically distinguish IBP from typical bulbar onset ALS. IBP was characterized by female predominance, relative preservation of limb function, more pure UMN bulbar signs and a relatively benign prognosis.

Monocarboxylate transporter functions and neuroprotective effects of valproic acid in experimental models of amyotrophic lateral sclerosis

Background Amyotrophic lateral sclerosis (ALS) is a devasting neurodegenerative disorder for which no successful therapeutics are available. Valproic acid (VPA), a monocarboxylate derivative, is a known antiepileptic drug and a histone deacetylase inhibitor. Methods To investigate whether monocarboxylate transporter 1 (MCT1) and sodium-coupled MCT1 (SMCT1) are altered in ALS cell and mouse models, a cellular uptake study, quantitative real time polymerase chain reaction and western blot parameters were used. Similarly, whether VPA provides a neuroprotective effect in the wild-type (WT; hSOD1WT) and ALS mutant-type (MT; hSOD1G93A) NSC-34 motor neuron-like cell lines was determined through the cell viability assay. Results [^3H]VPA uptake was dependent on time, pH, sodium and concentration, and the uptake rate was significantly lower in the MT cell line than the WT cell line. Interestingly, two VPA transport systems were expressed, and the VPA uptake was modulated by SMCT substrates/inhibitors in both cell lines. Furthermore, MCT1 and SMCT1 expression was significantly lower in motor neurons of ALS (G93A) model mice than in those of WT mice. Notably, VPA ameliorated glutamate- and hydrogen peroxide-induced neurotoxicity in both the WT and MT ALS cell lines. Conclusions Together, the current findings demonstrate that VPA exhibits a neuroprotective effect regardless of the dysfunction of an MCT in ALS, which could help develop useful therapeutic strategies for ALS.

The spectrum of behavioral disorders in amyotrophic lateral sclerosis: current view

Behavioral disorders, with an average prevalence of 30–60% are important non-motor symptoms in amyotrophic lateral sclerosis (ALS) that have a negative impact on prognosis, management and quality of life, yet the underlying neurobiology is poorly understood. Among people with ALS, apathy, fatigue, anxiety, irritability and other behavioral symptoms are the most prominent, although less frequent than cognitive impairment. The present review explores the current understanding of behavioral changes in ALS with particular emphasis on our current knowledge about their structural and functional brain correlates, substantiating a multisystem degeneration with particular dysfunction of frontal-subcortical circuits and dysfunction of fronto-striatal, frontotemporal and other essential brain systems. The natural history of behavioral dysfunctions in ALS and their relationship to frontotemporal lobe degeneration (FTLD) are not fully understood, although they form a clinical continuum, suggesting a differential vulnerability of non-motor brain networks, ALS being considered a brain network disorder. An assessment of risks or the early detection of brain connectivity signatures before structural changes may be helpful in investigating the pathophysiological mechanisms of behavioral impairment in ALS. Treatment of both ALS and co-morbid behavioral disorders is a multidisciplinary task, but whereas no causal or disease-modifying therapies for ALS are available, symptomatic treatment of a variety of behavioral symptoms plays a pivotal role in patient care, although the management of behavioral symptoms in clinical care still remains limited.

Deciphering lipid dysregulation in ALS: from mechanisms to translational medicine

Lipids, defined by low solubility in water and high solubility in nonpolar solvents, can be classified into fatty acids, glycerolipids, glycerophospholipids, sphingolipids, and sterols. Lipids not only regulate integrity and fluidity of biological membranes, but also serve as energy storage and bioactive molecules for signaling. Causal mutations in SPTLC1 (serine palmitoyltransferase long chain subunit 1) gene within the lipogenic pathway have been identified in amyotrophic lateral sclerosis (ALS), a paralytic and fatal motor neuron disease. Furthermore, lipid dysmetabolism within the central nervous system and circulation is associated with ALS. Here, we aim to delineate the diverse roles of different lipid classes and understand how lipid dysmetabolism may contribute to ALS pathogenesis. Among the different lipids, accumulation of ceramides, arachidonic acid, and lysophosphatidylcholine is commonly emerging  as detrimental to motor neurons. We end with exploring the potential ALS therapeutics by reducing these toxic lipids.

Quality of life and depression in patients with amyotrophic lateral sclerosis – does the country of origin matter?

Background Given the inevitable relentless progressing nature of amyotrophic lateral sclerosis (ALS), it is essential to identify factors influencing patients’ wellbeing. The study aimed to prospectively assess factors influencing the quality of life (QoL) and depression in ALS patients compared to healthy controls (HCs) from Poland, Germany and Sweden and their relationship to socio-demographic and clinical factors. Methods 314 ALS patients (120 from Poland, 140 from Germany, 54 from Sweden) and 311 age-, sex- and education-level-matched HCs underwent standardized interviews for quality of life, depression, functional status and pain. Results Patients from all three countries showed similar levels of functional impairment (ALSFRS-R). Overall, ALS patients assessed their quality of life as lower compared to HCs (p < 0.001 for the anamnestic comparative self-assessment (ACSA), p = 0.002 for the Schedule for the evaluation of the subjective quality of life - SEIQoL- direct weighting (SEIQoL-DW). Also, the German and Swedish patients, but not the Polish, reported higher depression levels than the corresponding HCs (p < 0.001). Analysis of ALS groups revealed that functional impairment was related to a lower quality of life (ACSA) and higher depression levels among German ALS patients. Longer time since diagnosis predicted lower depression and (in male subjects) higher quality of life. Conclusions ALS patients assess their quality of life and mood lower than healthy individuals within the studied countries. The relationships between clinical and demographic factors are moderated by country of provenance, which bears implications for the design and interpretation of scientific and clinical studies, which should reflect the complexity and heterogeneity of mechanisms determining QoL.

The Gold Coast criteria increases the diagnostic sensitivity for amyotrophic lateral sclerosis in a Chinese population

Objectives The aim of this study was to assess and compare the diagnostic utility of a new diagnostic criteria for amyotrophic lateral sclerosis (ALS), abbreviated as the ‘Gold Coast Criteria’, with the revised El Escorial (rEEC) and Awaji criteria. Methods Clinical and electrophysiological data of 1185 patients from January 2014 to December 2019 in the Peking Union Medical College Hospital ALS database were reviewed. The sensitivity of the Gold Coast criteria was compared to that of the possible rEEC and Awaji criteria (defined by the proportion of patients categorized as definite, probable, or possible ALS). Results A final diagnosis of ALS was recorded in 1162 patients. The sensitivity of the Gold Coast criteria (96.6%, 95% confidence interval [CI] = 95.3%–97.5%) was greater than that of the rEEC (85.1%, 95%CI = 82.9%–87.1%) and Awaji (85.3%, 95%CI = 83.2%–87.3%). In addition, the sensitivity of the novel criteria maintained robust across subgroups, and the advantage was more prominent in limb-onset ALS patients and those who completed electromyographic tests. In those who did not achieve any of the rEEC diagnostic categories, the sensitivity of Gold Coast criteria was 84.4%. Conclusions The current study demonstrated that the Gold Coast criteria exhibited greater diagnostic sensitivity than the rEEC and Awaji criteria in a Chinese ALS population. The application of the Gold Coast criteria should be considered in clinical practice and future therapeutic trials.

Towards clinical application of implantable brain–computer interfaces for people with late-stage ALS: medical and ethical considerations

Individuals with amyotrophic lateral sclerosis (ALS) frequently develop speech and communication problems in the course of their disease. Currently available augmentative and alternative communication technologies do not present a solution for many people with advanced ALS, because these devices depend on residual and reliable motor activity. Brain–computer interfaces (BCIs) use neural signals for computer control and may allow people with late-stage ALS to communicate even when conventional technology falls short. Recent years have witnessed fast progression in the development and validation of implanted BCIs, which place neural signal recording electrodes in or on the cortex. Eventual widespread clinical application of implanted BCIs as an assistive communication technology for people with ALS will have significant consequences for their daily life, as well as for the clinical management of the disease, among others because of the potential interaction between the BCI and other procedures people with ALS undergo, such as tracheostomy. This article aims to facilitate responsible real-world implementation of implanted BCIs. We review the state of the art of research on implanted BCIs for communication, as well as the medical and ethical implications of the clinical application of this technology. We conclude that the contribution of all BCI stakeholders, including clinicians of the various ALS-related disciplines, will be needed to develop procedures for, and shape the process of, the responsible clinical application of implanted BCIs.

Assessing the role of blood pressure in amyotrophic lateral sclerosis: a Mendelian randomization study

Background Observational studies have suggested a close but controversial relationship between blood pressure (BP) and amyotrophic lateral sclerosis (ALS). It remains unclear whether this association is causal. The authors employed a bidirectional two-sample Mendelian randomization (MR) approach to evaluate the causal relationship between BP and ALS. Genetic proxies for systolic blood pressure (SBP), diastolic blood pressure (DBP), antihypertensive drugs (AHDs), ALS, and their corresponding genome-wide association study (GWAS) summary datasets were obtained from the most recent studies with the largest sample sizes. The inverse variance weighted (IVW) method was adopted as the main approach to examine the effect of BP on ALS and four other MR methods were used for sensitivity analyses. To exclude the interference between SBP and DBP, a multivariable MR approach was used. Results We found that genetically determined increased DBP was a protective factor for ALS (OR = 0.978, 95% CI 0.960–0.996, P  = 0.017) and that increased SBP was an independent risk factor for ALS (OR = 1.014, 95% CI 1.003–1.025, P  = 0.015), which is supported by sensitivity analyses. The use of calcium channel blocker (CCB) showed a causal relationship with ALS (OR = 0.985, 95% CI 0.971–1.000, P  = 0.049). No evidence was revealed that ALS caused changes in BP. Conclusions This study provides genetic support for a causal effect of BP and ALS that increased DBP has a protective effect on ALS, and increased SBP is a risk factor for ALS, which may be related to sympathetic excitability. Blood pressure management is essential in ALS, and CCB may be a promising candidate.

Pathological neural networks and artificial neural networks in ALS: diagnostic classification based on pathognomonic neuroimaging features

The description of group-level, genotype- and phenotype-associated imaging traits is academically important, but the practical demands of clinical neurology centre on the accurate classification of individual patients into clinically relevant diagnostic, prognostic and phenotypic categories. Similarly, pharmaceutical trials require the precision stratification of participants based on quantitative measures. A single-centre study was conducted with a uniform imaging protocol to test the accuracy of an artificial neural network classification scheme on a cohort of 378 participants composed of patients with ALS, healthy subjects and disease controls. A comprehensive panel of cerebral volumetric measures, cortical indices and white matter integrity values were systematically retrieved from each participant and fed into a multilayer perceptron model. Data were partitioned into training and testing and receiver-operating characteristic curves were generated for the three study-groups. Area under the curve values were 0.930 for patients with ALS, 0.958 for disease controls, and 0.931 for healthy controls relying on all input imaging variables. The ranking of variables by classification importance revealed that white matter metrics were far more relevant than grey matter indices to classify single subjects. The model was further tested in a subset of patients scanned within 6 weeks of their diagnosis and an AUC of 0.915 was achieved. Our study indicates that individual subjects may be accurately categorised into diagnostic groups in an observer-independent classification framework based on multiparametric, spatially registered radiology data. The development and validation of viable computational models to interpret single imaging datasets are urgently required for a variety of clinical and clinical trial applications.

PP2A and GSK3 act as modifiers of FUS-ALS by modulating mitochondrial transport

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease which currently lacks effective treatments. Mutations in the RNA-binding protein FUS are a common cause of familial ALS, accounting for around 4% of the cases. Understanding the mechanisms by which mutant FUS becomes toxic to neurons can provide insight into the pathogenesis of both familial and sporadic ALS. We have previously observed that overexpression of wild-type or ALS-mutant FUS in Drosophila motor neurons is toxic, which allowed us to screen for novel genetic modifiers of the disease. Using a genome-wide screening approach, we identified Protein Phosphatase 2A (PP2A) and Glycogen Synthase Kinase 3 (GSK3) as novel modifiers of FUS-ALS. Loss of function or pharmacological inhibition of either protein rescued FUS-associated lethality in Drosophila . Consistent with a conserved role in disease pathogenesis, pharmacological inhibition of both proteins rescued disease-relevant phenotypes, including mitochondrial trafficking defects and neuromuscular junction failure, in patient iPSC-derived spinal motor neurons (iPSC-sMNs). In FUS-ALS flies, mice, and human iPSC-sMNs, we observed reduced GSK3 inhibitory phosphorylation, suggesting that FUS dysfunction results in GSK3 hyperactivity. Furthermore, we found that PP2A acts upstream of GSK3, affecting its inhibitory phosphorylation. GSK3 has previously been linked to kinesin-1 hyperphosphorylation. We observed this in both flies and iPSC-sMNs, and we rescued this hyperphosphorylation by inhibiting GSK3 or PP2A. Moreover, increasing the level of kinesin-1 expression in our Drosophila model strongly rescued toxicity, confirming the relevance of kinesin-1 hyperphosphorylation. Our data provide in vivo evidence that PP2A and GSK3 are disease modifiers, and reveal an unexplored mechanistic link between PP2A, GSK3, and kinesin-1, that may be central to the pathogenesis of FUS-ALS and sporadic forms of the disease.

Plasma exchange with albumin replacement and disease progression in amyotrophic lateral sclerosis: a pilot study

Background Plasma exchange (PE) is used to treat a range of neurological disorders. Based on results demonstrated in Alzheimer’s disease, we theorized that PE with albumin replacement (PE-A) might alter the metabolic profile of plasma and cerebrospinal fluid in patients with amyotrophic lateral sclerosis (ALS) by removing disease-inducing molecules. The aim of this study was to evaluate the effect of PE-A on disease progression in ALS. Methods In this open-label, non-controlled, single-arm, prospective pilot study, 13 adults with ALS had 6 months’ treatment with PE-A 5% and 6 months’ follow-up. Primary endpoints were changes from baseline in the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) score and forced vital capacity (FVC) through 48 weeks. A post hoc analysis compared individual patient data with the expected ALSFRS-R progression slope. Results The median ALSFRS-R score declined throughout the study, although the rate of decline was slower than expected in seven patients at treatment end and in five patients at study end. Six patients remained in the same baseline slope progression category, and four patients improved their slope category at treatment end. Median FVC decreased significantly during the study. Treatment was well tolerated. Of 330 PE-A procedures, 0.9% were associated with potentially related adverse events. Conclusion Although functional impairment progressed, about two-thirds of patients showed a slower than expected rate of decline at treatment end. Most patients had unaltered (54.5%) or reduced (36.4%) ALSFRS-R slope progression at treatment end. Further evaluation of PE-A in controlled studies involving more patients is warranted. EudraCT number 2013-004842-40. Trial registration ClinicalTrials.gov identifier: NCT02479802.

Widespread CNS pathology in amyotrophic lateral sclerosis homozygous for the D90A SOD1 mutation

Mutations in the gene encoding the ubiquitously expressed free radical scavenging enzyme superoxide dismutase-1 ( SOD1 ) are found in 2–6% of amyotrophic lateral sclerosis patients. The most frequent SOD1 mutation worldwide is D90A. Amyotrophic lateral sclerosis caused by this mutation has some unusual features: the heredity is usually recessive, the phenotype is stereotypic with slowly evolving motor symptoms beginning in the legs and may also include sensory, autonomic, and urinary bladder involvement. Furthermore, the mutant protein resembles the wild type, with normal content and enzymatic activity in the central nervous system. Here, we report neuropathological findings in nine patients homozygous for the D90A mutation. All nine had numerous small granular inclusions immunoreactive for misfolded SOD1 in motor neurons and glial nuclei in the spinal cord and brainstem. In addition to degeneration of the corticospinal tracts, all patients had degeneration of the dorsal columns. We also found intense gliosis in circumscribed cortical areas of the frontal and temporal lobes and in the insula. In these areas and in adjacent white matter, there were SOD1 staining neuropil threads. A few SOD1-immunopositive cytoplasmic neuronal inclusions were observed in cortical areas, as were glial nuclear inclusions. As suggested by the symptoms and signs and earlier neurophysiological and imaging investigations, the histopathology in patients homozygous for the D90A SOD1 extends beyond the motor system to include cognitive and sensory cortical areas. However, even in the patients that had a symptomatic disease duration of more than 2 or 3 decades and lived into their 70s or 80s, there were no SOD1-inclusion pathology and no typical dysfunction (apart from the musculature) in non-nervous organs. Thus, only specific parts of the CNS seem to be vulnerable to toxicity provoked by homozygously expressed mutant SOD1.

Targeted sequencing panels in Italian ALS patients support different etiologies in the ALS/FTD continuum

Background 5–10% of amyotrophic lateral sclerosis (ALS) patients presented a positive family history (fALS). More than 30 genes have been identified in association with ALS/frontotemporal dementia (FTD) spectrum, with four major genes accounting for 60–70% of fALS. In this paper, we aimed to assess the contribution to the pathogenesis of major and rare ALS/FTD genes in ALS patients. Methods We analyzed ALS and ALS/FTD associated genes by direct sequencing or next-generation sequencing multigene panels in ALS patients. Results Genetic abnormalities in ALS major genes included repeated expansions of hexanucleotide in C9orf72 gene (7.3%), mutations in SOD1 (4.9%), FUS (2.1%), and TARDBP (2.4%), whereas variants in rare ALS/FTD genes affected 15.5% of subjects overall, most frequently involving SQSTM1 (3.4%), and CHMP2B (1.9%). We found clustering of variants in ALS major genes in patients with a family history for “pure” ALS, while ALS/FTD related genes mainly occurred in patients with a family history for other neurodegenerative diseases (dementia and/or parkinsonism). Conclusions Our data support the presence of two different genetic components underlying ALS pathogenesis, related to the presence of a family history for ALS or other neurodegenerative diseases. Thus, family history may help in optimizing the genetic screening protocol to be applied.

Racial Disparities in the Diagnosis and Prognosis of ALS Patients in the United States

Background Amyotrophic lateral sclerosis (ALS) is a progressive, fatal disease with largely unknown etiology. This study compares racial differences in clinical characteristics of ALS patients enrolled in the National ALS Registry (Registry). Methods Data from ALS patients who completed the Registry’s online clinical survey during 2013–2022 were analyzed to determine characteristics such as site of onset, associated symptoms, time of symptom onset to diagnosis, and pharmacological and non-pharmacological interventions for White, Black, and other race patients. Results Surveys were completed by 4242 participants. Findings revealed that Black ALS patients were more likely to be diagnosed at a younger age, to have arm or hand initial site of onset, and to experience pneumonia than were White ALS patients. ALS patients of other races were more likely than White ALS patients to be diagnosed at a younger age and to experience twitching. The mean interval between the first sign of weakness and an ALS diagnosis for Black patients was almost 24 months, statistically greater than that of White ( p  = 0.0374; 16 months) and other race patients ( p  = 0.0518; 15.8 months). The mean interval between problems with speech until diagnosis was shorter for White patients (6.3 months) than for Black patients (17.7 months) and other race patients (14.8 months). Conclusions and Relevance Registry data shows racial disparities still exist in the diagnosis and clinical characteristics of ALS patients. Increased recruitment of non-White ALS patients and better characterization of symptom onset between races might aid clinicians in diagnosing ALS sooner, leading to earlier therapeutic interventions.

Accuracy of a machine learning method based on structural and locational information from AlphaFold2 for predicting the pathogenicity of TARDBP and FUS gene variants in ALS

Background In the sporadic form of amyotrophic lateral sclerosis (ALS), the pathogenicity of rare variants in the causative genes characterizing the familial form remains largely unknown. To predict the pathogenicity of such variants, in silico analysis is commonly used. In some ALS causative genes, the pathogenic variants are concentrated in specific regions, and the resulting alterations in protein structure are thought to significantly affect pathogenicity. However, existing methods have not taken this issue into account. To address this, we have developed a technique termed MOVA (method for evaluating the pathogenicity of missense variants using AlphaFold2), which applies positional information for structural variants predicted by AlphaFold2. Here we examined the utility of MOVA for analysis of several causative genes of ALS. Methods We analyzed variants of 12 ALS-related genes ( TARDBP , FUS , SETX , TBK1 , OPTN , SOD1 , VCP , SQSTM1 , ANG , UBQLN2 , DCTN1 , and CCNF ) and classified them as pathogenic or neutral. For each gene, the features of the variants, consisting of their positions in the 3D structure predicted by AlphaFold2, pLDDT score, and BLOSUM62 were trained into a random forest and evaluated by the stratified fivefold cross validation method. We compared how accurately MOVA predicted mutant pathogenicity with other in silico prediction methods and evaluated the prediction accuracy at TARDBP and FUS hotspots. We also examined which of the MOVA features had the greatest impact on pathogenicity discrimination. Results MOVA yielded useful results (AUC ≥ 0.70) for TARDBP , FUS , SOD1 , VCP , and UBQLN2 of 12 ALS causative genes. In addition, when comparing the prediction accuracy with other in silico prediction methods, MOVA obtained the best results among those compared for TARDBP , VCP , UBQLN2 , and CCNF . MOVA demonstrated superior predictive accuracy for the pathogenicity of mutations at hotspots of TARDBP and FUS . Moreover, higher accuracy was achieved by combining MOVA with REVEL or CADD. Among the features of MOVA, the x, y, and z coordinates performed the best and were highly correlated with MOVA. Conclusions MOVA is useful for predicting the virulence of rare variants in which they are concentrated at specific structural sites, and for use in combination with other prediction methods.

Analysis of brain and spinal MRI measures in a common domain to investigate directional neurodegeneration in motor neuron disease

Background Magnetic resonance imaging (MRI) of the brain and cervical spinal cord is often performed in diagnostic evaluation of suspected motor neuron disease/amyotrophic lateral sclerosis (MND/ALS). Analysis of MRI-derived tissue damage metrics in a common domain facilitates group-level inferences on pathophysiology. This approach was applied to address competing hypotheses of directionality of neurodegeneration, whether anterograde, cranio-caudal dying-forward from precentral gyrus or retrograde, dying-back. Methods In this cross-sectional study, MRI was performed on 75 MND patients and 13 healthy controls. Precentral gyral thickness was estimated from volumetric T1-weighted images using FreeSurfer, corticospinal tract fractional anisotropy (FA) from diffusion tensor imaging using FSL, and cross-sectional cervical cord area between C1-C8 levels using Spinal Cord Toolbox. To analyse these multimodal data within a common domain, individual parameter estimates representing tissue damage at each corticospinal tract level were first converted to z -scores, referenced to healthy control norms. Mixed-effects linear regression models were then fitted to these z -scores, with gradients hypothesised to represent directionality of neurodegeneration. Results At group-level, z -scores did not differ significantly between precentral gyral and intracranial corticospinal tract tissue damage estimates (regression coefficient − 0.24, [95% CI − 0.62, 0.14], p  = 0.222), but step-changes were evident between intracranial corticospinal tract and C1 (1.14, [95% CI 0.74, 1.53], p  < 0.001), and between C5 and C6 cord levels (0.98, [95% CI 0.58, 1.38], p  < 0.001). Discussion Analysis of brain and cervical spinal MRI data in a common domain enabled investigation of pathophysiological hypotheses in vivo. A cranio-caudal step-change in MND patients was observed, and requires further investigation in larger cohorts.

Increased incidence of motor neuron disease in Sweden: a population-based study during 2002–2021

Background Motor neuron diseases (MND), with amyotrophic lateral sclerosis constituting most cases, are rare conditions of unknown etiology. There have been reports of an increase in incidence during the latter half of the twentieth century in various Western countries, including Sweden. This study provides updated data on the incidence of MND in Sweden during the last 20 years. Methods Data was obtained from the Swedish National Patient Register on individuals diagnosed with MND from 2002 to 2021 and analysed in relation to group level data for the entire Swedish population. Incidence rates were calculated and presented in relation to year, age, sex, and region. Results In the early 2000s, there was a crude incidence rate of 3.5–3.7 per 100,000 person-years, which then increased to 4.0–4.6 from 2008 onward. Age standardization to the starting year (2002) partially mitigated this increase. The incidence rate was greater among men compared to women and was highest within the age range of 70 to 84 years. There were indications of a higher incidence rate in the northernmost parts of the country, although the difference was not statistically significant. Conclusions The incidence rate of MND in Sweden now seems to have surpassed 4 cases per 100,000 person-years. This is higher when compared to both other European countries and previous Swedish studies. It remains to be determined if this increase reflects an actual increasing incidence of MND in Sweden or is due to other factors such as better registry coverage.

Peak expiratory flow is a reliably household pulmonary function parameter correlates with disease severity and survival of patients with amyotrophic lateral sclerosis

Background Amyotrophic lateral sclerosis (ALS) is an incurable and fatal neurodegenerative disease; most ALS patients die within 3 to 5 years after symptom onset, usually as a consequence of respiratory failure. In the present study, we aim to screen the survival-related pulmonary function parameters, and to explore the predictive value of peak expiratory flow (PEF) in disease severity and prognosis in patients with ALS. Methods The discovery cohort included 202 ALS patients, and the demographic and clinical characteristics of eligible patients were collected and pulmonary function tests were performed using MS-PFT spirometer. In the validation cohort, 62 newly diagnosed ALS patients performed the pulmonary function test by MS-PFT spirometer and household peak flow meter (KOKA) simultaneously. Results Among 12 pulmonary function parameters, FVC, FEV1, PEF, MEF75%, and MVV were identified to be independent predictive factors for survival. PEF was highly correlated with FVC ( r  = 0.797), MVV ( r  = 0.877), FEV1 ( r  = 0.847), and MEF75% ( r  = 0.963). Besides, the values of PEF were positively associated with disease severity (ALSFRS-R score, r_s = 0.539, P  < 0.0001), and negatively associated with progression rate (ΔALSFRS-R, r_s = -0.316, P  < 0.0001). Finally, we also confirmed that the values of KOKA-measured PEF were highly correlated with the ones measured using MS-PFT spirometer ( r  = 0.9644, p  < 0.0001). Conclusions Our work emphasizes the critical role of PFTs in predicting prognosis of ALS patients. PEF is an easily available pulmonary function index, which is also a promising indicator in predicting disease severity and survival for ALS patients.

Optimizing breathlessness management in amyotrophic lateral sclerosis: insights from a comprehensive systematic review

Background Breathlessness is a prevalent symptom affecting the quality of life (QOL) of Amyotrophic Lateral Sclerosis (ALS) patients. This systematic review explored the interventions for controlling breathlessness in ALS patients, emphasizing palliative care (PALC), non-invasive ventilation (NIV), opioids, and non-pharmacological strategies. Methods A comprehensive search of PubMed, Cochrane Library, and Web of Science databases was conducted. Eligibility criteria encompassed adults with ALS or motor neuron disease experiencing breathlessness. Outcomes included QOL and symptom control. Study designs comprised qualitative studies, cohort studies, and randomized controlled trials. Results Eight studies were included, most exhibiting low bias risk, comprising one randomized controlled trial, three cohort studies, two comparative retrospective studies, and two qualitative studies (interviews). Most studies originated from Europe, with one from the United States of America. The participants totaled 3423, with ALS patients constituting 95.6%. PALC consultations significantly improved symptom assessment, advance care planning, and discussions about goals of care. NIV demonstrated efficacy in managing breathlessness, with considerations for device limitations. Opioids were effective, though predominantly studied in non-ALS patients. Non-pharmacological strategies varied in efficacy among patients. Conclusion The findings underscore the need for individualized approaches in managing breathlessness in ALS. PALC, NIV, opioids, and non-pharmacological strategies each play a role, with unique considerations. Further research, especially ALS-specific self-management studies, is warranted.

‘A lightbulb moment’: carers’ experiences of behavioural symptoms in motor neurone disease before and after MiNDToolkit

Background To explore carers’ experiences of behavioural symptoms in Motor Neurone Disease (MND), before and after using the MiNDToolkit, a novel internet-based psychoeducational intervention to support management of behavioural symptoms (BehSymp) in MND. The study also investigated carers’ views and acceptability of MiNDToolkit. Methods A qualitative process evaluation of carers engagement with, and acceptability of, the MiNDToolkit conducted using semi-structured interviews with carers ( n  = 11). All interviews were audio-recorded, professionally transcribed verbatim and analysed thematically. Results Five themes were identified: (1) In the dark: carers’ experiences and reactions to BehSymp; (2) Others can see: the role of HCPs in identifying symptoms – and perceived opportunities for carers to receive support; (3) Shedding light: carers implementation and perceived impact of the MiNDToolkit content; (4) Acceptability and carers’ engagement with MiNDToolkit; (5) Future implementation. Carers’ experience of BehSymp was particularly distressing when symptoms were apparently out of context. MiNDToolkit appeared to support learning that BehSymp were part of MND. Content resonated with carers, who reported learning about the full picture of MND, which led to acceptance and use of newly learned strategies. Engagement with the platform was good, with varied input from HCPs. Greater and nuanced involvement from HCPs seem important to support management of BehSymp. Recommendations for a full-scale trial emerged, including adding a paper booklet to accompany the intervention and creation of new modules on emotional lability, changes in relationships, and transitioning to a care home. Conclusions MiNDToolkit was acceptable to carers overall. Recommended improvements should be actioned in a full-scale trial.

Bisperoxovanadium promotes motor neuron survival and neuromuscular innervation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is the most common motor neuron (MN) disease, with no present cure. The progressive loss of MNs is the hallmark of ALS. We have previously shown the therapeutic effects of the phosphatase and tensin homolog (PTEN) inhibitor, potassium bisperoxo (picolinato) vanadium (bpV[pic]), in models of neurological injury and demonstrated significant neuroprotective effects on MN survival. However, accumulating evidence suggests PTEN is detrimental for MN survival in ALS. Therefore, we hypothesized that treating the mutant superoxide dismutase 1 G93A (mSOD1^G93A) mouse model of ALS during motor neuron degeneration and an in vitro model of mSOD1^G93A motor neuron injury with bpV(pic) would prevent motor neuron loss. To test our hypothesis, we treated mSOD1^G93A mice intraperitoneally daily with 400 μg/kg bpV(pic) from 70 to 90 days of age. Immunolabeled MNs and microglial reactivity were analyzed in lumbar spinal cord tissue, and bpV(pic) treatment significantly ameliorated ventral horn motor neuron loss in mSOD1^G93A mice ( p  = 0.003) while not significantly altering microglial reactivity ( p  = 0.701). Treatment with bpV(pic) also significantly increased neuromuscular innervation ( p  = 0.018) but did not affect muscle atrophy. We also cultured motor neuron-like NSC-34 cells transfected with a plasmid to overexpress mutant SOD1^G93A and starved them in serum-free medium for 24 h with and without bpV(pic) and downstream inhibitor of Akt signaling, LY294002. In vitro, bpV(pic) improved neuronal viability, and Akt inhibition reversed this protective effect ( p  < 0.05). In conclusion, our study indicates systemic bpV(pic) treatment could be a valuable neuroprotective therapy for ALS.

Gallbladder Cancer

Gallbladder cancer is often described as extremely aggressive with a poor prognosis. Most patients with early-stage gallbladder cancer are asymptomatic and will not be diagnosed until late in the disease course, contributing to gallbladder cancer’s generally poor 5-year survival. While gallbladder cancer is the most common biliary cancer, it is relatively rare in developed countries, and few prospective studies on its management exist. At this time, surgery remains the mainstay of treatment, followed by adjuvant chemoradiation. The operative procedure of choice depends on the patient’s presentation and tumor stage. For early-stage tumors (Tis and T1a), simple cholecystectomy can be curative. In more advanced disease, radical cholecystectomy must be performed. Surveillance includes an evaluation and updated cross-sectional imaging every 3–6 months for 2 years and 6–12 months for an additional 3 years after surgery. Despite ideal surgical management, disease recurrence within 24 months is common. The majority of recurrences have a metastatic component with or without local recurrence.

Significant drop in serum C-reactive protein in patients with solid neoplasia and bacterial infection is associated with a better prognosis and identifies candidates for short-course antibiotic therapy

Introduction The greater predisposition to infections, as well as the possibility of a worse response to treatment, can lead to the excessive use of antimicrobials among cancer patients. C-reactive protein (CRP) has gained prominence as a tool for monitoring therapeutic responses and reducing the duration of antibiotic therapy; however, few studies have analyzed this protein in cancer patient populations. We hypothesize that cancer patients with a good response to antibiotic therapy show a faster decline in serum CRP levels, which would allow us to identify candidates for short-course treatments. Objective To evaluate the behavior of serum CRP levels among adult cancer patients using antibiotic therapy, and its association with the duration of this treatment, therapeutic response, and clinical recurrence. Methods This work consisted of a retrospective study with cancer patients admitted to a university hospital between September 2018 and December 2019. Adults (age ≥ 18 years) who underwent at least one course of antibiotic therapy were included. CRP behavior over the first 7 days of treatment was classified as: i) good response: when the CRP value on the fifth day of therapy reached 50% or less of the peak value detected in the first 48 h of treatment, and ii) poor response: Maintenance, within the same interval, of a CRP value > 50% of the peak value in the first 48 h. The duration of antibiotic therapy was categorized as up to seven full days or more. Outcomes were assessed by events that occurred during the 30 days of hospitalization or until hospital discharge. Primary outcome: Clinical recurrence of the index infection. Secondary outcomes: i) Death from any cause; ii) microbiological recurrence; iii) therapeutic response; iv) colitis associated with Clostridioides difficile ; and v) isolation of multi-resistant bacteria, whether in clinical or surveillance samples. Results The final analysis consisted of 212 patients, with a median age (IQ) of 59.2 (48 – 67) years old and a predominance of females (65%), who were hypertensive (35%), smokers (21%), and diabetics (17.8%). There was no difference in clinical recurrence between the two groups (8.1% vs. 12.2%; p  = 0.364), with a lower 30-day mortality in the good CRP response group (32.2% vs. 14.5%; p  = 0.002). Despite the tendency towards a lower occurrence of other secondary outcomes in the good response group, these differences were not statistically significant. In the poor CRP response group, outcomes like clinical recurrence, mortality, and therapeutic response were significantly worse, regardless of the duration of antibiotic treatment. Conclusion In this study, cancer patients with a good CRP response during antibiotic therapy presented lower mortality and a higher proportion of satisfactory therapeutic responses. CRP can be a useful tool when combined with other clinical information in optimizing the duration of antimicrobial treatment in a hospitalized cancer population.

Optimizing the integration of family physicians into cancer survivorship care in the BC Interior: a mixed methods study of physicians’ opinions and experiences

Purpose With cancer diagnoses increasing worldwide and the number of cancer survivors rising, family physicians are being increasingly relied upon to provide post-cancer treatment care and/or survivorship care. This mixed-method study explores and evaluates barriers and facilitators to optimizing family physician-led survivorship care in the largely rural Interior of British Columbia (BC), Canada. Methods A mixed-method approach consisting of quantitative surveys and qualitative interviews was utilized to explore family physician and oncologist perspectives on the current state of survivorship care for breast, lung, prostate, and colorectal cancers within the BC Interior. Results Concerns about family physician knowledge in some aspects of survivorship care were identified by both groups, with mixed responses regarding geographical factors, resources, and communication networks. Success was dependent on information from oncologists with more common cancer sites fitting the model better. There was significant concern for individuals with no family physician. Five frequently discussed facilitators were identified: (1) providing accessible survivorship guidelines, (2) standardized discharge summaries (i.e., survivorship care plans), (3) clear points-of-contact at cancer care centers, (4) more educational opportunities, and (5) compatible electronic supports between care providers. Conclusions Overall, the model of family physician–led survivorship care was supported by family physicians and oncologists within the BC Interior, although success was dependent on several factors. Identifying the perspectives of physicians directly involved in the survivorship care pathway will be instrumental in developing and implementing solutions that will succeed within the BC Interior and similar regions. Implication to for Cancer Survivors We hope that the improvements to interprofessional cooperation driven by our research may improve the quality and continuity of care received by cancer survivors in British Columbia and beyond.  Implication to Cancer Survivors We hope that the improvements to interprofessional cooperation driven by our research may improve the quality and continuity of care received by cancer survivors in British Columbia and beyond.

Radiotherapy in Triple Negative Breast Cancer – Current Standards and Future Directions

Purpose of Review Triple negative breast cancer remains a diagnosis that strikes fear into patients and providers alike due to its associated treatment challenges and natural history. The recent decades have seen significant advances in the field of breast cancer radiation oncology, and in this review we seek to highlight current standards of care, the historic basis for current recommendations, and future directions. Recent Findings Advances in the field of breast radiation oncology have been numerous in the recent decades and include various hypofractionated regimens, new data and nuances in the administration of regional nodal radiation, new systemic agents and combinations with radiotherapy, and novel strategies that radically change current treatment paradigms. In this review, we will navigate the applicability of these data and approaches to the management of triple negative breast cancer. Summary Triple negative breast cancer remains a challenging disease process, but one that we have seen considerable advancement in the management of in the recent decades. We are optimistic that patients will continue to benefit from treatment approaches that improve disease control and quality of life.

Personalizing cancer therapy: the role of pharmacogenetics in overcoming drug resistance and toxicity

Cancer pharmacogenetics has become a cornerstone of precision oncology. It offers the potential to optimize therapeutic outcomes by tailoring treatments to individual genetic profiles. This review explores the central role of pharmacogenomics in addressing key challenges in cancer therapy, including drug resistance, variability in drug response between patients and chemotherapy-induced toxicity. We highlight key pharmacogenetic markers — such as DPYD, TPMT, UGT1A1, CYP2D6, ERCC1 and XRCC1 — that influence the metabolism, efficacy and toxicity of widely used chemotherapeutic agents. In addition, we discuss the role of ATP-binding cassette transporters (ABC transporters), including ABCB1, ABCG2 and ABCC2, in mediating drug resistance and their importance for personalized treatment strategies. Despite significant progress, the clinical translation of pharmacogenomics still faces several challenges. These include the need for large-scale validation of biomarkers in different populations, the integration of gene-environment interactions and the clarification of ethical and privacy concerns related to genetic testing. New technologies such as next-generation sequencing (NGS), liquid biopsies and artificial intelligence (AI)-based predictive models can overcome these obstacles by enabling real-time monitoring of tumor dynamics and refining personalized treatment protocols. The future of cancer pharmacogenetics lies in the integration of multi-omics data encompassing genomics, epigenomics, transcriptomics and proteomics to provide a comprehensive understanding of tumor biology and drug response. Collaboration between academic institutions, pharmaceutical companies and regulatory authorities is essential to establish standardized protocols for biomarker validation and expand pharmacogenomic databases. In addition, the development of ethical frameworks and patient-centered approaches will ensure the responsible integration of pharmacogenomics into clinical practice. By addressing current challenges and utilizing new technologies, cancer pharmacogenetics holds great promise for advancing precision oncology. This review highlights the importance of personalized medicine in improving therapeutic efficacy, minimizing adverse effects and ultimately improving patient outcomes in the fight against cancer.

Prevalence and Outcomes of Emergency Presentations of Colorectal Cancer in Veterans Affairs Health Care System

Background Colorectal cancer (CRC) diagnoses are frequently made through emergency presentations (EPs), a new cancer diagnosis following an emergency care episode or unplanned inpatient admission. The extent and implications of EPs are not well known in the Veterans Affairs (VA) health system, where robust CRC screening protocols exist. The impact of the COVID-19 pandemic on the route of CRC diagnosis also remains unclear. Methods We conducted a retrospective cohort study of all incident CRC cases diagnosed nationally in the VA health care system from 2017 to 2021. We applied a previously validated algorithm to identify CRC EPs and used multivariable logistic regression and Cox proportional hazards models to examine the associations between EPs and CRC stage, treatment, and mortality. Results We identified 9,096 patients with CRC, 28.1% of whom had EPs, with the proportion of EPs increasing over the study period from 26.4% in 2017–2019 to 31.4% in 2020–2021. Patients with EPs were more likely to have advanced stage disease (adjusted OR 1.70; 95% CI 1.53–1.88) and less likely to receive cancer treatment (adjusted OR 0.65; 95% CI 0.56–0.75) than patients without EPs. Patients with EPs also had significantly higher mortality risk (adjusted HR 1.70; 95% CI 1.56–1.84). Conclusion In a large cohort of patients diagnosed with CRC, we found EPs to be common and independently associated with worse cancer outcomes. EPs also increased during the COVID-19 pandemic. Interventions are needed to reduce potentially avoidable EPs and improve outcomes of patients with CRC diagnosis. Graphical Abstract

Novel MRI-based Hyper-Fused Radiomics for Predicting Pathologic Complete Response to Neoadjuvant Therapy in Breast Cancer

International audience; Rationale and Objectives: To propose a novel MRI-based hyper-fused radiomic approach to predict pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer (BC). Materials and Methods: Pretreatment dynamic contrast-enhanced (DCE) MRI and ultra-multi-b-value (UMB) diffusion-weighted imaging (DWI) data were acquired in BC patients who received NAT followed by surgery at two centers. Hyper-fused radiomic features (RFs) and conventional RFs were extracted from DCE-MRI or UMB-DWI. After feature selection, the following models were built using logistic regression and the retained RFs: hyper-fused model, conventional model, and compound model that integrates the hyper-fused and conventional RFs. The output probability of each model was used to generate a radiomic signature. The model's performance was quantified by the area under the receiver-operating characteristic curve (AUC). Multivariable logistic regression was used to identify variables (clinicopathological variables and the generated radiomic signatures) associated with pCR. Results: The training/external test set (center 1/2) included 547/295 women. The hyper-fused models (AUCs=0.81~0.85) outperformed (p&lt;0.05) the conventional models (AUCs=0.74~0.80) in predicting pCR. The compound models (AUCs=0.88~0.93) outperformed (p&lt;0.05) the hyper-fused models and conventional models for pCR prediction. The hyper-fused radiomic signatures (odds ratios=5.70~12.98; p&lt;0.05) and compound radiomic signatures (odds ratios=1.57~7.71; p&lt;0.05) were independently associated with pCR. These are true for the training and external test sets. Conclusion: The hyper-fused radiomic approach had significantly better performance for predicting pCR to NAT than the conventional radiomic approach, and the hyperfused RFs provided incremental discrimination of pCR beyond the conventional RFs. The generated hyper-fused radiomic signatures were independent predictors of pCR.</p></div>

Impact of Radiotherapy on Surgery in Pediatric Cancer Management

This chapter examines the intricate relationship between radiotherapy (RT) and surgery in the management of pediatric cancer, emphasizing the significant role that RT plays in both curative and palliative treatment strategies. Pediatric cancers present unique challenges due to the biological differences between pediatric and adult malignancies, and the impact of RT on long-term development and health. The chapter explores key concepts in radiation oncology that are essential for surgeons, such as clinical target volume, gross tumor volume, and advanced RT techniques like intensity-modulated radiation therapy and proton beam therapy. Additionally, the chapter discusses how RT affects surgical decision-making, timing, and techniques, highlighting the complexities of integrating RT with surgery, particularly in areas such as tissue fibrosis, vascular damage, and delayed wound healing. The impact of RT on the surgical resection of tumors, especially in previously irradiated tissues, is also considered, along with the importance of accurate staging and the surgeon’s role in radiation planning. Further, the chapter addresses the timing of RT relative to surgery, exploring the benefits and challenges of preoperative and postoperative RT, and the optimal sequencing for various pediatric cancers. Finally, the long-term effects of RT on surgical outcomes are reviewed, underscoring the importance of minimizing radiation-induced toxicities to improve the quality of life and overall survival of pediatric cancer patients. This chapter emphasizes the need for a multidisciplinary approach, where careful planning, coordination, and timing between surgery and RT are crucial for optimizing treatment outcomes and minimizing risks.

Oncologic outcomes of surgically treated colorectal cancer in octogenarians: a comparative study using inverse probability of treatment weighting (IPTW)

PURPOSE: Octogenarians constitute a growing number of diagnoses for colorectal cancer. However, the optimal treatment for these increasingly vulnerable octogenarians with colorectal cancer remains a challenging issue. The aim of this study was to evaluate the oncologic outcomes of colorectal cancer, comparing octogenarians (> 80 years) and younger age (60–79 years). METHODS: A total of 657 patients underwent surgery for colorectal cancer between January 2015 and December 2019 at Gangneung asan hospital. Among them, 444 patients over the age of 60 were enrolled. The exclusion criteria were as follows: only local resection, R1 and R2 resection, Stage IV, absence of data in follow-up, concurrent inflammatory bowel disease, concurrent malignancy, and prior history of malignancy. The patients were divided into two groups according to their age: Octogenarian group (OG, aged > 80 years, n = 83), and younger group (YG, aged 60 to 79 years, n = 361). Inverse probability of treatment weight (IPTW) was used to control for confounding factors. RESULTS: We used Inverse Probability of Treatment Weighting (IPTW) to control confounding factors and ensure a balanced comparison between octogenarians (OG) and younger patients (YG). Before IPTW adjustment, the OG had significantly worse 3-year overall survival (90.0% vs. 78.6%, p = 0.045), while 3-year disease-free survival (DFS) was similar between YG and OG (87.8% vs. 83.6%, p = 0.349). Additionally, the OG had a higher rate of emergency surgery (21.7% vs. 11.4%, p = 0.020), higher ASA classification (≥ III in 66.3% vs. 48.8%, p = 0.006), higher overall mortality (43.4% vs. 21.9%, p < 0.001), and less frequent use of adjuvant chemotherapy (17.2% vs. 57.6%, p < 0.001). Multivariate analysis showed that older age (hazard ratio [HR] = 2.177, 95% confidence interval [CI]: 1.452–3.264, p < 0.001), emergency surgery (HR = 1.831, 95% CI: 1.157–2.897, p = 0.010), severe postoperative complications (Clavien-Dindo III-V. HR = 1.357, 95% CI: 1.035–1.779, p = 0.027), higher TNM stage (stage III, HR = 5.143, 95% CI: 2.009–13.167, p < 0.001), and presence of perineural invasion (HR = 1.588, 95% CI: 1.058–2.385, p = 0.026) were significant predictors of worse survival. Similarly, independent factors associated with recurrence included emergency surgery (HR = 2.653, 95% CI: 1.550–4.542, p < 0.001), poor tumor differentiation (HR = 2.842, 95% CI: 1.198–6.743, p = 0.018), higher TNM stage (stage III, HR = 7.826, 95% CI: 2.355–26.016, p < 0.001), and presence of perineural invasion (HR = 1.876, 95% CI: 1.152–3.055, p = 0.011). However, age was not an independent factor associated with recurrence. In the subgroup analysis, the OG group with no or mild complications (Clavien-Dindo classification I-II) had a significantly better 3-year OS compared to those with severe complications (87.7% vs. 37.5%, p = 0.002). After IPTW adjustment, there were no significant differences in OS (73.2% vs. 77.5%, p = 0.120) or DFS (87.2% vs. 87.5%, p = 0.863) between the two groups. These findings suggest that age alone is not a critical determinant of oncologic outcomes once confounding variables are controlled. CONCLUSION: After IPTW adjustment, age was not an independent factor affecting oncologic outcomes. Instead, emergency surgery, severe complications, advanced stage, tumor differentiation, and perineural invasion were significant predictors of survival and recurrence. In the subgroup analysis, octogenarians with no or mild complications had significantly better 3-year OS than those with severe complications. These findings suggest that perioperative management and disease severity, rather than age alone, should guide treatment decisions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12876-025-03882-3.

Epidemiology and Clinical Features of Breast Cancer in Cotonou (2014-2019): Insights from a Population-Based Registry Study

Introduction Breast cancer is the most frequently diagnosed cancer worldwide and a leading cause of cancer-related mortality in women. This study aimed to describe the epidemiological profile and clinical characteristics of breast cancer cases diagnosed between 2014 and 2019 among women residing in Cotonou, Benin. Patients and Method This retrospective cohort study included 277 women with breast cancer registered in the Cotonou Population-Based Cancer Registry between 1 January 2014 and 31 December 2019. Qualitative variables were expressed as frequencies and percentages, whereas continuous variables were summarized as means with standard deviations. Overall survival was estimated using the Kaplan–Meier method. Data analyses were performed using R software (version 3.6.1) and SPSS. Results The crude incidence of breast cancer increased from 16.8 cases per 100,000 population in 2014 to 24.7 in 2019, with a peak of 38.9 in 2018. The mean age at diagnosis was 49.6 ± 11.9 years. Most patients were diagnosed at advanced stages (III and IV), with T4 tumors being the most frequent presentation. Triple-negative breast cancer was the most prevalent molecular subtype. Overall survival was 57% at 1 year and 37% at 5 years. Conclusions Breast cancer in Cotonou predominantly affects relatively young women and is often diagnosed at advanced stages, contributing to poor survival outcomes compared with high-income countries. Strengthening early detection and prevention strategies should be a public health priority.

Cost-effectiveness of Pembrolizumab in Combination with Chemotherapy as Neoadjuvant Treatment and Continued as a Single Agent Adjuvant Treatment for High-Risk Early-Stage Triple-Negative Breast Cancer in Hong Kong

Introduction The phase III randomized KEYNOTE-522 trial demonstrated that pembrolizumab in combination with chemotherapy as neoadjuvant treatment followed by adjuvant pembrolizumab (pembrolizumab + chemotherapy) provided significant improvements in event-free survival (EFS) and overall survival (OS) for patients with high-risk early-stage triple-negative breast cancer (eTNBC). The objective was to assess the cost-effectiveness of pembrolizumab + chemotherapy compared to neoadjuvant chemotherapy alone (chemotherapy) in patients with high-risk eTNBC from a Hong Kong third-party payer perspective. Methods A multistate transition model with four health states (event-free), locoregional recurrence, distant metastases, and death) was developed to assess the lifetime medical costs and health outcomes (3% annual discount), along with incremental cost-effectiveness ratios (ICERs) using efficacy and safety data from the KEYNOTE-522 trial. The health state utilities were derived from KEYNOTE-522 Euro-QoL-five-dimension five-level questionnaire (EQ-5D-5L) data. Costs were expressed in 2022 Hong Kong dollars (HKD). Scenario and sensitivity analyses were performed to assess the robustness of results. Results Over a 32-year time horizon, base case results showed that pembrolizumab + chemotherapy was associated with a 3.42 year longer EFS and expected gains of 3.05 life years (LYs) and 2.45 quality-adjusted life years (QALYs) compared to chemotherapy. The resultant ICERs were HKD 135,200 per QALY gained and HKD 108,463 per LY gained, which were lower than the World Health Organization (WHO) cost-effectiveness threshold of three times gross domestic product (GDP) per capita for Hong Kong of HKD 1,171,308 per QALY. The one-way sensitivity analyses (OWSA) and probabilistic sensitivity analysis (PSA) showed the results were robust across various inputs and alternative scenarios. Conclusion On the basis of the analysis conducted for a 56-year-old cohort with high-risk eTNBC and assumptions in the model, pembrolizumab + chemotherapy represents a cost-effective proposition (as the ICER is approximately 35% of the GDP per capita in Hong Kong) for patients with high-risk eTNBC in Hong Kong. The manuscript outlines the methods and results of a health economic model to estimate the cost-effectiveness of pembrolizumab + chemotherapy compared to neoadjuvant chemotherapy alone (chemotherapy) in patients with high-risk early-stage triple-negative breast cancer in the Hong Kong setting. The results of the manuscript show that the combination of pembrolizumab and chemotherapy provides an additional 3.05 life years and 2.45 quality-adjusted life years. Despite the potential drawbacks of employing gross domestic product (GDP) as a proxy for cost-effectiveness thresholds, the incremental cost-effectiveness ratio was less than three times GDP per capita in Hong Kong. Therefore, the new intervention was cost-effective. However, like any similar study, the results rely substantially on extrapolation of trial outcomes and thus sensitivity analysis has also been performed to handle the uncertainty associated with results. The one-way sensitivity analyses and probabilistic sensitivity analysis showed the results were robust. The manuscript provides economic evidence of the new intervention and provides new clinical insights.

Multiparametric MRI Radiomics for the Prediction of Histologic Grade in Bladder Cancer

Histological grade holds great clinical significance in the management and prognosis of bladder cancer; therefore, timely and accurate prediction through non-invasive techniques such as MRI may improve health outcomes. Our objective was to create an MRI-based radiomics model that can predict the histological grade of cancer pre-operatively. In a prospective study, we gathered data from 45 bladder cancer patients who had an mpMRI scan from December 2018 to December 2022 prior to their operative procedure. Radiomics features were extracted from T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic DCE-MRI-enhanced (DCE) MR images obtained from a 1.5 T MRI scanner. A standard 5-point VI-RADS scoring system was also assessed for each scan. The variable clustering algorithm was applied to these features, and all cluster features were univariably assessed using receiver operating characteristic (ROC) curves. Multiple predictive models were created and cross-validated based on multivariable analysis to minimize overfitting and predict the grade of the tumor. Among 45 eligible patients, 28 (62.2%) patients had high-grade tumors and the rest 17 (37.8%) were low grade. In the adjusted analysis, only DCE-MRI based (Gray Level Co-occurrence Matrix (Gray Level Co-occurrence Matrix (GLCM))-inverse variance (OR = 1.42, p  = 0.028), Major Axis Length (OR = 1.04, p  = 0.03)), and age (OR = 1.08, p  = 0.039) were associated with the high-grade bladder cancer. Our radiomics models comprising DCE-MRI-based parameters, a T2W parameter, and age yielded the highest performance for predicting grades of bladder cancer (AUC = 0.91; 95% CI 0.82–1.00). These models demonstrated reasonably high predictive performance in bootstrap validation analysis as well. An mpMRI radiomics approach based on MRI has the potential to serve as a non-invasive imaging tool for preoperative grading of bladder cancer.

Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Prediction

Clinical risk prediction models often underperform in real-world settings due to poor calibration, limited transportability, and subgroup disparities. These challenges are amplified in high-dimensional multimodal cancer datasets characterized by complex feature interactions and a p >> n structure. We present a fully reproducible multimodal machine learning framework for 5-year overall survival prediction in breast cancer, integrating clinical variables with high-dimensional transcriptomic and copy-number alteration (CNA) features from the METABRIC cohort. After variance- and sparsity-based filtering and dimensionality reduction, models were trained using stratified train/validation/test splits with validation-based hyperparameter tuning. Two survival approaches were compared: an elastic-net regularized Cox model (CoxNet) and a gradient-boosted survival tree model implemented using XGBoost. CoxNet provides embedded feature selection and stable estimation, whereas XGBoost captures nonlinear effects and higher-order interactions. Performance was assessed using time-dependent area under the ROC curve (AUC), average precision (AP), calibration curves, Brier score, and bootstrapped 95 percent confidence intervals. CoxNet achieved validation and test AUCs of 98.3 and 96.6, with AP values of 90.1 and 80.4. XGBoost achieved validation and test AUCs of 98.6 and 92.5, with AP values of 92.5 and 79.9. Fairness diagnostics showed stable discrimination across age groups, estrogen receptor status, molecular subtypes, and menopausal state. This work introduces a governance-oriented multimodal survival framework emphasizing calibration, fairness auditing, robustness, and reproducibility for high-dimensional clinical machine learning.

Nutritional status and barriers to optimal nutrition among pediatric patients with cancer in tanzania: a quantitative analysis

Background Childhood cancer is a growing public health concern in low- and middle-income countries (LMICs), where over two-thirds of global pediatric cancer cases occur. Despite advances in diagnosis and treatment, malnutrition remains a major barrier to achieving better outcomes. This study aimed to assess the magnitude of undernutrition and identify barriers to optimal nutrition among pediatric patients with cancer receiving treatment at a major referral hospital in Tanzania. Methods Two study populations were recruited: pediatric patients with cancer and their primary caregivers. Children were assessed for undernutrition using anthropometric indicators, including mid-upper arm circumference, weight-for-age, height-for-age, body mass index-for-age Z-score, and triceps skinfold thickness. Nutritional biomarkers, including serum albumin, total protein, hemoglobin, and mean corpuscular volume, were also evaluated. Caregivers completed structured questionnaires adapted from the Hunger Vital Sign™ and the United Nations Food and Agriculture Organization Nutrition-related Knowledge, Attitude, and Practices Questionnaire to assess the risk of food insecurity and caregiver nutrition knowledge and perceptions. Binary logistic regression was used to explore predictors of undernutrition. Results A total of 65 pediatric patients were enrolled, of whom 41.54% were classified as undernourished based on at least one anthropometric indicator below age-specific thresholds. Although 44.62% had low serum albumin and 30.77% had low total protein, these biomarkers may reflect overall illness rather than nutritional deficiency alone. Anemia was observed in 53.85% of children under 5 and 38.46% of those aged 5 and older. Logistic regression showed that undernutrition was more likely among children aged 5–10 and > 10 years compared to those < 5 years (OR: 1.80; 95% CI: 0.88–3.68; p  = 0.109), while anemia showed an inverse and unexpected association (OR: 0.15; 95% CI: 0.02–1.44; p  = 0.100); however, neither was statistically significant. The risk of food insecurity was identified in 78.4% of households. Caregivers not at risk demonstrated significantly better nutrition knowledge and more positive perceptions than those at risk. Conclusion Undernutrition is common among pediatric patients with cancer and may be influenced by clinical and socioeconomic factors, including anemia, caregiver knowledge, and household food insecurity. Trial Registration This study is not a clinical trial and does not require registration. Clinical trial number: not applicable.

Integrated Approach to Precision Oncology

Oncology is shifting from the traditional one-size-fits-all cancer treatment model to a multifaceted model that takes into account the complexity and heterogeneity of the disease. This shift, termed precision oncology, is enabled by advances in genetics, genomics, proteomics, advanced data analytics, and modern molecular biology techniques such as the CRISPR/Cas9 technology. This chapter provides an overview of how these different areas have contributed to the advancement of precision oncology. Besides, it is also important to acknowledge that in isolation, the contribution of these different areas to precision oncology is limited. There is therefore a need for an integrated approach to be truly game-changing. Hence, this chapter also emphasizes the necessity for an integrated approach and interdisciplinary collaboration to advance the field of precision oncology. We also propose a few future directions, such as a stronger focus on single-cell analysis and AI-assisted research, which collectively have the potential to personalize cancer treatment to an unprecedented level. It is hoped that these integrated strategies will prevail and transform cancer care into a model characterized by precision, personalization, and compassion.

Statin-dye conjugates for selective targeting of KRAS mutant cancer cells

Over 90% of pancreatic ductal adenocarcinoma (PDAC) patients involve KRAS mutations ( KRAS (MUT) ), for which current treatment options are limited. Statins, commonly used to lower cholesterol, have demonstrated certain selective toxicity towards KRAS -transformed cells, prompting the question of whether statins could achieve selective uptake specifically in KRAS (MUT) cells. To investigate this, we synthesized statin-dye conjugates by attaching a fluorescent dye (Cy5.5) to two statins: simvastatin and pravastatin, aiming to assess whether selective uptake indeed occurs. Our findings revealed that these conjugates exhibited markedly enhanced uptake in KRAS (MUT) cells compared to KRAS wild-type ( KRAS (WT) ) cells. Given the magnitude of the selective uptake, we realized that the uptake of these conjugates itself is of considerable intrinsic interests. We evaluated the uptake of these conjugates in both KRAS (MUT) and KRAS (WT) cells and examined their potential to selectively target KRAS (MUT) pancreatic cancer cells (PCCs) using an engineered PDAC tumor model co-cultured with PCCs and cancer-associated fibroblasts (CAFs). Our findings indicate that KRAS (MUT) cancer cells exhibited higher uptake of statin-Cy5.5 conjugates via enhanced macropinocytosis compared to KRAS (WT) cancer cells and CAFs. We also found enhanced uptake of the statin-Cy5.5 conjugate in MCF10A cells with PTEN deficiency, a condition known to elevate macropinocytosis, compared to control MCF10A cells with wild-type PTEN . Notably, in the PCC and CAF co-culture model, the pravastatin-Cy5.5 conjugate selectively killed KRAS (MUT) PCCs without affecting the KRAS (WT) CAFs. These findings highlight the potential of stain-drug conjugates as targeted delivery vehicles for KRAS (MUT) cancer therapy.

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

BACKGROUND: Existing breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or family history. Risk is often underestimated or overestimated due to reliance on population-level data and neglect of cellular aging and mechanobiological alterations. METHODS: We profiled primary human mammary epithelial cells (HMECs) from women of varying ages and risk backgrounds using mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform that captures single-cell mechanical properties. Using machine learning, we developed a classifier, MechanoAge, to predict age-related mechanical phenotypes and introduce a novel index, mechano-RISQ, to quantify deviations linked to breast cancer risk. We further assessed the cytoskeletal protein keratin 14 (KRT14) as a molecular mediator of these mechanical states through overexpression and knockdown experiments. FINDINGS: Cells from younger women carrying BRCA1/2 mutations or with a family history of breast cancer exhibited accelerated mechanical aging compared to age-matched controls. Elevated mechano-RISQ scores reflected an increased proportion of cells with “older” mechanical profiles. KRT14 overexpression induced an aged mechanical phenotype in younger cells, while knockdown partially reversed this state in older cells. CyTOF profiling and modeling showed KRT14 modulation impacted protein expression signatures associated with aging and risk, particularly in luminal cells. INTERPRETATION: Mechanical properties of breast epithelial cells reflect biologic aging and cancer susceptibility. Mechano-RISQ offers a new approach for identifying individuals at elevated risk, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of epithelial aging.

Integrative Geriatric Oncology: A Review of Current Practices

Purpose of Review This article aims to offer a comprehensive review of optimal integrative medicine practices for geriatric oncology patients. Given the aging population and the global rise in cancer incidence, it is crucial to identify evidence-based modalities and employ an integrated approach to enhance cancer outcomes and quality of life in older adults. Recent Findings It has been predicted that 20.5% (6.9 million) of new cancer cases in 2050 will occur in adults over 80 years old.^1 The increasing focus on lifestyle factors in healthy aging has shed light on various overlooked areas of significance. Notably, anti-inflammatory diets and the promotion of a healthy gut microbiome have demonstrated significant impacts on overall health outcomes, bolstering the body's innate capacity to combat disease. This review delves into further evidence and extrapolation concerning integrative approaches and their influence on cancer outcomes and older adults quality of life. Summary The complexity and unique nature of cancer in older adults requires a wide range of support from medical providers. Incorporating various integrative techniques as part of cancer treatment and side effect support can improve health outcomes and patient’s quality of life. Familiarity with the lifestyle interventions and other topics explored in this review equips healthcare providers to offer tailored and holistic care to geriatric patients navigating cancer.

CmirC update 2024: a multi-omics database for clustered miRNAs

Clustered miRNAs consist of two or more miRNAs transcribed together and may coordinately regulate gene expression. Differential expression of clustered miRNAs is found to be controlled by crosstalk of genetic or epigenetic mechanisms. It has been demonstrated that clustered miRNA expression patterns greatly impact cancer cell progression. With the CmirC initiative, we initially developed a comprehensive database to identify copy number variation (CNV) driven clustered miRNAs in cancer. Now, we extended the analysis and identified three miRNAs, mir-96, mir-183, and mir-21, were found to be significantly upregulated in 17 cancer types. Further, CmirC is now upgraded to determine the impact of changes in the DNA methylation status at clustered miRNAs by utilizing The Cancer Genomic Atlas (TCGA) cancer datasets. We examined specific methylation datasets from 9,639 samples, pinpointing 215,435 methylation sites and 27,949 CpG islands with miRNA cluster information. The integrated analysis identified 34 clusters exhibiting differentially methylated CpG sites across 14 cancer types. Furthermore, we determined that CpG islands in the promoter region of 20 miRNA clusters could play a regulatory role. Along with ensuring a straightforward and convenient user experience, CmirC has been updated with improved data browsing and analysis functionalities, as well as enabled hyperlinks to literature and miR-cancer databases. The enhanced version of CmirC is anticipated to play an important role in providing information on the regulation of clustered miRNA expression, and their targeted oncogenes and tumor suppressors. The newly updated version of CmirC is available at https://slsdb.manipal.edu/cmirclust/ .

Cardiotoxicity Induced by Targeted Cancer Therapies: Understanding the Risks and Developing Solutions

Background Cardiotoxicity has emerged as a major clinical concern associated with targeted cancer therapies. While these agents, including tyrosine kinase inhibitors (TKIs), HER2 inhibitors, angiogenesis inhibitors, and immune checkpoint inhibitors, revolutionize cancer treatment by selectively modulating tumor-associated molecular pathways, they inadvertently disrupt cardiovascular homeostasis due to the overlap of signaling mechanisms between tumorigenesis and cardiac physiology. Objectives This review aims to elucidate the mechanisms underlying cardiotoxicity induced by targeted cancer therapies, outline diagnostic and preventive strategies, and highlight emerging approaches in cardio-oncology for optimizing patient outcomes. Methods An integrative review of preclinical and clinical studies was conducted to analyze the cardiovascular effects of major targeted therapeutic classes. Mechanistic insights, diagnostic modalities, monitoring protocols, and ongoing clinical trials were critically examined to establish a translational perspective. Results Targeted therapies induce cardiotoxicity through multiple pathways, including mitochondrial dysfunction, oxidative stress, endothelial injury, and immune-mediated inflammation. Clinically, these effects manifest as hypertension, arrhythmias, heart failure, and myocardial ischemia. Diagnostic evaluation relies on echocardiography, cardiac biomarkers (e.g., troponin, BNP), and electrocardiography. Preventive and management strategies encompass baseline cardiovascular assessment, dose adjustment, use of cardioprotective agents, and continuous cardiac monitoring. Advances in cardio-oncology have promoted multidisciplinary management and integration of cardiovascular surveillance into cancer care protocols. Conclusion The growing prevalence of cardiotoxicity among cancer survivors underscores the need for collaborative, multidisciplinary care involving oncologists, cardiologists, and researchers. Individualized therapy guided by predictive biomarkers and novel imaging tools holds promise for balancing therapeutic efficacy with cardiovascular safety, thereby improving long-term survivorship and quality of life. Graphical Abstract

Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation

Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer histopathology, for example, a convolutional neural network (CNN) may classify cancer severity accurately for cancer types represented in its training data, yet fail on related but unseen types. Although adenocarcinomas from different organs share morphological features that might support limited cross-domain generalization, addressing domain shift directly is necessary for robust performance. Domain adaptation offers a way to transfer knowledge from labeled data in one cancer type to unlabeled data in another, helping mitigate the scarcity of annotated medical images. This work evaluates cross-domain classification performance among lung, colon, breast, and kidney adenocarcinomas. A ResNet50 trained on any single adenocarcinoma achieves over 98% accuracy on its own domain but shows minimal generalization to others. Ensembling multiple supervised models does not resolve this limitation. In contrast, converting the ResNet50 into a domain adversarial neural network (DANN) substantially improves performance on unlabeled target domains. A DANN trained on labeled breast and colon data and adapted to unlabeled lung data reaches 95.56% accuracy. We also examine the impact of stain normalization on domain adaptation. Its effects vary by target domain: for lung, accuracy drops from 95.56% to 66.60%, while for breast and colon targets, stain normalization boosts accuracy from 49.22% to 81.29% and from 78.48% to 83.36%, respectively. Finally, using Integrated Gradients reveals that DANNs consistently attribute importance to biologically meaningful regions such as densely packed nuclei, indicating that the model learns clinically relevant features and can apply them to unlabeled cancer types. ;8 pages, 6 figures, 3 table

Cervical Cancer Detection Using Multi-Branch Deep Learning Model

Cervical cancer is a crucial global health concern for women, and the persistent infection of High-risk HPV mainly triggers this remains a global health challenge, with young women diagnosis rates soaring from 10\% to 40\% over three decades. While Pap smear screening is a prevalent diagnostic method, visual image analysis can be lengthy and often leads to mistakes. Early detection of the disease can contribute significantly to improving patient outcomes. In recent decades, many researchers have employed machine learning techniques that achieved promise in cervical cancer detection processes based on medical images. In recent years, many researchers have employed various deep-learning techniques to achieve high-performance accuracy in detecting cervical cancer but are still facing various challenges. This research proposes an innovative and novel approach to automate cervical cancer image classification using Multi-Head Self-Attention (MHSA) and convolutional neural networks (CNNs). The proposed method leverages the strengths of both MHSA mechanisms and CNN to effectively capture both local and global features within cervical images in two streams. MHSA facilitates the model's ability to focus on relevant regions of interest, while CNN extracts hierarchical features that contribute to accurate classification. Finally, we combined the two stream features and fed them into the classification module to refine the feature and the classification. To evaluate the performance of the proposed approach, we used the SIPaKMeD dataset, which classifies cervical cells into five categories. Our model achieved a remarkable accuracy of 98.522\%. This performance has high recognition accuracy of medical image classification and holds promise for its applicability in other medical image recognition tasks.

Hierarchical Lineage Tracing Reveals Diverse Pathways of AML Treatment Resistance

Cancer cells adapt to treatment, leading to the emergence of clones that are more aggressive and resistant to anti-cancer therapies. We have a limited understanding of the development of treatment resistance as we lack technologies to map the evolution of cancer under the selective pressure of treatment. To address this, we developed a hierarchical, dynamic lineage tracing method called FLARE (Following Lineage Adaptation and Resistance Evolution). We use this technique to track the progression of acute myeloid leukemia (AML) cell lines through exposure to Cytarabine (AraC), a front-line treatment in AML, in vitro and in vivo. We map distinct cellular lineages in murine and human AML cell lines predisposed to AraC persistence and/or resistance via the upregulation of cell adhesion and motility pathways. Additionally, we highlight the heritable expression of immunoproteasome 11S regulatory cap subunits as a potential mechanism aiding AML cell survival, proliferation, and immune escape in vivo. Finally, we validate the clinical relevance of these signatures in the TARGET-AML cohort, with a bisected response in blood and bone marrow. Our findings reveal a broad spectrum of resistance signatures attributed to significant cell transcriptional changes. To our knowledge, this is the first application of dynamic lineage tracing to unravel treatment response and resistance in cancer, and we expect FLARE to be a valuable tool in dissecting the evolution of resistance in a wide range of tumor types.

In Silico Pharmacokinetic and Molecular Docking Studies of Natural Plants against Essential Protein KRAS for Treatment of Pancreatic Cancer

A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic cancer. KRAS acts as an on-off switch that promotes cell growth. But when the KRAS gene is mutated, it will be in one position, allowing the cell growth uncontrollably. This uncontrollable multiplication of cells causes cancer growth. Therefore, KRAS was selected as the target protein in the study. Fifty plant-derived compounds are selected for the study. To determine whether the examined drugs could bind to the KRAS complex's binding pocket, molecular docking was performed. Computational analyses were used to assess the possible ability of tested substances to pass the Blood Brain Barrier (BBB). To predict the bioactivity of ligands a machine learning model was created. Five machine learning models were created and have chosen the best one among them for analyzing the bioactivity of each ligand. From the fifty plant-derived compounds the compounds with the least binding energies are selected. Then bioactivity of these six compounds is analyzed using Random Forest Regression model. Adsorption, Distribution, Metabolism, Excretion (ADME) properties of compounds are analyzed. The results showed that borneol has powerful effects and acts as a promising agent for the treatment of pancreatic cancer. This suggests that borneol found in plants like mint, ginger, rosemary, etc., is a successful compound for the treatment of pancreatic cancer.

Folate receptor alpha in platinum-resistant ovarian cancer: prevalence in a multicenter Polish cohort and review of the evidence

Background Folate receptor alpha (FRα, FOLR1) is the required biomarker for mirvetuximab soravtansine, a treatment option for patients with platinum-resistant ovarian cancer. Since its approval, the drug has been introduced into national drug programs, but prevalence data from the Polish population are lacking. This study provides the first multicenter assessment of FOLR1 expression in Polish ovarian cancer in the context of treatment eligibility. Methods We retrospectively analyzed two institutional cohorts comprising 229 epithelial ovarian cancer cases from the Oncology Center in Bydgoszcz and the National Institute of Oncology in Warsaw. Immunohistochemistry was performed on formalin-fixed, paraffin-embedded tissue using the VENTANA FOLR1 (FOLR1-2.1) RxDx Assay on the BenchMark ULTRA platform. Membranous staining of moderate or strong intensity in ≥ 75% of viable tumor cells was considered positive, in accordance with criteria for FOLR1-targeted therapy. Results Positive FOLR1 expression was observed in 116 of 229 (50.7%, 95% CI 44.2–57.2) analyzed tumors, with 40% showing high expression. In most of these cases, staining intensity was moderate to strong, meeting the established threshold for therapeutic eligibility. In ~ 18% of tumors, FOLR1 expression fell into the borderline range (65–85%). Conclusion Approximately half of the ovarian cancer patients in this multicenter Polish cohort demonstrated FOLR1 positivity. Although this prevalence is lower than that reported in high-grade serous carcinoma cohorts, it likely reflects histological heterogeneity and methodological differences between studies. These findings provide the first population-based evidence from Poland and support the implementation of FOLR1 testing to guide access to mirvetuximab soravtansine. Further research integrating clinical and molecular data is warranted to validate these results and assess implications for cost-effectiveness and patient outcomes.

Time series forecasting with Hahn Kolmogorov-Arnold networks

Recent Transformer- and MLP-based models have demonstrated strong performance in long-term time series forecasting, yet Transformers remain limited by their quadratic complexity and permutation-equivariant attention, while MLPs exhibit spectral bias. We propose HaKAN, a versatile model based on Kolmogorov-Arnold Networks (KANs), leveraging Hahn polynomial-based learnable activation functions and providing a lightweight and interpretable alternative for multivariate time series forecasting. Our model integrates channel independence, patching, a stack of Hahn-KAN blocks with residual connections, and a bottleneck structure comprised of two fully connected layers. The Hahn-KAN block consists of inter- and intra-patch KAN layers to effectively capture both global and local temporal patterns. Extensive experiments on various forecasting benchmarks demonstrate that our model consistently outperforms recent state-of-the-art methods, with ablation studies validating the effectiveness of its core components.

Federated Proximal Optimization for Privacy-Preserving Heart Disease Prediction: A Controlled Simulation Study on Non-IID Clinical Data

Healthcare institutions have access to valuable patient data that could be of great help in the development of improved diagnostic models, but privacy regulations like HIPAA and GDPR prevent hospitals from directly sharing data with one another. Federated Learning offers a way out to this problem by facilitating collaborative model training without having the raw patient data centralized. However, clinical datasets intrinsically have non-IID (non-independent and identically distributed) features brought about by demographic disparity and diversity in disease prevalence and institutional practices. This paper presents a comprehensive simulation research of Federated Proximal Optimization (FedProx) for Heart Disease prediction based on UCI Heart Disease dataset. We generate realistic non-IID data partitions by simulating four heterogeneous hospital clients from the Cleveland Clinic dataset (303 patients), by inducing statistical heterogeneity by demographic-based stratification. Our experimental results show that FedProx with proximal parameter mu=0.05 achieves 85.00% accuracy, which is better than both centralized learning (83.33%) and isolated local models (78.45% average) without revealing patient privacy. Through generous sheer ablation studies with statistical validation on 50 independent runs we demonstrate that proximal regularization is effective in curbing client drift in heterogeneous environments. This proof-of-concept research offers algorithmic insights and practical deployment guidelines for real-world federated healthcare systems, and thus, our results are directly transferable to hospital IT-administrators, implementing privacy-preserving collaborative learning. ;27 pages, 7 figures, 4 tables

Trajectory-Driven Multi-Product Influence Maximization in Billboard Advertising

Billboard Advertising has emerged as an effective out-of-home advertising technique, where the goal is to select a limited number of slots and play advertisement content there, with the hope that it will be observed by many people and, effectively, a significant number of them will be influenced towards the brand. Given a trajectory and a billboard database and a positive integer $k$, how can we select $k$ highly influential slots to maximize influence? In this paper, we study a variant of this problem where a commercial house wants to make a promotion of multiple products, and there is an influence demand for each product. We have studied two variants of the problem. In the first variant, our goal is to select $k$ slots such that the respective influence demand of each product is satisfied. In the other variant of the problem, we are given with $\ell$ integers $k_1,k_2, \ldots, k_{\ell}$, the goal here is to search for $\ell$ many set of slots $S_1, S_2, \ldots, S_{\ell}$ such that for all $i \in [\ell]$, $|S_{i}| \leq k_i$ and for all $i \neq j$, $S_i \cap S_j=\emptyset$ and the influence demand of each of the products gets satisfied. We model the first variant of the problem as a multi-submodular cover problem and the second variant as its generalization. To solve the common-slot variant, we formulate the problem as a multi-submodular cover problem and design a bi-criteria approximation algorithm based on the continuous greedy framework and randomized rounding. For the disjoint-slot variant, we proposed a sampling-based approximation approach along with an efficient primal-dual greedy algorithm that enforces disjointness naturally. Extensive experiments with real-world trajectory and billboard datasets highlight the effectiveness and efficiency of the proposed solution approaches. ;31 Pages. arXiv admin note: text overlap with arXiv:2510.09050

Attention-Based Neural-Augmented Kalman Filter for Legged Robot State Estimation

In this letter, we propose an Attention-Based Neural-Augmented Kalman Filter (AttenNKF) for state estimation in legged robots. Foot slip is a major source of estimation error: when slip occurs, kinematic measurements violate the no-slip assumption and inject bias during the update step. Our objective is to estimate this slip-induced error and compensate for it. To this end, we augment an Invariant Extended Kalman Filter (InEKF) with a neural compensator that uses an attention mechanism to infer error conditioned on foot-slip severity and then applies this estimate as a post-update compensation to the InEKF state (i.e., after the filter update). The compensator is trained in a latent space, which aims to reduce sensitivity to raw input scales and encourages structured slip-conditioned compensations, while preserving the InEKF recursion. Experiments demonstrate improved performance compared to existing legged-robot state estimators, particularly under slip-prone conditions. ;8 pages, 6 figures, Published in IEEE Robotics and Automation Letters (RA-L)

M-SGWR: Multiscale Similarity and Geographically Weighted Regression

The first law of geography is a cornerstone of spatial analysis, emphasizing that nearby and related locations tend to be more similar, however, defining what constitutes "near" and "related" remains challenging, as different phenomena exhibit distinct spatial patterns. Traditional local regression models, such as Geographically Weighted Regression (GWR) and Multiscale GWR (MGWR), quantify spatial relationships solely through geographic proximity. In an era of globalization and digital connectivity, however, geographic proximity alone may be insufficient to capture how locations are interconnected. To address this limitation, we propose a new multiscale local regression framework, termed M-SGWR, which characterizes spatial interaction across two dimensions: geographic proximity and attribute (variable) similarity. For each predictor, geographic and attribute-based weight matrices are constructed separately and then combined using an optimized parameter, alpha, which governs their relative contribution to local model fitting. Analogous to variable-specific bandwidths in MGWR, the optimal alpha varies by predictor, allowing the model to flexibly account for geographic, mixed, or non-spatial (remote similarity) effects. Results from two simulation experiments and one empirical application demonstrate that M-SGWR consistently outperforms GWR, SGWR, and MGWR across all goodness-of-fit metrics.

Large Language Models as Automatic Annotators and Annotation Adjudicators for Fine-Grained Opinion Analysis

Fine-grained opinion analysis of text provides a detailed understanding of expressed sentiments, including the addressed entity. Although this level of detail is valuable, annotating opinions in datasets for model training requires considerable human effort and substantial cost, especially across diverse domains and real-world applications. To address this shortage of domain-specific labelled datasets, we explore the feasibility of LLMs as automatic annotators for fine-grained opinion analysis. We use a declarative annotation pipeline, an approach that reduces the variability of manual prompt engineering when using LLMs to identify fine-grained opinion spans in text. We also present a dedicated methodology for an LLM to adjudicate multiple labels and produce final annotations. We trial the pipeline with models of different sizes for the Aspect Sentiment Triplet Extraction (ASTE) and Aspect-Category-Opinion-Sentiment (ACOS) analysis tasks. In this work, we attempt to develop fully autonomous LLM-based annotators, but our results reveal an uneven picture characterised by a critical performance bifurcation: LLMs are reliable at the span level yet struggle to faithfully reproduce the relational structures that connect those spans. This suggests that LLMs are better positioned as high-fidelity annotation assistants and data augmentation tools to expand fine-grained opinion-annotated datasets, rather than replacing human annotators entirely.

Demystifying Prediction Powered Inference

Machine learning predictions are increasingly used to supplement incomplete or costly-to-measure outcomes in fields such as biomedical research, environmental science, and social science. However, treating predictions as ground truth introduces bias while ignoring them wastes valuable information. Prediction-Powered Inference (PPI) offers a principled framework that leverages predictions from large unlabeled datasets to improve statistical efficiency while maintaining valid inference through explicit bias correction using a smaller labeled subset. Despite its potential, the growing PPI variants and the subtle distinctions between them have made it challenging for practitioners to determine when and how to apply these methods responsibly. This paper demystifies PPI by synthesizing its theoretical foundations, methodological extensions, connections to existing statistics literature, and diagnostic tools into a unified practical workflow. Using the Mosaiks housing price data, we show that PPI variants produce tighter confidence intervals than complete-case analysis, but that double-dipping, i.e. reusing training data for inference, leads to anti-conservative confidence intervals and coverages. Under missing-not-at-random mechanisms, all methods, including classical inference using only labeled data, yield biased estimates. We provide a decision flowchart linking assumption violations to appropriate PPI variants, a summary table of selective methods, and practical diagnostic strategies for evaluating core assumptions. By framing PPI as a general recipe rather than a single estimator, this work bridges methodological innovation and applied practice, helping researchers responsibly integrate predictions into valid inference.

Teaching Machine Learning Fundamentals with LEGO Robotics

This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day course designed to teach machine learning concepts to students aged 12 to 17 through programming-free robotics activities. Machine Learning with Bricks is an open source platform and combines interactive visualizations with LEGO robotics to teach three core algorithms: KNN, linear regression, and Q-learning. Students learn by collecting data, training models, and interacting with robots via a web-based interface. Pre- and post-surveys with 14 students indicate statistically significant improvements in self-reported understanding of machine learning algorithms, changes in AI-related terminology toward more technical language, high platform usability, and increased motivation for continued learning. This work suggests that tangible, visualization-based approaches can make machine learning concepts accessible and engaging for young learners while maintaining technical depth. The platform is freely available at https://learning-and-dynamics.github.io/ml-with-bricks/, with video tutorials guiding students through the experiments at https://youtube.com/playlist?list=PLx1grFu4zAcwfKKJZ1Ux4LwRqaePCOA2J. ;10 pages, 8 figures

Autonomous Business System via Neuro-symbolic AI

Modern business environments demand continuous reconfiguration of cross-functional processes, yet most enterprise systems remain organized around siloed departments, rigid workflows, and hard-coded automation. Meanwhile, large language models (LLMs) demonstrate strong capabilities in interpreting natural language and synthesizing unstructured information, but they lack deterministic, auditable execution of complex business logic. We introduce Autonomous Business System (AUTOBUS), a system that integrates LLM-based AI agents, predicate-logic programming, and business-semantics-centric enterprise data into a unified neuro-symbolic architecture for executing end-to-end business initiatives. AUTOBUS models a business initiative as a network of interrelated tasks with explicit pre- and post-conditions, required data, evaluation rules, and API-level actions. Enterprise data is organized as a knowledge graph, whose entities, relationships, and constraints are translated into logic facts and foundational rules that ground reasoning and ensure semantic consistency. Core AI agents synthesize task instructions, enterprise semantics, and available tools into task-specific logic programs, which are executed by a logic engine that enforces constraints, coordinates auxiliary tools, and produces deterministic outcomes. Humans specify task instructions, define and maintain business semantics and policies, curate tools, and supervise high-impact or ambiguous decisions, ensuring accountability and adaptability. We detail the AUTOBUS architecture, the structure of AI-generated logic programs, and the human-AI collaboration model and present a case study that demonstrates accelerated time to market in a data-rich organization. A reference implementation of the case study is available at https://github.com/cecilpang/autobus-paper. ;IEEE SysCon 2026

Degradation-Aware Frequency Regulation of a Heterogeneous Battery Fleet via Reinforcement Learning

Battery energy storage systems are increasingly deployed as fast-responding resources for grid balancing services such as frequency regulation and for mitigating renewable generation uncertainty. However, repeated charging and discharging induces cycling degradation and reduces battery lifetime. This paper studies the real-time scheduling of a heterogeneous battery fleet that collectively tracks a stochastic balancing signal subject to per-battery ramp-rate and capacity constraints, while minimizing long-term cycling degradation. Cycling degradation is fundamentally path-dependent: it is determined by charge-discharge cycles formed by the state-of-charge (SoC) trajectory and is commonly quantified via rainflow cycle counting. This non-Markovian structure makes it difficult to express degradation as an additive per-time-step cost, complicating classical dynamic programming approaches. We address this challenge by formulating the fleet scheduling problem as a Markov decision process (MDP) with constrained action space and designing a dense proxy reward that provides informative feedback at each time step while remaining aligned with long-term cycle-depth reduction. To scale learning to large state-action spaces induced by fine-grained SoC discretization and asymmetric per-battery constraints, we develop a function-approximation reinforcement learning method using an Extreme Learning Machine (ELM) as a random nonlinear feature map combined with linear temporal-difference learning. We evaluate the proposed approach on a toy Markovian signal model and on a Markovian model trained from real-world regulation signal traces obtained from the University of Delaware, and demonstrate consistent reductions in cycle-depth occurrence and degradation metrics compared to baseline scheduling policies. ;11 pages, 2 figures

ROIDS: Robust Outlier-Aware Informed Down-Sampling

Informed down-sampling (IDS) is known to improve performance in symbolic regression when combined with various selection strategies, especially tournament selection. However, recent work found that IDS's gains are not consistent across all problems. Our analysis reveals that IDS performance is worse for problems containing outliers. IDS systematically favors including outliers in subsets which pushes GP towards finding solutions that overfit to outliers. To address this, we introduce ROIDS (Robust Outlier-Aware Informed Down-Sampling), which excludes potential outliers from the sampling process of IDS. With ROIDS it is possible to keep the advantages of IDS without overfitting to outliers and to compete on a wide range of benchmark problems. This is also reflected in our experiments in which ROIDS shows the desired behavior on all studied benchmark problems. ROIDS consistently outperforms IDS on synthetic problems with added outliers as well as on a wide range of complex real-world problems, surpassing IDS on over 80% of the real-world benchmark problems. Moreover, compared to all studied baseline approaches, ROIDS achieves the best average rank across all tested benchmark problems. This robust behavior makes ROIDS a reliable down-sampling method for selection in symbolic regression, especially when outliers may be included in the data set.

Prior-Informed Flow Matching for Graph Reconstruction

We introduce \textit{Prior-Informed Flow Matching (PIFM)}, a conditional flow model for graph reconstruction. Reconstructing graphs from partial observations remains a key challenge; classical embedding methods often lack global consistency, while modern generative models struggle to incorporate structural priors. PIFM bridges this gap by integrating embedding-based priors with continuous-time flow matching. Grounded in a permutation equivariant version of the distortion-perception theory, our method first uses a prior, such as GraphSAGE or node2vec, to form an informed initial estimate of the adjacency matrix based on local information. It then applies rectified flow matching to refine this estimate, transporting it toward the true distribution of clean graphs and learning a global coupling. Experiments on different datasets demonstrate that PIFM consistently enhances classical embeddings, outperforming them and state-of-the-art generative baselines in reconstruction accuracy.

From Observations to States: Latent Time Series Forecasting

Deep learning has achieved strong performance in Time Series Forecasting (TSF). However, we identify a critical representation paradox, termed Latent Chaos: models with accurate predictions often learn latent representations that are temporally disordered and lack continuity. We attribute this to the dominant observation-space forecasting paradigm, where minimizing point-wise errors on noisy and partially observed data encourages shortcut solutions instead of the recovery of underlying system dynamics. To address this, we propose Latent Time Series Forecasting (LatentTSF), a paradigm that shifts TSF from observation regression to latent state prediction. LatentTSF employs an AutoEncoder to project each observation into a learned latent state space and performs forecasting entirely in this space, allowing the model to focus on learning structured temporal dynamics. We provide an information-theoretic analysis showing that the latent objectives can be motivated as surrogates for maximizing mutual information between predicted and ground-truth latent states and future observations. Extensive experiments on widely-used benchmarks confirm that LatentTSF effectively mitigates latent chaos, yielding consistent improvements in both forecasting accuracy and representation quality. Our code is available at https://github.com/Muyiiiii/LatentTSF. ;Accepted at ICML 2026

Local Language Models for Context-Aware Adaptive Anonymization of Sensitive Text

Qualitative research often contains personal, contextual, and organizational details that pose privacy risks if not handled appropriately. Manual anonymization is time-consuming, inconsistent, and frequently omits critical identifiers. Existing automated tools tend to rely on pattern matching or fixed rules, which fail to capture context and may alter the meaning of the data. This study uses local LLMs to build a reliable, repeatable, and context-aware anonymization process for detecting and anonymizing sensitive data in qualitative transcripts. We introduce a Structured Framework for Adaptive Anonymizer (SFAA) that includes three steps: detection, classification, and adaptive anonymization. The SFAA incorporates four anonymization strategies: rule-based substitution, context-aware rewriting, generalization, and suppression. These strategies are applied based on the identifier type and the risk level. The identifiers handled by the SFAA are guided by major international privacy and research ethics standards, including the GDPR, HIPAA, and OECD guidelines. This study followed a dual-method evaluation that combined manual and LLM-assisted processing. Two case studies were used to support the evaluation. The first includes 82 face-to-face interviews on gamification in organizations. The second involves 93 machine-led interviews using an AI-powered interviewer to test LLM awareness and workplace privacy. Two local models, LLaMA and Phi were used to evaluate the performance of the proposed framework. The results indicate that the LLMs found more sensitive data than a human reviewer. Phi outperformed LLaMA in finding sensitive data, but made slightly more errors. Phi was able to find over 91% of the sensitive data and 94.8% kept the same sentiment as the original text, which means it was very accurate, hence, it does not affect the analysis of the qualitative data. ;Accepted and Waiting to be Published. ICAI'25: 27th International Conference on Artificial Intelligence https://american-cse.org/csce2025/conferences-ICAI

PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions

Prompting is central to interaction with AI systems, yet many users struggle to explore alternative directions, articulate creative intent, or understand how variations in prompts shape model outputs. We introduce prompt recommender systems (PRS) as an interaction approach that supports exploration, suggesting contextually relevant follow-up prompts. We present PromptHelper, a PRS prototype integrated into an AI chatbot that surfaces semantically diverse prompt suggestions while users work on real writing tasks. We evaluate PromptHelper in a 2x2 fully within-subjects study (N=32) across creative and academic writing tasks. Results show that PromptHelper significantly increases users' perceived exploration and expressiveness without increasing cognitive workload. Qualitative findings illustrate how prompt recommendations help users branch into new directions, overcome uncertainty about what to ask next, and better articulate their intent. We discuss implications for designing AI interfaces that scaffold exploratory interaction while preserving user agency, and release open-source resources to support research on prompt recommendation.

Text-Pass Filter: An Efficient Scene Text Detector

To pursue an efficient text assembling process, existing methods detect texts via the shrink-mask expansion strategy. However, the shrinking operation loses the visual features of text margins and confuses the foreground and background difference, which brings intrinsic limitations to recognize text features. We follow this issue and design Text-Pass Filter (TPF) for arbitrary-shaped text detection. It segments the whole text directly, which avoids the intrinsic limitations. It is noteworthy that different from previous whole text region-based methods, TPF can separate adhesive texts naturally without complex decoding or post-processing processes, which makes it possible for real-time text detection. Concretely, we find that the band-pass filter allows through components in a specified band of frequencies, called its passband but blocks components with frequencies above or below this band. It provides a natural idea for extracting whole texts separately. By simulating the band-pass filter, TPF constructs a unique feature-filter pair for each text. In the inference stage, every filter extracts the corresponding matched text by passing its pass-feature and blocking other features. Meanwhile, considering the large aspect ratio problem of ribbon-like texts makes it hard to recognize texts wholly, a Reinforcement Ensemble Unit (REU) is designed to enhance the feature consistency of the same text and to enlarge the filter's recognition field to help recognize whole texts. Furthermore, a Foreground Prior Unit (FPU) is introduced to encourage TPF to discriminate the difference between the foreground and background, which improves the feature-filter pair quality. Experiments demonstrate the effectiveness of REU and FPU while showing the TPF's superiority.

Provable Learning of Random Hierarchy Models and Hierarchical Shallow-to-Deep Chaining

The empirical success of deep learning is often attributed to deep networks' ability to exploit hierarchical structure in data, constructing increasingly complex features across layers. Yet despite substantial progress in deep learning theory, most optimization results sill focus on networks with only two or three layers, leaving the theoretical understanding of hierarchical learning in genuinely deep models limited. This leads to a natural question: can we prove that deep networks, trained with gradient-based methods and standard input-label pairs, can efficiently exploit hierarchical structure? In this work, we consider Random Hierarchy Models -- a hierarchical context-free grammar introduced by arXiv:2307.02129 and conjectured to separate deep and shallow networks. We prove that, under mild conditions, a deep convolutional network can be efficiently trained to learn this function class. Our proof builds on a general observation: if intermediate layers can receive clean signal from the labels and the relevant features are weakly identifiable, then layerwise training each individual layer suffices to hierarchically learn the target function. ;COLT 2026

Reversible Efficient Diffusion for Image Fusion

Multi-modal image fusion aims to consolidate complementary information from diverse source images into a unified representation. The fused image is expected to preserve fine details and maintain high visual fidelity. While diffusion models have demonstrated impressive generative capabilities in image generation, they often suffer from detail loss when applied to image fusion tasks. This issue arises from the accumulation of noise errors inherent in the Markov process, leading to inconsistency and degradation in the fused results. However, incorporating explicit supervision into end-to-end training of diffusion-based image fusion introduces challenges related to computational efficiency. To address these limitations, we propose the Reversible Efficient Diffusion (RED) model - an explicitly supervised training framework that inherits the powerful generative capability of diffusion models while avoiding the distribution estimation.

TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks

Task decomposition is critical for understanding and learning complex long-horizon manipulation tasks. Especially for tasks involving rich physical interactions, relying solely on visual observations and robot proprioceptive information often fails to reveal the underlying event transitions. This raises the requirement for efficient collection of high-quality multi-modal data as well as robust segmentation method to decompose demonstrations into meaningful modules. Building on the idea of the handheld demonstration device Universal Manipulation Interface (UMI), we introduce TacUMI, a multi-modal data collection system that integrates additionally ViTac sensors, force-torque sensor, and pose tracker into a compact, robot-compatible gripper design, which enables synchronized acquisition of all these modalities during human demonstrations. We then propose a multi-modal segmentation framework that leverages temporal models to detect semantically meaningful event boundaries in sequential manipulations. Evaluation on a challenging cable mounting task shows more than 90 percent segmentation accuracy and highlights a remarkable improvement with more modalities, which validates that TacUMI establishes a practical foundation for both scalable collection and segmentation of multi-modal demonstrations in contact-rich tasks.

Principled Fine-tuning of LLMs from User-Edits: A Medley of Preference, Supervision, and Reward

We study how to fine-tune LLMs using user-edit deployment data consisting of a set of context, an agent's response, and user edits. This deployment data is naturally generated by users in applications such as LLMs-based writing assistants and coding agents. The _natural_ origin of user edits makes it a desired source for adapting and personalizing LLMs. In this setup, there emerges a unification of various feedback types namely preferences, supervised labels, and cost that are typically studied separately in the literature. In this paper, we initiate the theoretical investigation of learning from user edits. We first derive bounds for learning algorithms that learn from each of these feedback types. We prove that these algorithms have different trade-offs depending upon the user, data distribution, and model class. We then propose a simple ensembling procedure to jointly learn from these feedback types. On two domains adapted from Gao et al. 2024, we show our ensembling procedure outperforms these methods that learn from individual feedback. Further, we show that our proposed procedure can robustly adapt to different user-edit distributions at test time. ;Accepted at NeurIPS 2025

Larger than memory image processing

This report addresses larger-than-memory image analysis for petascale datasets such as 1.4 PB electron-microscopy volumes and 150 TB human-organ atlases. We argue that performance is fundamentally I/O-bound. We show that structuring analysis as streaming passes over data is crucial. For 3D volumes, two representations are popular: stacks of 2D slices (e.g., directories or multi-page TIFF) and 3D chunked layouts (e.g., Zarr/HDF5). While for a few algorithms, chunked layout on disk is crucial to keep disk I/O at a minimum, we show how the slice-based streaming architecture can be built on top of either image representation in a manner that minimizes disk I/O. This is in particular advantageous for algorithms relying on neighbouring values, since the slicing streaming architecture is 1D, which implies that there are only 2 possible sweeping orders, both of which are aligned with the order in which images are read from the disk. This is in contrast to 3D chunks, in which any sweep cannot be done without accessing each chunk at least 9 times. We formalize this with sweep-based execution (natural 2D/3D orders), windowed operations, and overlap-aware tiling to minimize redundant access. Building on these principles, we introduce a domain-specific language (DSL) that encodes algorithms with intrinsic knowledge of their optimal streaming and memory use; the DSL performs compile-time and run-time pipeline analyses to automatically select window sizes, fuse stages, tee and zip streams, and schedule passes for limited-RAM machines, yielding near-linear I/O scans and predictable memory footprints. The approach integrates with existing tooling for segmentation and morphology but reframes pre/post-processing as pipelines that privilege sequential read/write patterns, delivering substantial throughput gains for extremely large images without requiring full-volume residency in memory. ;10 pages

Diffusion Model-based Reinforcement Learning for Version Age of Information Scheduling: Average and Tail-Risk-Sensitive Control

Ensuring timely and semantically accurate information delivery is critical in real-time wireless systems. While Age of Information (AoI) quantifies temporal freshness, Version Age of Information (VAoI) captures semantic staleness by accounting for version evolution between transmitters and receivers. Existing VAoI scheduling approaches primarily focus on minimizing average VAoI, overlooking rare but severe staleness events that can compromise reliability under stochastic packet arrivals and unreliable channels. This paper investigates both average-oriented and tail-risk-sensitive VAoI scheduling in a multi-user status update system with long-term transmission cost constraints. We first formulate the average VAoI minimization problem as a constrained Markov decision process and introduce a deep diffusion-based Soft Actor-Critic (D2SAC) algorithm. By generating actions through a diffusion-based denoising process, D2SAC enhances policy expressiveness and establishes a strong baseline for mean performance. Building on this foundation, we put forth RS-D3SAC, a risk-sensitive deep distributional diffusion-based Soft Actor-Critic algorithm. RS-D3SAC integrates a diffusion-based actor with a quantile-based distributional critic, explicitly modeling the full VAoI return distribution. This enables principled tail-risk optimization via Conditional Value-at-Risk (CVaR) while satisfying long-term transmission cost constraints. Extensive simulations show that, while D2SAC reduces average VAoI, RS-D3SAC consistently achieves substantial reductions in CVaR without sacrificing mean performance. The dominant gain in tail-risk reduction stems from the distributional critic, with the diffusion-based actor providing complementary refinement to stabilize and enrich policy decisions, highlighting their effectiveness for robust and risk-aware VAoI scheduling in multi-user wireless systems. ;16 pages, 11 figures

Nonparametric LLM Evaluation from Preference Data

Evaluating the performance of large language models (LLMs) from human preference data is crucial for obtaining LLM leaderboards. However, many existing approaches either rely on restrictive parametric assumptions or lack valid uncertainty quantification when flexible machine learning methods are used. In this paper, we propose a nonparametric statistical framework, called DMLRank, for comparing and ranking LLMs from preference data using debiased machine learning (DML). For this, we introduce generalized average ranking scores (GARS), which generalize commonly used ranking models, including the Bradley-Terry model or PageRank/ Rank centrality, with complex human responses such as ties. DMLRank comes with the following advantages: (i)~It produces statistically efficient estimates of GARS ranking scores. (ii) It naturally allows the incorporation of black-box machine learning methods for estimation. (iii) It can be combined with pre-trained LLM evaluators (e.g., using LLM-as-a-judge). (iv) It suggests optimal policies for collecting preference data under budget constraints. We demonstrate these advantages both theoretically and empirically using both synthetic and real-world preference datasets. In summary, our framework provides practitioners with powerful, state-of-the-art methods for comparing or ranking LLMs for leaderboards. ;Accepted at ICML 2026

RobustDebias: Debiasing Language Models using Distributionally Robust Optimization

Pretrained language models have been shown to exhibit biases and social stereotypes. Prior work on debiasing these models has largely focused on modifying embedding spaces during pretraining, which is not scalable for large models. Fine-tuning pretrained models on task-specific datasets can both degrade model performance and amplify biases present in the fine-tuning data. We address bias amplification during fine-tuning rather than costly pretraining, focusing on BERT models due to their widespread use in language understanding tasks. While Empirical Risk Minimization effectively optimizes downstream performance, it often amplifies social biases during fine-tuning. To counter this, we propose \textit{RobustDebias}, a novel mechanism which adapts Distributionally Robust Optimization (DRO) to debias language models during fine-tuning. Our approach debiases models across multiple demographics during MLM fine-tuning and generalizes to any dataset or task. Extensive experiments on various language models show significant bias mitigation with minimal performance impact.

Robust Learning of a Group DRO Neuron

We study the problem of learning a single neuron under standard squared loss in the presence of arbitrary label noise and group-level distributional shifts, for a broad family of covariate distributions. Our goal is to identify a ''best-fit'' neuron parameterized by $\mathbf{w}_*$ that performs well under the most challenging reweighting of the groups. Specifically, we address a Group Distributionally Robust Optimization problem: given sample access to $K$ distinct distributions $\mathcal p_{[1]},\dots,\mathcal p_{[K]}$, we seek to approximate $\mathbf{w}_*$ that minimizes the worst-case objective over convex combinations of group distributions $\boldsymbolλ \in Δ_K$, where the objective is $\sum_{i \in [K]}λ_{[i]}\,\mathbb E_{(\mathbf x,y)\sim\mathcal p_{[i]}}(σ(\mathbf w\cdot\mathbf x)-y)^2 - νd_f(\boldsymbolλ,\frac{1}{K}\mathbf1)$ and $d_f$ is an $f$-divergence that imposes (optional) penalty on deviations from uniform group weights, scaled by a parameter $ν\geq 0$. We develop a computationally efficient primal-dual algorithm that outputs a vector $\widehat{\mathbf w}$ that is constant-factor competitive with $\mathbf{w}_*$ under the worst-case group weighting. Our analytical framework directly confronts the inherent nonconvexity of the loss function, providing robust learning guarantees in the face of arbitrary label corruptions and group-specific distributional shifts. The implementation of the dual extrapolation update motivated by our algorithmic framework shows promise on LLM pre-training benchmarks.

Laryngo pharyngeal affection of COVID-19 during Delta and Omicron variant (Comparative Study)

Background Since the declaration of COVID-19 as a pandemic in 2020, its main symptoms have primarily affected the respiratory system. However, it also presents other systemic manifestations, including symptoms related to the larynx and pharynx. COVID-19 has evolved into subsequent variants, starting from the alpha variant and currently dominated by the Omicron variant, with the Delta variant being the most severe. The study aimed to elucidate the laryngo pharyngeal manifestations related to Delta and Omicron variants of COVID-19 as well as the associated risk factors. Results This study adopted a case–control design. The data were collected from patients who attended the phoniatric outpatient clinic at Menoufia University Hospital from January to December 2022. Patients were categorized into three groups (50 patients each). Group I consisted of patients who exhibited COVID-19 Laryngo pharyngeal symptoms during the Delta wave (Group 1) and the Omicron wave (Group 2). The control group included non-COVID-19 participants. The symptoms related to the larynx and pharynx were documented. In addition, laryngoscopic and stroboscopic examinations were done. The age of individuals affected by the Delta variant was higher. Delta cases exhibited a higher rate of smoking, diabetes, hypertension, and COPD compared to Omicron cases. Additionally, Delta cases displayed greater severity. Dysphagia, dysphonia, choking attacks related to swallowing, and stridor had significant higher rate in the Delta variant compared to the Omicron variant. The Delta variant primarily exhibited unilateral vocal fold paralysis in 38% of cases, as well as bilateral vocal fold paresis and paralysis in 20% and 22% of cases, respectively. In contrast, Omicron cases predominantly showed vocal fold congestion (80% of cases). Delta cases were more prone to experiencing abnormalities in amplitude, symmetry, and periodicity. Conclusions The Delta variant is more prone to neurologic affection of the vocal folds manifesting as paresis and paralysis, whereas the Omicron variant, which has maintained its dominance thus far, experiences milder affection, primarily manifesting as congestion. Consequently, laryngeal affection with various degrees of severity is still suspected.

Patient-Made Knowledge Networks: Long COVID Discourse, Epistemic Injustice, and Online Community Formation

Long COVID represents an unprecedented case of patient-led illness definition, emerging through Twitter in May 2020 when patients began collectively naming, documenting, and legitimizing their condition before medical institutions recognized it. This study examines 2.8 million tweets containing #LongCOVID to understand how contested illness communities construct knowledge networks and respond to epistemic injustice. Through topic modeling, reflexive thematic analysis, and exponential random graph modeling (ERGM), we identify seven discourse themes spanning symptom documentation, medical dismissal, cross-illness solidarity, and policy advocacy. Our analysis reveals a differentiated ecosystem of user roles -- including patient advocates, research coordinators, and citizen scientists -- who collectively challenge medical gatekeeping while building connections to established ME/CFS advocacy networks. ERGM results demonstrate that tie formation centers on epistemic practices: users discussing knowledge sharing and community building formed significantly more network connections than those focused on policy debates, supporting characterization of this space as an epistemic community. Long COVID patients experienced medical gaslighting patterns documented across contested illnesses, yet achieved WHO recognition within months -- contrasting sharply with decades-long struggles of similar conditions. These findings illuminate how social media affordances enable marginalized patient populations to rapidly construct alternative knowledge systems, form cross-illness coalitions, and contest traditional medical authority structures.

“A Mysterious Malaise”

In this chapter provides a comprehensive overview of Long COVID and its most prevalent manifestation, fatigue, tracing the evolution of our understanding from the emergence of COVID-19 in December 2019 through 2024. First, the chapter examines the societal and economic impact of long COVID, affecting an estimated 400 million individuals globally by 2023, with annual economic costs reaching approximately $1 trillion. Through careful analysis of major research milestones and epidemiological studies, the text explores the condition’s prevalence, with fatigue affecting approximately 75% of Long COVID patients. The chapter presents detailed visualizations illustrating the chronological development of COVID-19 and Long COVID research, global cumulative incidence, economic impact across major Western economies, and the distribution of major symptoms. Special attention is given to the complex socioeconomic implications of the condition, including its disproportionate impact on marginalized communities and women and its threat to sustainable development goals. The chapter also examines workforce participation effects, with studies showing affected individuals being 10% less likely to be employed and working 25–50% fewer hours when employed. The text concludes by emphasizing the urgent need for continued research into underlying mechanisms and effective treatment strategies, setting the foundation for the book’s exploration of this significant public health challenge.

COVID-19 induces persistent transcriptional changes in adipose tissue that are not associated with Long COVID

Long COVID is a heterogeneous condition characterized by a wide range of symptoms that persist for 90 days or more following SARS-CoV-2 infection. Now more than five years out from the onset of the SARS-CoV-2 pandemic, the mechanisms driving Long COVID are just beginning to be elucidated. Adipose tissue has been proposed as a potential reservoir for viral persistence and tissue dysfunction contributing to symptomology seen in Long COVID. To test this hypothesis, we analyzed subcutaneous adipose tissue (SAT) from two cohorts: participants with subacute COVID-19 (28–89 days post-infection) compared to pre-pandemic controls, and participants with Long COVID compared to those with those classified as “indeterminate” based on the RECOVER-Adult Long COVID Research Index (12-47 months post-infection). We found no evidence of persistent SARS-CoV-2 RNA in adipose tissue in any participant. SAT from participants with subacute COVID-19 displayed significant transcriptional remodeling, including depleted immune activation pathways and upregulated Hox genes and integrin interactions, suggesting resident immune cell exhaustion and perturbations in tissue function. However, no consistent changes in gene expression were observed between Long COVID samples and samples from indeterminant participants. Thus, SAT may contribute to inflammatory dysregulation following COVID-19, but does not appear to play a clear role in Long COVID pathophysiology. Further research is needed to clarify the role of adipose tissue in COVID-19 recovery.

Integrating Zero-Shot Classification to Advance Long COVID Literature: A Systematic Social Media-Centered Review

Long COVID continues to challenge public health by affecting a significant segment of individuals who have recovered from acute SARS-CoV-2 infection yet endure prolonged and often debilitating symptoms. Social media has emerged as a vital resource for those seeking real-time information, peer support, and validating their health concerns related to Long COVID. This paper examines recent works focusing on mining, analyzing, and interpreting user-generated content on social media platforms such as Twitter, Reddit, Facebook, and YouTube to capture the broader discourse on persistent post-COVID conditions. A novel transformer-based zero-shot learning approach serves as the foundation for classifying research papers in this area into four primary categories: Clinical or Symptom Characterization, Advanced NLP or Computational Methods, Policy, Advocacy, or Public Health Communication, and Online Communities and Social Support. This methodology showcases the adaptability of advanced language models in categorizing research papers without predefined training labels, thus enabling a more rapid and scalable assessment of existing literature. This review highlights the multifaceted nature of Long COVID research, where computational techniques applied to social media data reveal insights into narratives of individuals suffering from Long COVID. This review also demonstrates the capacity of social media analytics to inform clinical practice and contribute to policy-making related to Long COVID.

Causal Inference via Electronic Health Records in the National Clinical Cohort Collaborative: Challenges and Solutions in Long COVID Research

Observational analyses of electronic health record (EHR) data using databases such as the National Clinical Cohort Collaborative include unique challenges for researchers seeking causal inferences, particularly when evaluating subjectively-defined outcomes like Long COVID. We explore several challenges and describe potential solutions. 1. Lack of true negatives: Many diagnoses and conditions either have a positive indicator or a missing status, requiring investigators to carefully consider which patients are likely negative for this condition. 2. Differential monitoring: EHR data include nonrandom missingness driven by patients engaging with the healthcare system at different rates, which is often related to both the exposure and outcome of interest. 3. Bias: EHR data sources face many biases, but are particularly vulnerable to informative missingness, differential monitoring, and model misspecification. 4. Large sample size: High precision (i.e., narrow confidence intervals) paired with potential bias leads to a high risk of incorrectly rejecting the null hypothesis. 5. Defining index time: It is important that investigators deliberately define index time (i.e., t ( 0 ) , baseline) to ensure that they only adjust for baseline confounders and do not adjust for (or condition on) factors that are affected by the exposure of interest (i.e., colliders or mediators). 6. Parameter selection: Investigators should only select parameters that are supported by the data distribution. This manuscript provides an overview of these challenges and solutions, using both simulated data and real-world data, with the outcome of Long COVID as the running example.

VEGFA sex-specific signature is associated to long COVID symptom persistence

Background Long COVID involves persistent symptoms after COVID-19 recovery, affecting multiple organ systems for months or years. Risk factors include female sex, prior chronic conditions, severe SARS-CoV-2 infection, reinfections, and lack of vaccination. As a major public health concern, ongoing research continues to investigate its causes, mechanisms, and long-term effects. Methods Proteomic expression analysis of 171 individuals, in two time points, with confirmed SARS-CoV-2 infection, including 133 long COVID patients from the deeply characterized COVICAT cohort, assessed 1395 protein biomarkers using Olink® technology. Statistical analyses with linear mixed models examined protein expression changes, long COVID status, and sex-specific differences. Functional analysis included gene set enrichment analysis and protein–protein interaction networks. Results Findings revealed VEGFA overexpression in long COVID patients (effect size 0.322, SE = 0.098, p  = 0.0013), along with sex-specific expression patterns and the influence of sex-hormonal status in females, with significant overexpression of circulating VEGFA levels specifically in postmenopausal women (Mann–Whitney U test p value = 8.55 × 10^−3). Network analysis identified 109 nodes and 274 edges, with VEGFA ranking highest in centrality. Dysregulated chemokine signaling, complement activation, and viral reactivation were also confirmed, consistent with prior studies. Conclusions Using high-throughput proteomic profiling in a population-based cohort, we observed that vascular dysfunction, particularly involving VEGFA, is a key feature of long COVID, especially in milder cases, with significant overexpression of VEGFA in postmenopausal women. Sex-specific proteomic patterns suggest distinct recovery mechanisms, highlighting the need to consider sex, vascular health, and disease severity in the pathogenesis and management of long COVID.

Post infectious fatigue and circadian rhythm disruption in long-COVID and other infections: a need for further research

Chronic fatigue syndrome (CFS) remains a subject of scientific research specifically with regards to its association with infections, including the more recently described Long COVID condition. Chronic fatigue and sleep disturbances in Long COVID are intricately linked to disruptions in circadian rhythms, driven by distinct molecular and cellular mechanisms triggered by SARS-CoV-2 infection. This can be driven by various mechanisms including dysregulation of key clock genes (CLOCK, BMAL1, PER2), mitochondrial dysfunction impairing oxidative phosphorylation, and cytokine-induced neuroinflammation (e.g., interleukin-6, tumor necrosis factor-alpha). Epigenetic changes, including DNA methylation at clock-related loci, particularly in peripheral tissues, further contribute to systemic circadian dysregulation. This work underscores the multifaceted molecular and systemic disruptions to circadian regulation in relation to fatigue and sleep disturbances identified as post-infectious sequelae, focusing on the Long COVID condition.

Statistical and Predictive Analysis to Identify Risk Factors and Effects of Post COVID-19 Syndrome

Based on recent studies, some COVID-19 symptoms can persist for months after infection, leading to what is termed long COVID. Factors such as vaccination timing, patient characteristics, and symptoms during the acute phase of infection may contribute to the prolonged effects and intensity of long COVID. Each patient, based on their unique combination of factors, develops a specific risk or intensity of long COVID. In this work, we aim to achieve two objectives: (1) conduct a statistical analysis to identify relationships between various factors and long COVID, and (2) perform predictive analysis of long COVID intensity using these factors. We benchmark and interpret various data-driven approaches, including linear models, random forests, gradient boosting, and neural networks, using data from the Lifelines COVID-19 cohort. Our results show that Neural Networks (NN) achieve the best performance in terms of MAPE, with predictions averaging 19\% error. Additionally, interpretability analysis reveals key factors such as loss of smell, headache, muscle pain, and vaccination timing as significant predictors, while chronic disease and gender are critical risk factors. These insights provide valuable guidance for understanding long COVID and developing targeted interventions. ;Comment: 8 pages, 9 figures, 2 tables, initially submitted in IJCNN 2025, but rejected because of the high number of contributions (requested to be presented as a poster in the conference without being published in conference proceedings)

Global Prevalence of Long COVID, its Subtypes and Risk factors: An Updated Systematic Review and Meta-Analysis

IMPORTANCE: Updated knowledge regarding the global prevalence of long COVID (or post-COVID-19 condition), its subtypes, risk factors, and variations across different follow-up durations and geographical regions is necessary for informed public health recommendations and healthcare delivery. OBJECTIVE: The primary objective of this systematic review is to evaluate the global prevalence of long COVID and its subtypes and symptoms in individuals with confirmed COVID-19 diagnosis, while the secondary objective is to assess risk factors for long COVID in the same population. DATA SOURCES: Studies on long COVID published from July 5, 2021, to May 29, 2024, searched from PubMed, Embase, and Web of Science were used for this systematic review. Supplemental updates to the original search period were made. STUDY SELECTION: There were four inclusion criteria: (1) human study population with confirmed COVID-19 diagnosis; (2) appropriate index diagnosis date; (3) outcome must include either prevalence, risk factors, duration, or symptoms of long COVID; and (4) follow-up time of at least two months after the index date. The exclusion criteria were: (1) non-human study population; (1) case studies or reviews; (2) studies with imaging, molecular, and/or cellular testing as primary results; (3) studies with specific populations such as healthcare workers, residents of nursing homes, and/or those living in long-term care facilities; and (4) studies that did not meet the sample size threshold needed to estimate overall prevalence with margin of error of 0.05. DATA EXTRACTION AND SYNTHESIS: Two screeners independently performed screenings and data extraction, and decision conflicts were collectively resolved. The data were pooled using a random-effects meta-analysis framework with a DerSimonian-Laird inverse variance weighted estimator. MAIN OUTCOMES AND MEASURES: The primary estimand (target population parameter of interest) was the prevalence of long COVID and its subtypes among individuals with confirmed COVID-19 diagnoses, and the secondary estimand was effect sizes corresponding to ten common risk factors of long COVID in the same population. RESULTS: A total of 442 studies were included in this mega-systematic review, and 429 were meta-analyzed for various endpoints, avoiding duplicate estimates from the same study. Of the 442 studies, 17.9% of the studies have a high risk of bias. Heterogeneity is evident among meta-analyzed studies, where the I (2) statistic is nearly 100% in studies that estimate overall prevalence. Global estimated pooled prevalence of long COVID was 36% among COVID-19 positive individuals (95% confidence interval [CI] 33%-40%) estimated from 144 studies. Geographical variation was observed in the estimated pooled prevalence of long COVID: Asia at 35% (95% CI 25%-46%), Europe at 39% (95% CI 31%-48%), North America at 30% (95% CI 24%-38%), and South America at 51% (95% CI 35%-66%). Stratifying by follow-up duration, the estimated pooled prevalence for individuals with longer follow-up periods of 1 to 2 years (47% [95% CI 37%-57%]) compared to those with follow-up times of less than 1 year (35% [95% CI 31%-39%]) had overlapping CI and were therefore not statistically distinguishable. Top five most prevalent long COVID subtypes among COVID-19 positive cases were respiratory at 20% (95% CI 14%-28%) estimated from 31 studies, general fatigue at 20% (95% CI 18%-23%) estimated from 121 studies, psychological at 18% (95% CI 11%-28%) estimated from 10 studies, neurological at 16% (95% CI 8%-30%) estimated from 23 studies, and dermatological at 12% (95% CI 8%-17%) estimated from 10 studies. The most common symptom based on estimated prevalence was memory problems estimated at 11% (95% CI 7%-19%) meta-analyzed from 12 studies. The three strongest risk factors for long COVID were being unvaccinated for COVID-19, pre-existing comorbidity, and female sex. Individuals with any of these risk factors had higher odds of having long COVID with pooled estimated odds ratios of 2.34 (95% CI 1.49-3.67) meta-analyzed from 6 studies, 1.59 (95% CI 1.28-1.97) from 13 studies, and 1.55 (95% CI 1.25-1.92) from 22 studies, respectively. CONCLUSIONS AND RELEVANCE: This study shows long COVID is globally prevalent in the COVID-19 positive population with highly varying estimates. The prevalence of long COVID persists over extended follow-up, with a high burden of symptoms 1 to 2 years post-infection. Our findings highlight long COVID and its subtypes as a continuing health challenge worldwide. The heterogeneity of the estimates across populations and geographical regions argues for the need for carefully designed follow-up with representative studies across the world. KEY POINTS: QUESTION: What are the prevalence and patterns of long COVID and its subtypes, and what are the risk factors of long COVID? RESULTS: Meta-analysis of 429 studies published from 2021-2024 estimated a pooled global long COVID prevalence of 36% in COVID-19 positive individuals. Variations in geographical regions showed that South America had the highest pooled prevalence of 51% (95% CI: 35%-66%), and the prevalence does not seem to diminish with extended follow-up (less than 1 year: 35%, 95% CI: 31%-39% vs. 1 to 2 years: 47%, 95% CI: 37%-57%). The estimated pooled prevalence of eight major long COVID subtypes in COVID-19 positive individuals were 20% (respiratory), 20% (general fatigue), 18% (psychological), 16% (neurological), 12% (dermatological), 10% (cardiovascular), 9% (musculoskeletal) and 5% (gastrointestinal). MEANING: Quantitative evidence shows a persistent prevalence of long COVID globally, with a significant burden of symptoms 1 to 2 years post-infection, underscoring the need for having accurate and standardized diagnostic tests and biomarkers for long COVID, a better understanding of the physiology of the condition, its treatment, and its potential effect on healthcare needs and workforce participation. The heterogeneity and wide range of the prevalence estimates call for representative samples in well-designed follow-up studies of long COVID across the world.

Prevalence of and factors associated with long COVID among US adults: a nationwide survey

Background People with long COVID report prolonged, multisystem involvement and significant disability. This study aimed to determine long COVID prevalence and factors associated with it among US adults using nationally representative data. Methods This cross-sectional analysis utilized data from 2022 Behavioral Risk Factor Surveillance System survey, a nationally representative telephone survey conducted among noninstitutionalized adults aged ≥ 18 years residing in the United States. Age-adjusted prevalence of long COVID was calculated using weighted survey analysis. Poisson regression was employed to assess adjusted prevalence ratios (aPRs) associated with long COVID across various demographic, socioeconomic and health-related characteristics. Results Among 390,233 participants, 120,178 reported COVID-19, with 25,582 experiencing long COVID. Age-adjusted prevalence of self-reported COVID-19 and long COVID were estimated at 34.1% (95% CI, 33.7–34.4%) and 7.2% (95% CI, 7.0–7.4%) as of 2022, respectively. Among adults reporting COVID-19, 20.9% (95% CI, 20.5–21.4%) had ever experienced long COVID. An inverted U-shaped association was observed between long COVID risk and age, with the highest prevalence (23.5%) in the 45–54 age group. Long COVID was more prevalent among women (aPR, 1.40 [95% CI, 1.34–1.47]), individuals without a spouse (aPR, 1.06 [95% CI, 1.00–1.13]), uninsured (aPR, 1.16 [95% CI, 1.06–1.27]), and those with a high school education (aPR, 1.17 [95% CI, 1.12–1.23]), cardiovascular disease (aPR, 1.17 [95% CI, 1.09–1.25]), depressive disorder (aPR, 1.41 [95% CI, 1.34–1.48]), chronic obstructive pulmonary disease (aPR, 1.33 [95% CI, 1.24–1.43]), asthma (aPR, 1.28 [95% CI, 1.21–1.35]), and kidney disease (aPR, 1.11 [95% CI, 1.01–1.21]). Long COVID was less prevalent among non-Hispanic Black (aPR, 0.87 [95% CI, 0.81–0.95]), students (aPR, 0.87 [95% CI, 0.76–0.99]) or retired individuals (aPR, 0.89 [95% CI, 0.82–0.98]), and those with household incomes ≥$100,000 (aPR, 0.85 [95% CI, 0.79–0.92]). Conclusions Long COVID affects 7.2% of US adults, with higher vulnerability among women, middle-aged individuals, White individuals, socioeconomically disadvantaged groups, and those with chronic conditions. These findings underscore the need for targeted public health strategies to address disparities in long COVID burden and support high-risk populations.

Examining The CoVCues Dataset: Supporting COVID Infodemic Research Through A Novel User Assessment Study

The public confidence and trust in online healthcare information have been greatly dented following the COVID-19 pandemic, which triggered a significant rise in online health misinformation. Existing literature shows that different datasets have been created to aid with detecting false information associated with this COVID infodemic. However, most of these datasets contain mostly unimodal data, which comprise primarily textual cues, and not visual cues, like images, infographics, and other graphic data components. Prior works point to the fact that there are only a handful of multimodal datasets that support COVID misinformation identification, and they lack an organized, processed and analyzed repository of visual cues. The novel CoVCues dataset, which represents a varied set of image artifacts, addresses this gap and advocates for the use of visual cues towards detecting online health misinformation. As part of validating the contents and utility of our CoVCues dataset, we have conducted a preliminary user assessment study, where different participants have been surveyed through a set of questionnaires to determine how effectively these dataset images contribute to the user perceived information reliability. These survey responses helped provide early insights into how different stakeholder groups interpret visual cues in the context of online health information and communication. The findings from this novel user assessment study offer valuable feedback for refining our CoVCues dataset and for supporting our claim that visual cues are underutilized but useful in combating the COVID infodemic. To our knowledge, this user assessment research study, as described in this paper, is the first of its kind work, involving COVID visual cues, that demonstrates the important role that our CoVCues dataset can potentially play in aiding COVID infodemic related future research work. ;10 pages, To Be Published In Proceedings Of The 1st IEEE Workshop on Healthcare and Medical Device Security, Privacy, Resilience, and Trust (IEEE HMD-SPiRiT), Accepted & Presented At The 7th IEEE International Conference on Trust, Privacy & Security in Intelligent Systems, and Applications (IEEE TPS 2025) on Nov. 11, 2025 in Pittsburgh, PA, USA

Open-science discovery of DNDI-6510, a compound that addresses genotoxic and metabolic liabilities of the COVID Moonshot SARS-CoV-2 Mpro lead inhibitor

The 2020 SARS-CoV-2 coronavirus pandemic highlighted the urgent need for novel small molecule antiviral drugs. (S)- x38 DNDI-6510 is a non-covalent SARS-CoV-2 main protease inhibitor developed by the open science collaboration COVID Moonshot. Here, we report on the metabolic and toxicologic optimization of the lead series previously disclosed by the COVID Moonshot Initiative, leading up to the selection of (S)- x38 DNDI-6510 as the preclinical candidate. We describe the thorough profiling of the series, identifying key risks such as formation of genotoxic metabolites and high clearance, which were successfully addressed during lead optimization. In addition, we disclose the in vitro and in vivo evaluation of (S)- x38 DNDI-6510 in pharmacokinetic and pharmacodynamic models, exploring multiple approaches to ameliorate rodent-specific metabolic clearance, and show that both co-dosing of (S)- x38 DNDI-6510 with an ABT inhibitor and utilizing a metabolically humanized mouse model (8HUM) achieve significant improvements in exposure. Through comparisons of ABT co-dosing and humanized mouse models in efficacy experiments, we demonstrate that continuous exposure over cellular EC (90) is required for SARS-CoV-2 antiviral efficacy in vivo in an antiviral model using a mouse-adapted SARS-CoV-2 strain. Finally, (S)- x38 DNDI-6510 was assessed in maximum tolerated dose experiments in two species, demonstrating significant in vivo PXR-linked auto-induction of metabolism, leading to the discontinuation of this compound. In summary, we report the successful effort to overcome series-specific AMES liabilities in a lead development program. Downstream optimization of existing series will require in-depth optimization of rodent-specific liabilities and metabolic induction profile.

Long COVID risk by pre-infection symptoms and functional status: A retrospective cohort study of data from the All of Us Research Program.

ABSTRACT IMPORTANCE: Over seven million U.S. adults experience "long COVID", or persistent health issues after COVID-19. Multiple guidelines recommend the inclusion of functional status in long COVID diagnostic criteria, but more evidence is needed to guide this recommendation. This study explores the adjusted odds of developing long COVID by pre-infection symptoms and functional status, and the feasibility of estimating functional status using health records data. DESIGN: Retrospective cohort study in a multicenter national longitudinal cohort of U.S. adults with history of COVID-19, using health records and survey responses through July 2022 (All of Us CDR 7.0). EXPOSURE(S): Pre-infection (-5 years) incidences of (a) at least one symptom common in long COVID, and (b) functional status, indicated by All of Us baseline survey responses and diagnostic/procedure/billing codes. Disease and demographic data covariates were included in the adjusted models. RESULTS: N = 65,464 met inclusion criteria (n=40,655 had post-infection occurrences of at least one symptom (long COVID group), while n=24,809 had none). Adjusted odds ratios within 99% confidence intervals [99% CI] of developing long COVID increased with lower pre-infection self-reported mental health ("Good" compared to "Excellent" AOR=1.14 [1.04,1.25], P>0.000), and more pre-infection symptoms (compared to the median of four, people with zero had much lower odds (AOR=0.15 [0.04,0.61], P=0.008). Adjusted odds were not significantly affected by any single pre-infection symptom, self-rated physical ability, or clinical documentation of functional impairment. CONCLUSIONS. Greater pre-illness symptom burden and lower self-rated mental health increased the odds of long COVID symptoms, after adjusting for demographics, variant, functional status, and individual symptoms. Long COVID represents a change from baseline functioning and health, even in people with pre-infection incident symptoms and functional impairments. This estimation of pre-infection functional status using harmonized electronic health records data demonstrated the feasibility of these data in developing the diagnostic utility of functional status changes in long COVID.

Post COVID 19 resurgence of diphtheria in Kano, Nigeria: analysis of 18,320 cases

BACKGROUND: The COVID 19 pandemic led to significant disruptions in health services, including immunisation programs, which contributed to a major post-pandemic diphtheria outbreak in Kano State, Nigeria. This region accounted for 85% of the nation's documented diphtheria cases. METHODS: This study examined the epidemiology, clinical characteristics, and mortality outcomes of cases diagnosed between February 2022 and April 2024. Data were collected through the Surveillance Outbreak Response Management and Analysis System (SORMAS), and case definitions followed WHO guidelines. Case fatality rate (CFR) was calculated, and a logistic regression model was used to assess mortality-related risk factors, reporting adjusted odds ratios (AOR). FINDINGS: A total of 18,320 cases were analysed, with the outbreak showing a bimodal distribution. The primary peak occurred in August 2023, followed by a smaller secondary peak in early 2024. The case fatality rate (CFR) was 4.5%. Patients who were not vaccinated had more than double the likelihood of death compared to fully vaccinated individuals (AOR 2.45; 95% CI: 2.05, 2.94, p < 0.0001; logistic regression). Similarly, patients without vaccination documentation also had greater odds, with more than 87% increase likelihood of mortality compared to fully vaccinated individuals (AOR 1.87, 95% CI: 1.27, 2.68, p < 0.0001; logistic regression). INTERPRETATION: Our study reinforces previous reports of the weakening preventive health delivery systems in resource constrained settings, particularly after the disruptions caused by the COVID 19 pandemic leading to the resurgence of vaccine preventable diseases, such as diphtheria. These highlight the need for improved vaccination coverage and surveillance systems. FUNDINGS: This research did not receive any external funding.

Causal association of long COVID with brain structure changes: Findings from a 2-sample Mendelian randomization study

Nearly 7.5% U.S. adults have long COVID. Recent epidemiological studies indicated that long COVID, is significantly associated with subsequent brain structure changes. However, it remains unknown if long COVID is causally associated with brain structure change. Here we applied two Mendelian Randomization (MR) methods – Inverse Variance Weighting MR method (IVW) for correlated instrument variables and Component analysis-based Generalized Method of Moments (PC-GMM) – to examine the potential causal relationships from long COVID to brain structure changes. The MR study was based on an instrumental variable analysis of data from a recent long COVID genome-wide association study (GWAS) (3,018 cases and 994,582 controls), the Enhancing NeuroImaging Genetics through Meta Analysis (ENIGMA) (Global and regional cortical measures, N = 33,709; combined hemispheric subcortical volumes, N = 38,851), and UK Biobank (left/right subcortical volumes, N = 19,629). We found no significant causal relationship between long COVID and brain structure changes. As we gain more insights into long COVID and its long-term health outcomes, future works are necessary to validate our findings and understand the mechanisms underlying the observed associations, though not causal, of long COVID with subsequent brain structure changes.

Assessing Neuropsychiatric Symptoms in Long COVID: A Retrospective Cohort Study from a South Texas Long COVID Clinic

Long COVID, previously known as Post-Acute Sequelae of SARS-CoV-2 (PASC), refers to prolonged symptoms or diagnosable conditions following COVID-19 infection. The neuropsychiatric profile of Long COVID patients remains ambiguous. This study aimed to assess neuropsychiatric symptoms in a retrospective cohort of Long COVID patients (N = 162) at a Rehabilitation Medicine clinic in South Texas. Clinical data from patient records were used to calculate a Symptom Score, and screening tools for stress/PTSD (PCL-5), depression (PHQ-9), anxiety (GAD-7), and quality of life (SWL) were employed to evaluate if Long COVID duration and severity could predict neuropsychiatric outcomes. The majority were female (71%) and Hispanics (53%) who presented for treatment of Long COVID symptoms during the study period, including fatigue (93%), coughing/shortness of breath (81%), fever (67%), anosmia (58%), ageusia (54%), and weight loss (56%). A minority of participants were hospitalized (N = 49) or required ventilator support (N = 5) during acute infection. There was a high burden of neuropsychiatric symptoms, including subjective cognitive impairment (79%), headache (74%), and insomnia (58%). Symptom Score (median = 9, IQR [8,11]) was significantly correlated with increased depression (PHQ-9; p < 0.05), anxiety (GAD-7; p < 0.05) and elevated stress/PTSD (PCL-5; p < 0.05) symptoms. Long COVID patients taking stimulants or mood stabilizers had higher GAD-7 (p < 0.031, p < 0.035) and PHQ-9 (p < 0.034, p < 0.009) scores but not PCL-5 scores. Importantly, duration of Long COVID symptomatology also did not predict PCL-5 scores. No patient factors (e.g., sex, age, BMI, ethnicity) mediated Symptom Score. Nonetheless, historically marginalized groups, such as women and Hispanics, have been disproportionately affected by COVID-19. This study is the first to utilize validated screening tools to determine the presence and severity of neuropsychiatric symptoms in Long COVID patients. These findings may guide clinical management and future research on Long COVID, especially in historically excluded populations. SCOPE STATEMENT: We enthusiastically submit our Original Research article, entitled “ Assessing Neuropsychiatric Symptoms in Long COVID: A Retrospective Cohort Study from a South Texas Long COVID Clinic ” for consideration for publication in the journal Frontiers in Neurology. We believe the scope of our article aligns well with the scope and aim of the journal’s Neurorehabilitation Section. Long COVID is a debilitating neurological disorder with prominent and enduring cognitive and psychological impact. This study sought to characterize Long COVID symptoms from a cohort of patients at a Rehabilitation Medicine/Long COVID clinic in Southwest Texas. We stratified symptoms using validated psychiatric evaluation tools (e.g., PCL-5, GAD-7, PHQ-9, SWL) to determine if and to what extent psychiatric comorbidity exacerbated Long COVID symptoms. Our findings suggest that a Long COVID patient’s depression, anxiety, and stress/post traumatic stress scores are highly correlated with other neurological symptoms. We advance the implementation of a Long COVID “Symptom Score”, as well as the use of validated screening instruments to identify psychiatric features of Long COVID with the goal of maximizing life satisfaction and function over the course of treatment.

"If it has an exclamation point, I step away from it, I need facts, not excited feelings": Technologically Mediated Parental COVID Uncertainty

As a novel virus, COVID introduced considerable uncertainty into the daily lives of people all over the globe since late 2019. Relying on twenty-three semi-structured interviews with parents whose children contracted COVID, we analyzed how the use of social media moderated parental uncertainty about the symptoms, prognosis, long-term potential health ramifications of infection, vaccination, and other issues. We framed our findings using Mishel's Uncertainty in Illness theory. We propose new components to the theory that account for technological mediation in uncertainty. We also propose design recommendations to help parents cope with health uncertainty using social media.

Long-term effects of the COVID-19 pandemic for patients with cancer

Since the outbreak in Wuhan (China) at the end of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has caused instability at various levels of society. While most patients completely recover from their SARS-CoV-2 infection, 10–20% of infected persons and up to 60% of infected patients with cancer develop long COVID. Long COVID is defined as the continuation of symptoms, which cannot be explained by alternative causes, that last longer than four weeks after initial infection. Even though it is generally accepted that patients with cancer are at increased risk of developing severe COVID-19, it is still unclear how long COVID manifests and whether long COVID impacts quality of life in this cohort. Hence, this study observed that patients with cancer reported a negative impact of the enforced COVID-19 restrictions on the emotional and psychological wellbeing. While patients with cancer experience similar long COVID symptoms as healthy controls, the prevalence is remarkably higher possibly due to their compromised immune system and weakened physiological reserve. Introduction: Long COVID is defined as the continuation of symptoms, unexplainable by alternative diagnosis, longer than four weeks after SARS-CoV-2 infection. These symptoms might hinder daily activities and overall well-being, ultimately impacting quality of life (QoL). Several studies have reported fatigue as the most common symptom, followed by dyspnoea, headache and myalgia. Although it is assumed that long COVID affects 10–20% of SARS-CoV-2 infected individuals, recently numbers up to 60% were described for patients with cancer. This study uncovers the impact of the COVID-19 pandemic on QoL of patients with cancer and how long COVID manifests in this cohort. Methods: A group of 96 patients with cancer was followed from March 2022 till March 2023. Online questionnaires assessing symptoms associated with long COVID, anxiety and depression (HADS), quality of life (EORTC-QLQ-C30) and cognitive functioning (CFQ) were sent every three months during this period. Furthermore, a semi-structured focus group was organised for qualitative data collection. Results: Overall, these patients reported a negative impact of the enforced COVID-19 restrictions on the emotional and psychological wellbeing. Forty nine patients with cancer (51.0%) were infected with SARS-CoV-2 over the course of the study, of which 39 (79.6%) reported long COVID symptoms. The most commonly reported symptoms were myalgia (46.2%), fatigue (38.5%) and disturbed sleep (35.9%) and it was observed that male sex is associated with poor long COVID outcomes. Conclusion: While patients with cancer experience similar long COVID symptoms as healthy controls, the prevalence is remarkably higher possibly due to their compromised immune system and weakened physiological reserve.

Risk of developing long COVID based on acute COVID-19 severity

Aim Long-haul COVID-19 poses a profound burden to public health. According to the CDC, approximately one in five people who contract COVID-19 have lingering COVID-related symptoms 3 months after testing positive for COVID-19. This paper examines how overall symptom severity in the acute COVID-19 period is related to the risk of developing long COVID and experiencing severe long COVID-related symptoms within the US Veteran population. Subject and methods This study used the Ipsos KnowledgePanel technique to survey 3340 US Veterans and their experiences with COVID-19. Of those surveyed, 658 Veterans had tested positive for COVID-19 and 302 (46%) had symptoms that lasted more than 4 weeks. Veterans reported the types of symptoms they had, the severity of each symptom, and the duration of each symptom. The survey was conducted in October and November of 2021. Results The most common symptoms experienced 4 weeks after testing positive were fatigue, joint pain, headaches, coughing, and fever. Older patients were more likely to experience long COVID, but age was not statistically associated with severity of symptoms experienced during long COVID. The strongest predictors of whether or not a person would develop long COVID and/or severe long COVID symptoms was the severity of their symptoms during the acute phase. Conclusion Understanding the risk factors associated with developing long COVID and severe long COVID will help identify people who would benefit from an intervention that can either mitigate or at least manage the symptoms to prevent further health problems.

To Explore the Potential Inhibitors against Multitarget Proteins of COVID 19 using In Silico Study

The global pandemic due to emergence of COVID 19 has created the unrivaled public health crisis. It has huge morbidity rate never comprehended in the recent decades. Researchers have made many efforts to find the optimal solution of this pandemic. Progressively, drug repurposing is an emergent and powerful strategy with saving cost, time, and labor. Lacking of identified repurposed drug candidates against COVID 19 demands more efforts to explore the potential inhibitors for effective cure. In this study, we used the combination of molecular docking and machine learning regression approaches to explore the potential inhibitors for the treatment of COVID 19. We calculated the binding affinities of these drugs to multitarget proteins using molecular docking process. We perform the QSAR modeling by employing various machine learning regression approaches to identify the potential inhibitors against COVID 19. Our findings with best scores of R2 and RMSE demonstrated that our proposed Decision Tree Regression (DTR) model is the most appropriate model to explore the potential inhibitors. We proposed five novel promising inhibitors with their respective Zinc IDs ZINC (3873365, 85432544, 8214470, 85536956, and 261494640) within the range of -19.7 kcal/mol to -12.6 kcal/mol. We further analyzed the physiochemical and pharmacokinetic properties of these most potent inhibitors to examine their behavior. The analysis of these properties is the key factor to promote an effective cure for public health. Our work constructs an efficient structure with which to probe the potential inhibitors against COVID-19, creating the combination of molecular docking with machine learning regression approaches. ;Comment: 22 pages

Impact of Preexisting Rare Diseases on COVID-19 Severity, Reinfection, and Long COVID, and the Modifying Effects of Vaccination and Antiviral Therapy: A Retrospective Study from the N3C Data Enclave

BACKGROUND: Over 10,000 rare diseases (RDs) affect more than 300 million people globally, yet their influence on COVID-19 severity, reinfection risk, and long COVID remains poorly understood. This study evaluates the impact of RDs on these outcomes and examines the effectiveness of vaccination and antiviral treatments among individuals with and without RDs. METHODS: We conducted a retrospective cohort study using harmonized electronic health records (EHRs) from the National COVID Cohort Collaborative (N3C), encompassing 21,704,702 individuals, including 4,825,605 with confirmed SARS-CoV-2 infection between Jan 1, 2020, and Jan 4, 2024. RDs were defined using 12,003 conditions curated from GARD and Orphanet, mapped to OMOP concepts, and classified into 18 RD classes based on medical specialty involvement. Primary outcomes included: (1) COVID-19 severity (hospitalization and life-threatening disease), (2) long COVID, and (3) SARS-CoV-2 reinfection. We applied multivariable logistic regression with inverse probability of treatment weighting and reported adjusted odds ratios with 95% confidence intervals and associated p-values. Models were controlled for demographics, comorbidities, and exposure to vaccination and antiviral treatments. FINDINGS: Of 21,704,702 individuals, we identify 4,825,605 COVID-19 positive individuals, 6.36% had RDs, with markedly higher rates of rare disease (RD) patients that have life-threatening illness (16% vs. 6.1% without life-threatening illness) and that are hospitalized (13% vs. 6.0% without hospitalization). Otorhinolaryngologic diseases showed the highest risk of life-threatening outcomes (OR 4.51; 95% CI 3.81-5.33), followed by developmental defect during embryogenesis (OR 1.84; 95% CI 1.72-1.98) and cardiac conditions (OR 1.79; 95% CI 1.51-2.11). Hospitalization risk was highest for otorhinolaryngologic (OR 2.90; 95% CI 2.61-3.23), developmental defect during embryogenesis (OR 2.06; 95% CI 1.97-2.16), and hematologic and endocrine diseases (OR 1.81; 95% CI 1.75-1.87 and OR 1.81; 95% CI 1.64-1.99, respectively). In patients with RDs, vaccination alone or antiviral treatment alone was associated with reduced odds of life-threatening COVID-19 disease compared to non-vaccinated individuals (OR 0.71; 95% CI 0.66-0.77 and OR 0.33; 95% CI 0.26-0.42, respectively). The combination of both vaccination and antiviral treatment showed the greatest reduction in odds ratio (OR 0.24; 95% CI 0.20-0.27). Similar results were observed in patients without RDs. In contrast, vaccination or antiviral therapy alone, compared to no intervention, did not significantly reduce long COVID risk in RD patients, although these interventions alone did result in a lower odds ratio in patients without RD. However, their combination was protective in both groups. Vaccination alone, compared to no vaccination, also reduced the risk of reinfection across RD and non-RD populations. INTERPRETATION: RD patients face elevated risks of severe COVID-19 outcomes. While vaccination and antivirals significantly reduce the acute severity of illness, their impact on long COVID appears limited in this population. Notably, vaccination was protective against COVID-19 reinfection in both RD and non-RD populations. These findings highlight the need for targeted strategies to protect RD patients beyond current interventions, particularly in preventing long-term complications. FUNDING: This work was supported in part by the intramural and extramural programs at NCATS (ZIA ZICTR000410).

Immunological Density Shapes Recovery Trajectories in Long COVID

Post-acute sequelae of SARS-CoV-2 infection (Long COVID) frequently persists for months, yet drivers of clinical remission remain incompletely defined. Here we analyzed 97,564 longitudinal PASC assessments from 13,511 participants with linked vaccination histories to disentangle passive temporal progression from vaccine-associated change. Using a clinically validated threshold (PASC $\geq 12$), trajectories separated into three phenotypes: Protected (persistently sub-threshold), Refractory (persistently symptomatic), and Responders (transitioning from symptomatic to recovered). Across the full cohort, symptom severity increased modestly with elapsed time ($r=0.0521$, $P=1.26\times10^{-59}$), whereas cumulative vaccination showed an inverse association with severity ($r=-0.0434$, $P=5.95\times10^{-42}$). In summary, baseline Long COVID severity appears clinically deterministic. In the absence of intervention, symptoms typically persist without spontaneous resolution. Recovery is primarily associated with repeated immunization.

COVID-19 Vaccination Timing, Relative to Acute COVID-19, and Subsequent Risk of Long COVID

OBJECTIVES: Long COVID is a debilitating condition that impacts millions of Americans, but patients and clinicians have little information on how to prevent this disorder. Vaccination is a vital tool in preventing acute COVID-19 and may confer additional protection against Long COVID. There is limited evidence regarding the optimal timing of COVID-19 vaccination (i.e., vaccination schedule) to minimize the risk of Long COVID. METHODS: We applied Longitudinal Targeted Maximum Likelihood Estimation to electronic health record (EHR) data from a retrospective cohort of patients vaccinated against COVID-19 between December 2021 and September 2022. We evaluated the association between binary COVID-19 vaccination status (two or more doses vs. zero doses) and 12-month Long COVID risk among patients diagnosed with acute COVID-19 between December 2021 and September 2022. In addition, we compared the 12-month cumulative risk of Long COVID (ICD-10 code U09.9) among patients diagnosed with acute COVID-19 one to three months after vaccination, three to five months after vaccination, or five to seven months after vaccination while adjusting for relevant high-dimensional baseline and time-dependent covariates. RESULTS: We analyzed EHR data from a retrospective cohort of 1,558,018 patients. In our binary cohort (n = 519,980), we found that vaccinated patients had a lower risk of Long COVID than unvaccinated patients (adjusted marginal risk ratio 0.84 (0.81, 0.88)). In our longitudinal cohort (n = 1,085,291), we did not find a significant difference in Long COVID risk comparing patients who were diagnosed with acute COVID-19 one to three months after vaccination versus patients who were diagnosed with COVID-19 three to five months (adjusted marginal risk ratio 0.93 (95% CI 0.62, 1.41) or 5 to 7 months (adjusted marginal risk ratio 1.06 (95% CI 0.72, 1.56)) after vaccination. CONCLUSIONS: We found that COVID-19 vaccination before SARS-CoV-2 infection was protective against Long COVID, and we did not find that this protection significantly waned within 7 months after vaccination. These findings suggest that COVID-19 vaccination protects against Long COVID.

IMPACT OF COVID-19 PANDEMIC ON THE SURGICAL TREATMENT OF GASTRIC CANCER: A 3-YEAR ANALYSIS

BACKGROUND: The COVID-19 pandemic has overloaded healthcare systems worldwide. Other diseases, such as neoplasms, including gastric cancer, remained prevalent and had their treatment compromised. AIMS: The aim of this study was to evaluate the impact of the COVID-19 pandemic on the treatment of gastric cancer and adherence to the recommended preoperative COVID-19 screening protocol. METHODS: A retrospective study evaluated patients diagnosed with gastric adenocarcinoma who underwent surgical treatment between 2015 and 2023. RESULTS: A total of 769 patients with gastric cancer were evaluated and organized into two groups: (i) pre-COVID group and (ii) COVID group. The pre-COVID group consisted of 527 patients operated on between 2015 and 2019, and the COVID group consisted of 242 patients from 2020 to 2023. The average number of surgical procedures per year in the pre-COVID group was 105 and 81 in the COVID group. There was a statistically significant difference between ASA classification (p=0.002) and clinical staging (p=0.015), which were worse in the COVID group. We observed an increase in diagnostic surgeries (p=0.026), with an increase in the minimally invasive route (p<0.001). In patients undergoing curative surgery, there was a greater indication for postoperative ICU (p=0.022) and neoadjuvant chemotherapy (p<0.001). There was no difference in 30- and 90-day mortality. CONCLUSIONS: The surgical and oncological outcomes for patients operated on during the pandemic remained uncompromised, even though many presented with more advanced initial stages and poorer clinical performance. High adherence to protocols and a low rate of complications related to coronavirus indicate that surgeries were performed safely during this period.

Restrictive migration policies and their impact on HIV prevention, care and treatment services

Migration policies have a significant impact on population health, particularly for individuals living with human immunodeficiency virus (HIV). These policies not only determine who is allowed to enter a country but also influence which immigrants can access services provided by the government. Some countries continue to impose restrictions on HIV-positive individuals, justifying these measures as necessary to protect public health and mitigate healthcare and economic concerns. However, these restrictions lack a valid public health rationale. Due to social, economic and political constraints, restrictive migration laws hinder access to HIV prevention, care and treatment services for immigrants living with HIV. Immigrants face numerous challenges in accessing medication, adhering to treatment regimens and benefitting from HIV preventive efforts. This situation increases the risk of HIV infection and adverse health outcomes due to limited access to preventive programmes, social stigma and engagement in risky behaviours. Additionally, these restrictive migration rules negatively affect immigrants’ mental health. To improve the health of both immigrants and host communities, inclusive and evidence-based migration policies that address healthcare through public health and human rights lenses are required.

Why some countries but not others? Urbanisation, GDP and endemic disease predict global SARS-CoV-2 excess mortality patterns

Context The global impact of the SARS-CoV-2 pandemic has been uneven, with some regions experiencing significant excess mortality while others have been relatively unaffected. Yet factors which predict this variation remain enigmatic, particularly at large spatial scales. Objectives We aimed to uncover the key drivers of excess mortality across countries and regions to help understand the factors contributing to the varied impacts of the pandemic worldwide. Methods We used spatially explicit Bayesian models that integrate environmental, socio-demographic and endemic disease data at the country level to provide robust global estimates of excess SARS-CoV-2 mortality (P-scores) for the years 2020 and 2021. Results We find that urbanization, gross domestic product (GDP) and spatial patterns are strong predictors of excess mortality, with countries characterized by low GDP but high urbanization experiencing the highest levels of excess mortality. Intriguingly, we also observed that the prevalence of malaria and human immunodeficiency virus (HIV) are associated with country-level SARS-CoV-2 excess mortality in Africa and the Western Pacific, whereby countries with low HIV prevalence but high malaria prevalence tend to have lower levels of excess mortality. While these associations are correlative in nature at the macro-scale, they emphasize that patterns of endemic disease and socio-demographic factors are needed to understand the global dynamics of SARS-CoV-2. Conclusions Our study identifies factors associated with variation in excess mortality across countries, providing insights into why some were more impacted by the pandemic than others. By understanding these predictors, we can better inform global outbreak management strategies, such as targeting medical resources to highly urban countries with low GDP and high HIV prevalence to reduce mortality during future outbreaks.

High prevalence of late presentation with advanced HIV disease and its predictors among newly diagnosed patients in Kumasi, Ghana

Background Late presentation with advanced HIV disease (LP-AHD) remains a significant challenge to Human Immunodeficiency Virus (HIV) care, contributing to increased morbidity, mortality, and healthcare costs. Despite global efforts to enhance early diagnosis, a considerable proportion of individuals with HIV infection are unaware of being infected and therefore present late for HIV care. For the first time in Ghana, this study assessed the prevalence of LP-AHD and associated factors among people diagnosed with HIV (PDWH). Method This bi-center retrospective cross-sectional study included 315 PDWH at the Aniniwah Medical Centre and Komfo Anokye Teaching Hospital, both in Kumasi, Ghana. A well-structured questionnaire was used to collect data on sociodemographic, clinical, lifestyle and psychosocial factors from the study participants. Statistical analyses were done in SPSS version 26.0 and GraphPad Prism version 8.0 at significant p -value of < 0.05 and 95% confidence interval. Predictors of LP-AHD were assessed using binary logistic regression models. Results This study observed that, 90 out of the 315 study PDWH (28.6%) reported late with advanced HIV disease (AHD). Participants within the age group of 36–45 years (adjusted Odds Ratio [aOR]: 0.32, 95% CI: 0.14–0.69; p  = 0.004) showed a significantly decreased likelihood of LP-AHD. However, participants who perceived cost of HIV care to be high (aOR: 7.04, 95% CI: 1.31–37.91; p  = 0.023), who were diagnosed based on clinical suspicion (aOR: 13.86, 95 CI: 1.83–104.80; p  = 0.011), and missed opportunities for early diagnosis by clinicians (aOR: 2.47, 95% CI: 1.30–4.74; p  = 0.006) were significantly associated with increased likelihood of LP-AHD. Conclusion The prevalence of LP-AHD among PDWH in Ghana is high. Efforts to improve early initiation of HIV/AIDS care should focus on factors such as the high perceived costs of HIV care, diagnosis based on clinical suspicion, and missed opportunities for early diagnosis by physicians.

Abnormal Vaginal Microbiota Associated with miRNA Targeting the HIV-Host Interactome

Understanding the molecular mechanisms underlying the ability of vaginal dysbiosis to alter the mucosal barrier to HIV acquisition is an essential step toward prevention. We hypothesized that micro(mi)-RNAs dysregulated by vaginal pathobiont bacteria epigenetically control host pathways exploited by the virus. The impact of these endogenous non-coding short RNAs on the anti-viral mucosal barrier function in the female reproductive tract is largely unknown. This study utilized cervicovaginal specimens collected during the luteal and follicular phase of the menstrual cycle along with data on age, race, ethnicity, education, and body mass index from 141 healthy reproductive-age women confirmed negative for sexually transmitted infections. Vaginal microbiota was classified by Nugent scoring. Shot-gun vaginal microbiome sequencing and metagenome taxonomic classification was performed on a subset of 21 women. Levels of miRNAs in exosomes isolated from cervicovaginal secretions were quantified using the EdgeSeq-NextGen global transcriptome platform. Differential expression (DE) was determined using R. Epigenetic target prediction was performed using MirTarBase. MiRNA profiles varied by both Nugent score categories (0-3 scores = normal, 4-6 = intermediate, and 7-10 = bacterial vaginosis, BV) and by metagenome classification. Higher microbiome diversity was associated with higher number of significantly dysregulated miRNAs (588 in BV compared to Nugent 0-3 versus 42 in Nugent 4-6 compared to Nugent 0-3, false discovery rate FDR<0.01) affecting over 400 experimentally validated genes targeted for post-transcriptional regulation. The miRNAs dysregulated by G. vaginalis -dominated compared to L. crispatus -dominated metagenomes included 24 DE miRNAs (92% overlap with BV by Nugent score) and 112 validated target genes. BV-dysregulated miRNA mediated the immunosuppressive effects of BV on cytokine levels previously associated with HIV acquisition risk. The gene ontology predictions based on BV-dysregulated miRNAs identified enrichment for 445 downregulated and 50 upregulated genes previously validated as part of the HIV-host interactome. miRNAs mediation revealed a mechanism of suppressed immunity by BV predictive of HIV risk. In conclusion, miRNAs dysregulated by vaginal dysbiosis may facilitate immune imbalance and cellular pathways associated with HIV risk.

Social network isolation and cohesion and their association with HIV clustering among Black and Latino sexual minority men: implications for public health strategy

Evidence shows social networks shape HIV transmission by influencing partner access, behaviors, and health. Molecular cluster analysis offers insight into recent transmission, yet little is known about how network isolation or cohesion relate to clustering. This study examined social networks to identify factors associated with molecular HIV clustering. Data are from a cross-sectional study among Black and Latinx sexual minority men collected 2022–24 in Raleigh-Durham, NC. Participants nominated up to 20 friends and 20 sex partners. Other survey measures included age, gender, and socioeconomic status. Dyadic homophily was calculated using Jaccard similarity for race, STI status, drug use, and ethnicity. Network measures included the number of alters and a constraint score reflecting embeddedness. Molecular clusters were identified using pol sequences and nextHIV2. Ordinal logistic regression assessed associations with HIV status and cluster membership, adjusting for covariates. Among 100 participants (387 contacts), 60% were HIV-negative, 18% were people living with HIV (PWH) not in clusters, and 22% were PWH in clusters. Contact networks were sparse. Older age and more contacts were associated with cluster membership. Racial homophily increased odds of clustering, whereas larger networks, lower constraint, and greater STI negative status similarity reduced odds. Findings indicate that homophily and network structure are associated with HIV clustering and may inform HIV prevention strategies.

Percent of lung involved in disease on chest X-ray predicts unfavorable treatment outcome in pulmonary tuberculosis

Radiology may better define tuberculosis (TB) severity and guide duration of treatment. We aimed to systematically study baseline chest X-rays (CXR) and their association with TB treatment outcome using real-world data. We used logistic regression to associate TB treatment outcomes with CXR findings, including percent of lung involved in disease (PLI), cavitation, and Timika score, alone or in combination with other clinical characteristics, stratifying by drug resistance status and HIV (n = 2,809). We fine-tuned convolutional neural nets (CNN) to automate PLI measurement from the CXR DICOM images (n = 5,261). PLI is the only CXR finding associated with unfavorable outcome across drug resistance and HIV subgroups [Rifampicin-susceptible disease without HIV, adjusted odds ratio (aOR) 1·11 (1·01, 1·22), P-value 0·025]. The most informed model of baseline characteristics tested predicts outcome with a validation mean area under the curve (AUC) of 0·769. PLI and Timika (AUC 0·656 and 0·655 respectively) predict unfavorable outcomes better than cavitary information (best AUC 0·591). The addition of PLI improves prediction compared to sex and age alone (AUC 0·680 and 0·627, respectively).PLI>25% provides a better separation of favorable and unfavorable outcomes compared to PLI>50%. The best performing ensemble of CNNs has an AUC 0·850 for PLI>25% and mean absolute error of 11·7% for the PLI value. PLI is better than cavitation for predicting unfavorable treatment outcome in pulmonary TB in non-clinical trial settings and it can be accurately and automatically predicted with CNNs. ONE SENTENCE SUMMARY: The percent of lung involved in disease improves prediction of unfavorable outcomes in pulmonary tuberculosis when added to clinical characteristics.

Molecular epidemiology of JC polyomavirus genotypes in PLWH from Turkey

Background JC polyomavirus (JCPyV) is a globally prevalent human polyomavirus that establishes lifelong latency, primarily in renal tissue. Despite its global importance, molecular epidemiological data on JCPyV still remain limited. Objectives This study aimed to investigate the prevalence, genotype distribution, and genetic variations of JCPyV in urine and plasma samples from people living with HIV (PLWH) in Turkey. Additionally, we explored the correlation between JCPyV presence, immunological parameters, and demographic factors, providing the first molecular epidemiological report of JCPyV in this population. A prospective, multicentre, cross-sectional study was conducted on 107 PLWH and 77 healthy controls. JCPyV DNA was detected and quantified using qPCR, and VP1 gene sequencing was performed to determine viral genotypes. Phylogenetic analysis was conducted using Clustal Omega and the Neighbour-Joining method with a bootstrap value of 1000. Results JCPyV viruria was detected in 46% of PLWH and 18.18% of healthy individuals, with no significant association between viruria frequency and immunodeficiency severity ( p  > 0.05). Genotype IV was the most prevalent (37.5%), followed by Genotype I (31.25%) and Genotype II (31.25%), aligning with European epidemiological data. No Genotype III was detected. No VP1 mutations associated with PML or immune evasion were identified. However, amino acid substitutions were observed at positions 74, 92, 116, 127, and 133, warranting further investigation. Conclusion This study provides the first molecular epidemiological analysis of JCPyV in PLWH from Turkey, demonstrating a genotype distribution consistent with European data. While no significant PML-associated VP1 mutations were detected, the identification of substitutions underscores the need for continued molecular surveillance. Understanding JCPyV genotype dynamics and immune evasion strategies is crucial for developing targeted therapeutics, including VP1-based vaccines and monoclonal antibody treatments for high-risk populations.

Cross-sectional and Longitudinal Association Between Sleep and HIV Prevention and Care Behaviors Among Transgender Women of Color: The TURNNT Cohort Study

We investigated the association between sleep health and HIV care and prevention outcomes among transgender women of color (TWOC). We used data from TWOC living in New York City collected from 2020 to 2022. Our exposures of interest were short sleep (sleeping for less than seven hours per night), poor-quality sleep (self-rated quality of sleep as “very bad” or “fairly bad”), and long sleep onset latency (taking at least 30 min to fall asleep). We asked participants about their HIV care and prevention outcomes, including HIV/STI testing, condom use, PrEP use, and HIV viral load suppression. We used Targeted Maximum Likelihood Estimation to estimate the association between sleep and these outcomes and included age, education, income, US-born nativity, and hormone replacement therapy use as potential confounders. Among the 314 participants, 54.5% had short sleep, 35.0% had poor-quality sleep, and 24.8% experienced long sleep onset latency. Among people living with HIV (51.6%), those who had short amounts of sleep were less likely to always use a condom (Adjusted Prevalence Ratio [aPR]: 0.67; 95% CI: 0.51–0.87) and be virally suppressed (aPR: 0.87; 95% CI: 0.79–0.96), and those who had poor-quality sleep were less likely to be virally suppressed (aPR: 0.83; 95% CI: 0.72–0.96). Among people living without HIV (46.2%), experiencing long sleep onset latency decreased the likelihood of always using condoms (aPR: 0.36; 95% CI: 0.15–0.85). Improving sleep health among TWOC could improve HIV care and prevention outcomes and reduce health inequities. Investigamos la asociación entre la salud del sueño y los resultados de atención y prevención del VIH entre mujeres transgénero de color. Utilizamos datos de mujeres transgénero de color residentes en la ciudad de Nueva York recopilados entre 2020 y 2022. Nuestras exposiciones de interés fueron sueño corto (dormir menos de siete horas por noche), sueño de mala calidad (calidad de sueño autoevaluada como “muy mala” o “bastante mala”) y latencia de inicio del sueño prolongada (tardar al menos 30 minutos en conciliar el sueño). Preguntamos a las participantes sobre sus resultados de atención y prevención del VIH, incluyendo pruebas de VIH/ITS, uso de condones, uso de PrEP y supresión de la carga viral del VIH. Utilizamos Targeted Maximum Likelihood Estimation para estimar la asociación el sueño y estos resultados e incluimos la edad, la educación, los ingresos, el lugar de nacimiento y el uso de terapia de reemplazo hormonal como posibles factores de confusión. Entre las 314 participantes, el 54,5% tuvo sueño corto, el 35,0% tuvo sueño de mala calidad y el 24,8% experimentó latencia de inicio del sueño prolongada. Entre las personas con VIH (51,6%), quienes dormían poco tenían menos probabilidades de usar siempre preservativo (razón de prevalencia: 0,67; IC del 95%: 0,51-0,87), y de presentar supresión viral (0,87; IC del 95%: 0,79-0,96); y quienes dormían mal tenían menos probabilidades de presentar supresión viral (0,83; IC del 95%: 0,72-0,96). Entre las personas sin VIH (46,2%), quienes experimentaban una larga latencia del sueño disminuyeron la probabilidad de usar siempre preservativo (0,36; IC del 95%: 0,15-0,85). Mejorar la salud del sueño entre las mujeres con VIH podría mejorar los resultados de la atención y la prevención del VIH y reducir las desigualdades en salud.

Renal and Cardiovascular Complications Following Type 2 Diabetes Mellitus in People With and Without HIV: Data From the Cohort Study on Morbidity and HIV in Sweden (COSMOHS) Between 2010 and 2024

BACKGROUND: Type 2 diabetes mellitus (T2DM) is common among people with HIV (PWH), although studies comparing T2DM-related complications in people with and without HIV (PWoH) remain limited. This was assessed using data from the Cohort Study on Morbidity and HIV in Sweden (COSMOHS). METHODS: Nationwide study including Swedish residents born 1930–2006, diagnosed with T2DM between 2010 and 2019, followed until 31 December 2024, using 7 national registers including national HIV and diabetes registers. Follow-up began at T2DM diagnosis. Outcomes included renal events, cardiovascular events, and all-cause mortality. Cox proportional regression estimated hazard ratios (adjHRs) by HIV status, stratified by propensity score quintiles of age, sex, migrant status, comorbidities, and socioeconomics. RESULTS: 350 PWH and 311 668 PWoH were included, with similar median follow-up time (PWH, 7.8 years; PWoH, 8.8 years). PWH had higher renal risk than PWoH (major adverse kidney event: adjHR, 2.04; 95% CI, 1.57-2.65) but no significantly increased cardiovascular risk (major adverse cardiovascular event: adjHR, 1.18; 95% CI, .87–1.60) or all-cause mortality. Findings were consistent across sensitivity analyses, including competing-risk models for death and excluding PWH receiving tenofovir disoproxil fumarate at baseline. Accounting for the benign plasma/serum-creatinine increase associated with bictegravir, cobicistat, dolutegravir, and rilpivirine, the increased renal risk remained, although not statistically significant. The renal risk was most prominent in PWH with BMI ≥30 kg/m(2). CONCLUSIONS: In this nationwide cohort, PWH with T2DM exhibited higher risk of renal complications than PWoH, indicating enhanced renal monitoring may be warranted. Further research should investigate underlying mechanisms, including antiretroviral therapy, to guide clinical management.

Brief communication: The rate of switching from first-line to second-line antiretroviral therapy among people living with HIV in Aden City, Yemen

Background Effective management of antiretroviral therapy (ART) is crucial in combating the global HIV pandemic. This study, the first of its kind in Yemen, investigates the rate and determinants of switching from first-line to second-line ART among people living with HIV (PLWH) in Aden City, Yemen. Methods A retrospective cohort study was conducted using data from PLWH who started first-line ART at Al-Wahda Hospital from 2007 to May 2022. PLWH in prevention of mother-to-child transmission (PMTCT) programs, those already on second-line ART at enrollment, and those with less than 3 months of follow-up were excluded. Cumulative incidence curves and multivariable proportional hazards models were used to identify factors associated with switching, considering death and loss to follow-up as competing risks. Analyses were carried out using IBM SPSS version 26. Results Out of 149 PLWH, 18 (12.1%) switched to second-line ART with a cumulative incidence rate of 1.8 per 100 person-years. Significant factors for switching included being older than 33 years (HR: 1.45, 95% CI: 1.12–1.89), having WHO stage 3 disease (HR: 1.58, 95% CI: 1.21–2.06), and being on a TDF-FTC-EFV-based first-line regimen (HR: 1.35, 95% CI: 1.03–1.77). This switching rate is consistent with rates observed in other resource-limited settings, indicating it is neither exceptionally high nor low compared to similar contexts​. Conclusions The study highlights key factors associated with switching to second-line ART in Yemen, emphasizing the need for targeted interventions and continuous monitoring to enhance treatment outcomes. These findings are consistent with regional data from other resource-limited settings.

Fractal-Fractional HIV Dynamics with Mittag-Leffler Kernel: Analysis, Stability, and Numerical Simulations

In this paper, a fractal--fractional HIV model with the Mittag--Leffler kernel is proposed using the Atangana--Baleanu--Caputo operator to capture the memory and hereditary properties of the disease dynamics. The existence and uniqueness of the solutions are investigated using suitable analytical techniques, and the Hyers--Ulam stability analysis is carried out to verify the stability behavior of the proposed system. For the numerical simulations, the Newton polynomial approximation method together with the Atangana--Toufik numerical scheme is employed to obtain approximate solutions for different parameter settings. Furthermore, several visualization techniques, including sensitivity heatmap representation and tornado diagram analysis, are utilized to study the influence of model parameters on the HIV dynamics. The obtained numerical results demonstrate that the proposed fractal--fractional framework provides an effective and reliable approach for analyzing the transient and long-term behavior of HIV transmission dynamics.

Sustaining Rwanda’s HIV response after elimination of PEPFAR funding: a modeling analysis

INTRODUCTION: HIV prevention and treatment supported by the United States President’s Emergency Plan for AIDS Relief (PEPFAR) have saved millions of lives. Rwanda is among the most successful countries around the world in achieving global targets with PEPFAR support. However, abrupt funding uncertainty around PEPFAR raises concerns about continued HIV epidemic control. We projected the impact of the Government of Rwanda’s (GoR’s) capacity to offset the elimination of PEPFAR funding on adult HIV epidemic and care continuum outcomes over 10 years. METHODS: Using an HIV policy model calibrated to Rwanda, we assessed: capacity to sustain HIV services at 50% (with no capacity by GoR to cover the PEPFAR funding gap), 75%, 90%, and 100% (with full capacity by GoR to cover the PEPFAR funding gap). Scenarios were operationalized by reducing the number on antiretroviral therapy (ART), with immediate ART discontinuation and proportional decreases in HIV diagnosis, ART initiation, and care re- engagement. We projected HIV epidemic outcomes (HIV prevalence, HIV incidence, number with HIV, new HIV infections, deaths) and care continuum outcomes (percent diagnosed, percent on ART among those diagnosed, percent virally suppressed among those on ART). We calculated differences in projected outcomes for partial or no capacity versus full capacity. Secondary analyses assessed delayed coverage capacity by 1 and 3 years. RESULTS: Compared to full capacity at 10 years, the model projected a 13.9%–38.7% increase in HIV prevalence and 69.0%–246.7% increase in HIV incidence across coverage capacity scenarios. This translated to 29,000–64,000 additional adults with HIV and 20,000–92,000 cumulative new adult HIV infections. Cumulative projected deaths increased by 10,000–51,200. The model projected continual reductions in percent diagnosed at 10 years; percent virally suppressed among those on ART was similar across scenarios. Higher, and more delayed, coverage capacity had projected outcomes similar to lower, and less delayed, coverage capacity. CONCLUSIONS: Even in countries like Rwanda that have achieved epidemic control, abrupt and persistent elimination of PEPFAR funding could drastically reverse critical gains. Evidence quantifying the consequences of different capacities to sustain HIV services underscores the high stakes of rapid and sufficient action.

Brief communication: reasons for non-adherence of co-trimoxazole prophylaxis therapy among people living with HIV in a resource-limited setting, Northern Ethiopia

This study aimed to assess the prevalence and reasons for nonadherence to cotrimoxazole prophylaxis therapy. A cross-sectional study was conducted among people living with HIV attending Ayder Comprehensive Specialized Hospital. Data were collected through interviews and reviews of medical records. Binary logistic regression was employed to analyze factors associated with CPT nonadherence. Approximately two-thirds (65.5%) of the participants were non-adherent to co-trimoxazole prophylaxis therapy. The main reasons for non-adherence were side effects, pill fatigue and forgetfulness. Strategies to improve adherence to co-trimoxazole prophylaxis therapy should focus on the combined patient, clinical and medication related issues of people living with HIV.

Probability of vertical HIV transmission: A systematic review and meta-regression

BACKGROUND: Eliminating HIV vertical transmission (VT) and is a global priority. Estimates of paediatric HIV infections are commonly derived through mathematical models relying on rates of VT stratified by maternal immunological and treatment status from literature, namely the UNAIDS-supported Spectrum AIDS Impact Module (Spectrum-AIM) to assess progress towards eliminating VT. Default VT probabilities were last updated in 2018, since then there have been substantial changes to service delivery and ART regimens. METHODS: We aimed to (1) update the systematic review of VT probabilities by maternal status compatible with Spectrum-AIM, (2) conduct a meta-regression to systematically pool studies to estimate VT probabilities with statistical uncertainty, and (3) assess determinants of VT, including maternal viral load. We searched PubMed, Embase, Global Health Database, WHO Global Index Medicus, CINAHL Complete, and Cochrane CENTRAL for peer-reviewed articles in English from all geographic regions with data on VT from randomized controlled trials, cohort studies, or observational studies. We excluded sources that did not stratify VT by maternal treatment or immunological status. We fit four meta-regression models to produce VT probability estimates compatible with stratifications used in Spectrum-AIM and assessed how updated VT probabilities estimated new paediatric infections compared to default parameters in Spectrum-AIM. We conducted subgroup analyses to assess how study inclusion affected model estimates. Finally, we fit a meta-regression model to assess ART class and initiation timing on viral load suppression at delivery. FINDINGS: The updated systematic review identified 24 new studies published between January 2018 and February 2024. Combined with previous review data, 110 studies were included in the meta-regression analysis. Estimates were broadly consistent with previous reviews. For women not receiving PMTCT, the odds of perinatal transmission decreased by 0.20 (0.16–0.25) for each 100 mm (3) increase in median CD4 of the study population. Among women on ART during pregnancy, each additional week on ART before delivery reduced the odds of VT by 5.6% (4.3%–6.8%). ART regimen class affected VT probability; the odds ratio of perinatal VT among WLHIV who initiated an INSTI-based regimen versus a NNRTI-based regimen 20 weeks before delivery was 0.355 (0.140–0.898). However, this effect was confounded by study region. Viral load suppression at delivery was significantly lower among women who started ART late during pregnancy (p=0.02), but did not significantly differ by ART class (p>0.05). INTERPRETATION: Vertical transmission rates vary substantially according to maternal immunological stage, prophylactic regimen, and timing of treatment initiation. Time of initiation on ART before delivery was strongly associated with viral load suppression at delivery. Our estimates and their uncertainty can be used in Spectrum-AIM to produce estimates of paediatric incidence to inform funding and monitor progress towards eliminating VT. FUNDING: National Institutes of Health, UNAIDS, and the Medical Research Council

Assessment of the impact of HIV infection on the hypothalamic-pituitary-ovarian axis and pubertal development among adolescent girls at a tertiary centre in Zimbabwe: a cross-sectional study

BACKGROUND: Proper planning of reproductive health needs for HIV-infected adolescents requires a clear understanding of the effects of HIV infection on adolescents’ pubertal development. OBJECTIVE: To assess the effects of HIV infection on the hypothalamic-pituitary-ovarian (HPO) axis, ovarian reserve and pubertal development in adolescent girls at a tertiary hospital in Zimbabwe. METHODS: This was a cross-sectional survey of HIV-infected adolescent girls aged 10–19 years, with available CD4 + count results at a tertiary hospital in Zimbabwe. Consecutive sampling was used to select study participants. Pubertal milestones were assessed using the age of menarche and Tanner stage for breast and pubic hair development. Growth was assessed using World Health Organisation growth charts. The HPO axis was evaluated by measuring serum follicular stimulating hormone (FSH), luteinising hormone (LH) and estradiol. The ovarian reserve was assessed in adolescents above 18 years of age by measuring the serum anti-mullein hormone (AMH) levels. Data were analysed in STATA version 13.0, and the results are presented as mean (SD) or median (quartiles) and proportions, as appropriate. RESULTS: One hundred and one (101) HIV-infected adolescents were recruited for the study. Menarche, thelarche and pubarche were delayed in 15.9%, 28.6% and 46.8% of the adolescents, respectively. A total of 59.4% had moderate to severe stunting, and 53.5% were either overweight or obese. Most participants had normal serum FSH, LH, and estradiol levels, and there was no association between these hormone levels and growth indicators. The serum AMH levels were reduced in 24.1% of the adolescents. There were no significant differences in the hormonal levels and pubertal development between the WHO CD4 classes. CONCLUSION: HIV infection is associated with stunted growth and delayed sexual maturation with an intact HPO axis in the majority of adolescents. There was no association between growth indicators and FSH and LH levels. The degree of HIV immunosuppression had no significant impact on the HPO axis and pubertal development. A larger study is needed to assess the impact of HIV infection on ovarian reserve. TRIAL REGISTRATION: : This protocol was approved by the Medical Research Council of Zimbabwe (MRCZ) (reference number MRCZ/A/1730).

Unsuccessful Direct Acting Antiviral Hepatitis C Treatment Among People With HIV: Findings From an International Cohort

International audience; BACKGROUND: Historically, hepatitis C virus (HCV) was difficult to treat among people with HIV. However, treatment with direct-acting antivirals (DAAs) results in 90%-95% of people being cured. There is a need to understand why a proportion of people are not cured. We aimed to examine characteristics that may indicate an increased probability of unsuccessful DAA HCV treatment. METHODS: Data were from the International Collaboration on Hepatitis C Elimination in HIV Cohorts. People who commenced DAA HCV treatment between 2014 and 2019 were included. Unsuccessful treatment was defined as a positive HCV RNA test at a person's first RNA test at least 4 weeks (SVR4+) following the end of treatment. Multivariable mixed-effects logistic regression was used to examine characteristics associated with unsuccessful treatment. RESULTS: Of 4468 people who commenced DAA treatment, 4098 (91.7%) had an SVR test 4+ weeks following the end of treatment, 207 (5%) of whom were unsuccessfully treated. Compared to a CD4+ cell count &gt; 500 cells/mm(3), cell counts &lt; 200 (aOR 1.81, 95%CI 1.00-3.29) and between 200 and 349 (aOR 1.95, 95%CI 1.30-2.93) were associated with increased odds of unsuccessful treatment. Among 1921 people with data on injection drug use in the 12 months prior to treatment, there was some evidence that recent injection drug use was associated with increased odds of unsuccessful treatment; however, this was not statistically significant (aOR 1.67, 95%CI 0.99-2.82). CONCLUSIONS: The overwhelming majority of people were successfully treated for HCV. Overall, 5% of those with an SVR4+ test were unsuccessfully treated; this was more likely among people with evidence of immunodeficiency and those who reported recently injecting drugs.

Linkage of HIV treatment and population-based surveillance records in rural South Africa: the AHRI Unified Data Platform (AUDP)

Background Integrating HIV clinical records with population-based surveillance data allows the study of health care seeking behaviours, access to care, and predictors of patient outcomes. We implemented a graph-based record linkage algorithm to deduplicate and link HIV clinical and population-based surveillance records in an HIV-endemic setting in rural South Africa. Methods We linked four data sources to create the Africa Health Research Institute (AHRI) Unified Data Platform: AHRI’s Health and Demographic Surveillance System (HDSS), AHRI Clinic and Hospital Information System (AHRILink), National Health Laboratory Service (NHLS), and Three Integrated Electronic Registers (TIER.Net) HIV care and treatment records. HDSS data were collected between January 1, 2000, and July 31, 2024, through repeated household surveys of over 140,000 individuals. Clinical and laboratory data were obtained for one hospital and 17 clinics in Hlabisa, KwaZulu-Natal, covering the HDSS surveillance area. We implemented a probabilistic record linkage algorithm trained and validated on a subset of records with national identity numbers. We assessed linkage accuracy, computed descriptive statistics for the linked database, and estimated the HIV care cascade for this population. Results A total of 986,832 records were successfully linked across the four databases, achieving a sensitivity of 92.7% and a positive predictive value of 96.5% (F-score=0.95). The average number of records (standard deviation (SD)) in TIER.Net, HDSS, AHRILink and NHLS were 1.18 (0.44),1.05 (0.23),1.13 (0.40), and 5.21 (4.24), respectively. The linked data indicated that 12,293 HDSS resident adults (≥15 years) were living with HIV at some point during the 2022 and 2024 surveillance rounds. Of these, 10,622 (86.4%) had ever sought HIV care in the public sector, of whom 10,492 (98.8%) had ever started ART and 7,065 (66.5%) were currently on ART, of whom 6,301 (89.2%) were virally suppressed(viral load<200 copies/mL). Conclusion HIV care and population surveillance records from four data sources were deduplicated and linked with high accuracy, revealing persistent gaps in retention in care and viral suppression in an HIV-endemic region in rural South Africa. The AHRI Unified Data Platform offers the potential to deepen our understanding of HIV epidemiology in a well-described population and to improve services for HIV. Trial registration Not applicable.

Single cell multiomic analysis of the impact of Delta-9-tetrahydrocannabinol on HIV infected CD4 T cells

Cannabis use is prevalent among individuals living with HIV in the United States, but the impact of cannabis exposure on the reservoir of latently infected cells that persists during antiretroviral therapy (ART) remains unclear. To address this gap, we analyzed the effect of Δ−9-tetrahydrocannabinol (THC) on primary CD4 T cells that were latently infected with HIV. We found that THC had no detectable effect on baseline or latency reversing agent (LRA) stimulated HIV expression, or on expression of an activation marker (CD38). However, using an integrated multiomic single-cell analysis of genome-wide chromatin accessibility and gene expression, we observed altered expression of several hundred genes in HIV infected CD4 T cells after THC exposure, including transcriptional downregulation of genes involved in protein translation and antiviral pathways, indicating that THC suppresses innate immune activation in infected cells. Additionally, chromatin accessibility analysis demonstrated upregulated chromatin binding activity for the transcriptional regulator CTCF, and reduced activity for members of the ETS transcription factor family in infected cells after THC exposure. These findings provide insights into the mechanisms by which cannabis use could influence the persistence of HIV within cellular reservoirs and the molecular phenotype of latently infected cells. Further elucidation of the underlying mechanisms involved in THC-mediated changes to HIV infected cells, will lead to an improved understanding of the impact of cannabis use on the HIV reservoir.

Prevalence of hepatitis B, hepatitis C, and tuberculosis among people living with HIV in Iran: a systematic review and meta-analysis

The prevalence of HBV, HCV, and TB was high among Iranian PLWH The prevalence was higher among at-risk groups such as PWIDs and homeless people The prevalence of HCV was more than HBV and TB among PLWH Background Up to now several studies estimate the prevalence of HBV, HCV, and TB among people living with HIV (PLWH) in Iran; however, their results are inconsistent. This study aimed to estimate the overall prevalence of HBV, HVC, and TB among Iranian PLWH. Methods In this systematic review and meta-analysis six databases including Medline, Web of Science, Scopus, MagIran, Scientific Information Database (SID), and Barakat Knowledge network system were searched up to October 2023 with no language restriction. All studies estimated the prevalence of HBV, HCV, and TB among PLWH in Iran were included. The random-effects model was used to report the study estimates. Results were reported at a 95% confidence interval (CI). Results Out of 1050 retrieved references, 58 articles met the eligibility criteria. Overall among PLWH, HBV prevalence was 13.0% (95% CI: 11.0, 15.0), HCV prevalence was 54% (95% CI: 45.0, 64.0), and TB prevalence was 19% (95% CI: 13.0, 24.0). The results from multivariate meta-regression analysis showed no statistically significant association between HBV and TB prevalence with the year of study, quality of studies, age, gender, and persons who inject drugs (PWID). HCV prevalence was significantly associated with PWID. Conclusion We found HBV, HCV, and TB infections are common among PLWH in Iran and required to be screened and treated with effective and timely services.

Enhanced Yield and Gentle Purification of HIV for Cryo-Electron Tomography Analysis of Virion Maturation

HIV is a lentivirus characterized by the formation of its mature core. Visualization and structural examination of HIV requires purification of virions to high concentrations. The yield and integrity of these virions are crucial for ensuring a uniform representation of all viral particles in subsequent analyses. In this study, we present a method for purification of HIV virions which minimizes forces applied to virions while maximizing the efficiency of collection. This method allows us to capture between 1,000 and 5,000 HIV virions released from individual HEK293 cells after transfection with the NL4.3 HIV backbone, a 10 fold advantage over other methods. We utilized this approach to investigate HIV core formation from several constructs: pNL4-3(RT:D (185) A&D (186) A) with an inactive reverse transcriptase, NL4.3(IN: V (165) A&R (166) A) with a type-II integrase mutation, and NL4.3(Ѱ: Δ(105-278)&Δ(301-332)) featuring an edited Ѱ packaging signal. Notably, virions from NL4.3(Ѱ: Δ(105-278)&Δ(301-332)) displayed a mixed population, comprising immature virions, empty cores, and cores with detectable internal density. Conversely, virions derived from NL4.3(IN: V (165) A&R (166) A) exhibited a type II integrase mutant phenotype characterized by empty cores and RNP density localized around the cores, consistent with previous studies. In contrast, virions released from pNL4-3(RT:D (185) A&D (186) A) displayed mature cores containing detectable RNP density. We suggest that the purification methods developed in this study can significantly facilitate the characterization of enveloped viruses.

Seroprevalence and Demographic Distribution of Blood-Borne Viral Infections among Hemodialysis Patients in Sudan: A Multicenter Study from Conflict-Affected Khartoum State

Background Blood-borne viral infections pose considerable health challenges to patients undergoing hemodialysis. This multicenter study evaluated the prevalence and demographic distribution of hepatitis B, hepatitis C, and HIV infections among hemodialysis patients. Methods This retrospective cross-sectional study included 940 hemodialysis patients from fifteen facilities in Khartoum State, Sudan from July 2025 to November 2025. The outcomes of the serological tests for hepatitis B surface antigen (HBsAg), anti-hepatitis C virus (anti-HCV) antibodies, and anti-HIV antibodies, conducted via standard enzyme-linked immunosorbent assay (ELISA) test, were acquired from the Public Health Laboratory (PHL), Khartoum State. Data was analyzed with R software (version 4.4.2). Results The overall prevalence rates were 7.1% for HBsAg, 12.2% for anti-HCV, and 0.6% for HIV. The prevalence of hepatitis C virus (HCV) exhibited significant variability across dialysis centers ( p <0.001), with higher rates observed in certain facilities. No notable correlations between infection status and age or gender were detected. Co-infections were rare. Conclusions Blood-borne viral infections persist as a significant issue in Sudanese hemodialysis facilities, emphasizing the imperative for consistent infection control measures such as routine blood screening for transfusion. Targeted interventions at high-prevalence centers are essential for decreasing transmission and enhancing patient outcomes. Trial registration Not applicable.

Uptake and timing of viral load testing and frequency of viraemic episodes during pregnancy in South Africa

BACKGROUND: Repeated monitoring of viral load (VL) among pregnant women living with HIV (WLWH) is critical in vertical transmission prevention. For women who are newly diagnosed with HIV during pregnancy, a subsequent VL is recommended three months after ART initiation, and for all women living with HIV, follow-up VL is required every six months throughout pregnancy and breastfeeding [2]. Here, we describe the uptake and timing of VL testing and frequency and distribution of viraemic episodes during pregnancy. METHODS: We linked prospective cohort data from WLWH whose infants were born at Rahima Moosa Mother and Child Hospital (RMMCH) in Johannesburg, South Africa (2013-2018) to laboratory data from the National Health Laboratory Services national HIV cohort. We report the uptake and timing of VL testing, and frequency of viremia and viral suppression. We also explore factors associated with having at least one or more VL test. RESULTS: Data from 4,064 women with known dates of entry into antenatal care and delivery during the study period were analysed. Overall, less than half (46%) completed VL testing during pregnancy. Most VL were conducted during the third trimester (67%). Only 5% (n=100) were during the first trimester and 11% within 7 days of delivery. Three-quarters of tests during pregnancy indicated viral suppression (VL <400 copies/mL), 7% viraemic (VL 400-1000 copies/mL), and 19% high grade viraemia (VL >1000 copies/mL). We found that being older (≥35) and being engaged in HIV care prior to pregnancy were significantly associated with VL testing during pregnancy. CONCLUSION: With less than half of pregnant women living with HIV in this study having a VL measure during their pregnancy, and VL testing occurring late in pregnancy, this study highlights critical gaps in providing quality HIV care to women and prevention of vertical transmission.

PSGL-1 excludes HIV Env from virion surface through spatial hindrance involving structural folding of the decameric repeats (DR)

P-selectin glycoprotein ligand-1 (PSGL-1), a mucin-like surface glycoprotein, is primarily expressed on lymphoid and myeloid cells. PSGL-1 has recently been identified as an HIV restriction factor, blocking HIV infectivity mainly through virion incorporation that sterically hinders virion attachment to target cells. PSGL-1 also inhibits HIV Env incorporation into virions. However, the molecular mechanisms of PSGL-1-mediated Env exclusion remained unclear. Here, we investigated the role of PSGL-1’s extracellular (EC) and intracellular (IC) domains in Env exclusion. We demonstrate that both EC and IC are important for Env exclusion; when EC was deleted, PSGL-1 completely lost its ability to inhibit Env incorporation, whereas when IC was deleted, PSGL-1 partially lost this activity. In addition, when the decameric repeats (DR) were deleted from EC, PSGL-1 also lost its ability to inhibit Env incorporation. Sequential DR deletion mutagenesis further demonstrated that a minimum of 9 DRs is necessary for Env exclusion. Molecular modeling of the DR structure revealed that PSGL-1 mutants with 7 or fewer DRs pose as an extended “rod-like” structure, whereas those with 9 or more DRs collapse into a “coil-like” structure that spatially excludes Env. Our studies suggest a model in which Env exclusion involves Gag-mediated PSGL-1 targeting to the virion assembly site where DR-mediated spatial exclusion blocks Env incorporation.

Targeting monocytic Occludin impairs transendothelial migration and HIV neuroinvasion

Transmigration of circulating monocytes from the bloodstream to tissues represents an early hallmark of inflammation. This process plays a pivotal role during viral neuroinvasion, encephalitis, and HIV-associated neurocognitive disorders. How monocytes locally unzip endothelial tight junction-associated proteins (TJAPs), without perturbing impermeability, to reach the central nervous system remains poorly understood. Here, we show that human circulating monocytes express the TJAP Occludin (OCLN) to promote transmigration through endothelial cells. We found that human monocytic OCLN (hmOCLN) clusters at monocyte-endothelium interface, while modulation of hmOCLN expression significantly impacts monocyte transmigration. Furthermore, we designed OCLN-derived peptides targeting its extracellular loops (EL) and show that transmigration of treated monocytes is inhibited in vitro and in zebrafish embryos, while preserving vascular integrity. Monocyte transmigration toward the brain is an important process for HIV neuroinvasion and we found that the OCLN-derived peptides significantly inhibit HIV dissemination to cerebral organoids. In conclusion, our study identifies an important role for monocytic OCLN during transmigration and provides a proof-of-concept for the development of mitigation strategies to prevent monocyte infiltration and viral neuroinvasion. This study highlights the contribution of Occludin (OCLN) expressed by monocytes during transmigration. Targeting monocytic Occludin prevents monocyte transmigration, which is sufficient for potent inhibition of HIV neuroinvasion. Silencing monocytic OCLN decreases transmigration through endothelial cells, while its re-expression increases transmigration. Using a short peptide from the extracellular loop 2 of OCLN prevents transmigration of monocytes in vitro and in zebrafish. HIV-carrying monocyte transmigration is critical for HIV neuroinvasion of cerebral organoids. HIV neuroinvasion is abrogated by OCLN-derived peptides. This study highlights the contribution of Occludin (OCLN) expressed by monocytes during transmigration. Targeting monocytic Occludin prevents monocyte transmigration, which is sufficient for potent inhibition of HIV neuroinvasion.

The impact of western versus agrarian diet consumption on gut microbiome composition and immune dysfunction in people living with HIV in rural and urban Zimbabwe

BACKGROUND: People living with HIV (PLWH) suffer from chronic inflammation even with effective antiretroviral therapy (ART). A high-fat, low-fiber western-type diet has been linked with inflammation, in part through gut microbiome changes. In sub-Saharan Africa (SSA), a region with high HIV burden, urbanization has been linked with a shift from traditional agrarian towards westernized diets, and with changes in food security. To explore the relationship between diet, inflammation, and the gut microbiome in PLWH, we enrolled 1) ART Naïve PLWH who provided samples before and after 24 weeks of ART, 2) PLWH on ART at both timepoints and 3) HIV-seronegative controls. Individuals were evenly recruited from rural and urban Zimbabwe (locations were 145 kilometers/90 miles apart). Using a food frequency survey designed to measure intake of agrarian versus western-type food items in Zimbabwe, we determined how diet differs with urbanization, HIV-infection and treatment, and is related to inflammation and the gut microbiome. RESULTS: Individuals residing in a rural area of Zimbabwe less frequently consumed high-fat, low-fiber western type food items and had lower consumption of diverse food items overall, except for sadza-a subsistence staple-processed from home-grown grains. Consumption of a more western-type diet correlated with lower CD4+ T cell percentage in untreated and treated PLWH and with increased T cell exhaustion in PLWH on ART. PLWH on ART at time of enrollment also consumed diverse food items at a lower frequency and more often were underweight. Low food consumption correlated with muted improvements in T cell exhaustion after 24 weeks of ART. Individuals residing in the rural area had more Prevotella -rich/ Bacteroides -poor microbiomes, but this did was not significantly mediated by diet. western diet consumption reduced the diversity of carbohydrate substrate degradation capabilities in the microbiome, based on predictions made using metagenomic polysaccharide utilization loci. CONCLUSIONS: Taken together, this work supports that consumption of more high-fat/low-fiber type food items has the potential to exacerbate HIV pathogenesis in a sub-Saharan setting where HIV burden is high and reinforces the importance of nutritional support for promoting immunologic response to ART in PLWH in SSA.

Late diagnosis of respiratory syncytial virus and influenza co-infection during coronavirus disease 2019 pandemic: a case report

Background Respiratory syncytial virus (RSV)-induced disease is one of the important causes of flu-like illness in older adults and can cause serious disease in those who are at high-risk medical conditions. During coronavirus disease 2019 (COVID-19) pandemic, because of overlapping symptoms of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) infection with other respiratory infections, diagnosing diseases based on clinical and radiological findings was challenging and could cause misdiagnosis. Case presentation An 87-year-old Persian man was admitted to the hospital due to loss of consciousness, respiratory distress, tachypnea, and oliguria. He had previously hospitalized because of cough, fever, loss of appetite, and fatigue. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) polymerase chain reaction (PCR) test was performed which was negative; however, based on ground glass opacity on his chest computed tomography (CT) scan and being on the outbreak of COVID-19, he fulfilled case definition of COVID-19; therefore, he received protocol’s treatment (remdesivir) for COVID-19 and relatively recovered and discharged. In our center, we requested brain and chest CT scans, blood tests, and multiplex PCR. Multiplex PCR revealed co-infection of influenza virus and RSV. Although we had started pneumonia and sepsis treatment, old age, weak immune system and the delay in initiation of right antibiotic and antivirus therapy altogether led him to die. Conclusion As a takeaway lesson of this case report, it is necessary to pay attention to viruses that show similar symptoms during future specific virus pandemics, especially in patients with old age and weak immune systems.

Facile, Rapid, and Low-Cost Detection for Influenza Viruses and Respiratory Syncytial Virus Based on a Catalytic DNA Assembly Circuit

[Image: see text] Influenza viruses and respiratory syncytial virus (RSV) have contributed to severe respiratory infections, causing huge economic and healthcare burdens. To achieve rapid and precise detection of influenza viruses and RSV, we proposed a catalytic hairpin assembly (CHA) combined with the lateral flow immunoassay (CHA–LFIA) detection method. The presence of the target RNA triggers the initiation of CHA circuits. H1/H2 complexes, the amplified signal products, which were labeled with digoxin and biotin, were detected with a highly sensitive lateral flow immunoassay system. The sensitivity of the CHA–LFIA system to influenza A and B viruses and RSV reached up to 1, 1, and 5 pM, respectively. In addition, this method exhibited excellent capability for differentiating between target RNA and base-mismatched RNA. The results demonstrated that an enzyme-free, rapid, highly sensitive, and specific method had been developed to detect influenza A and B viruses and RSV.

Parental preference for influenza vaccine for children in China: a discrete choice experiment

OBJECTIVES: To investigate what factors affect parents’ influenza vaccination preference for their children and whether there exists preference heterogeneity among respondents in China. DESIGN: Cross-sectional study. A discrete choice experiment was conducted. Five attributes were identified based on literature review and qualitative interviews, including protection rate, duration of vaccine-induced protection, risk of serious side effects, location of manufacturer and out-of-pocket cost. SETTING: Multistage sampling design was used. According to geographical location and the level of economic development, 10 provinces in China were selected, and the survey was conducted at community healthcare centres or stations. PARTICIPANTS: Parents with at least one child aged between 6 months and 5 years old were recruited and the survey was conducted via a face-to-face interview in 2019. In total, 600 parents completed the survey, and 449 who passed the internal consistency test were included in the main analysis. MAIN OUTCOMES AND MEASURES: A mixed logit model was used to estimate factors affecting parents’ preference to vaccinate their children. In addition, sociodemographic characteristics were included to explore the preference heterogeneity. RESULTS: In general, respondents preferred to vaccinate their children. All attributes were statistically significant and among them, the risk of severe side effects was the most important attribute, followed by the protection rate and duration of vaccine-induced protection. Contrary to our initial expectation, respondents have a stronger preference for the domestic than the imported vaccine. Some preference heterogeneity among parents was also found and in particular, parents who were older, or highly educated placed a higher weight on a higher protection rate. CONCLUSION: Vaccination safety and vaccine effectiveness are the two most important characteristics that influenced parents’ decision to vaccinate against influenza for their children in China. Results from this study will facilitate future policy implementations to improve vaccination uptake rates.

Stabilization of H5 highly pathogenic avian influenza hemagglutinin improves vaccine-elicited neutralizing antibody responses

Transmission of highly pathogenic avian influenza from H5 clade 2.3.4.4b has expanded in recent years to infect large populations of birds and mammals, heightening the risk of a human pandemic. Influenza viruses adapted to transmission in birds and some other animals tend to have a less stable hemagglutinin (HA) than seasonal influenza viruses, enabling membrane fusion at comparatively high pH levels. Here, we combine five mutations within H5 HA that dramatically increase its melting temperature and promote stable closure of the HA trimer. Structural analysis by cryo-electron microscopy revealed that the stabilizing mutations create several new hydrophobic interactions, while maintaining local HA structure. We found that vaccinating mice with stabilized H5 HA immunogens resulted in higher hemagglutination inhibition and neutralization titers than non-stabilized comparators. Epitope mapping of vaccine-elicited polyclonal antibody responses using negative stain electron microscopy and deep mutational scanning showed that site E on the side of the HA receptor binding domain was immunodominant across all groups; however, the stabilized immunogens shifted responses toward the receptor binding site (RBS), eliciting a higher proportion of neutralizing antibodies. These findings highlight that H5 HA-stabilizing mutations enhance the quality of antibody responses across different vaccine formats, underscoring their potential to improve pandemic preparedness vaccines targeting viruses from this widely circulating clade. ONE SENTENCE SUMMARY: Vaccinating with stabilized H5 HA increases the proportion of humoral responses against neutralizing RBS epitopes.

Estradiol Mediates Greater Germinal Center Responses to Influenza Vaccination in Female than Male Mice

Adult females of reproductive ages develop greater antibody responses to inactivated influenza vaccine (IIV) than males. How sex, age, and sex steroid changes impact B cells and durability of IIV-induced immunity and protection over 4-months post-vaccination (mpv) was analyzed. Vaccinated adult females had greater germinal center (GC) B cell and plasmablast frequencies in lymphoid tissues, higher neutralizing antibody responses 1-4 mpv, and better protection against live H1N1 challenge than adult males. Aged mice, regardless of sex, had reduced B cell frequencies, less durable antibody responses, and inferior protection after challenge than adult mice, which correlated with diminished estradiol among aged females. To confirm that greater IIV-induced immunity was caused by sex hormones, four core genotype (FCG) mice were used, in which the testes determining gene, Sry , was deleted from ChrY and transferred to Chr3, to separate gonadal sex (i.e., ovaries or testes) from sex chromosome complement (i.e., XX or XY complement). Vaccinated, gonadal female FCG mice (XXF and XYF) had greater numbers of B cells, higher antiviral antibody titers, and reduced pulmonary virus titers following live H1N1 challenge than gonadal FCG males (XYM and XXM). To establish that lower estradiol concentrations cause diminished immunity, adult and aged females received either a placebo or estradiol replacement therapy prior to IIV. Estradiol replacement significantly increased IIV-induced antibody responses and reduced morbidity after the H1N1 challenge among aged females. These data highlight that estradiol is a targetable mechanism mediating greater humoral immunity following vaccination among adult females. IMPORTANCE: Females of reproductive ages develop greater antibody responses to influenza vaccines than males. We hypothesized that female-biased immunity and protection against influenza was mediated by estradiol signaling in B cells. Using diverse mouse models ranging from advanced age mice to transgenic mice that separate sex steroids from sex chromosome complement, those mice with greater concentrations of estradiol consistently had greater numbers of antibody producing B cells in lymphoid tissue, higher antiviral antibody titers, and greater protection against live influenza virus challenge. Treatment of aged female mice with estradiol enhanced vaccine-induced immunity and protection against disease, suggesting that estradiol signaling in B cells is critical for improved vaccine outcomes in females.

Translational Modeling of Chloroquine and Hydroxychloroquine Dosimetry in Human Airways for Treating Viral Respiratory Infections

Purpose Chloroquine and hydroxychloroquine are effective against respiratory viruses in vitro . However, they lack antiviral efficacy upon oral administration. Translation of in vitro to in vivo exposure is necessary for understanding the disconnect between the two to develop effective therapeutic strategies. Methods We employed an in vitro ion-trapping kinetic model to predict the changes in the cytosolic and lysosomal concentrations of chloroquine and hydroxychloroquine in cell lines and primary human airway cultures. A physiologically based pharmacokinetic model with detailed respiratory physiology was used to predict regional airway exposure and optimize dosing regimens. Results At their reported in vitro effective concentrations in cell lines, chloroquine and hydroxychloroquine cause a significant increase in their cytosolic and lysosomal concentrations by altering the lysosomal pH. Higher concentrations of the compounds are required to achieve similar levels of cytosolic and lysosomal changes in primary human airway cells in vitro . The predicted cellular and lysosomal concentrations in the respiratory tract for in vivo oral doses are lower than the in vitro effective levels. Pulmonary administration of aerosolized chloroquine or hydroxychloroquine is predicted to achieve high bound in vitro -effective concentrations in the respiratory tract, with low systemic exposure. Achieving effective cytosolic concentrations for activating immunomodulatory effects and adequate lysosomal levels for inhibiting viral replication could be key drivers for treating viral respiratory infections. Conclusion Our analysis provides a framework for extrapolating in vitro effective concentrations of chloroquine and hydroxychloroquine to in vivo dosing regimens for treating viral respiratory infections. Graphical abstract

Enhanced passive safety surveillance of a quadrivalent inactivated split virion influenza vaccine in Finland during the influenza season 2020/21

Background The European Medicines Agency (EMA) requires enhanced safety surveillance to be conducted for annual seasonal influenza vaccines with the aim of rapidly detecting any potential new safety concerns before the peak immunisation period of the vaccine in any given year. The aim of this study was to detect any clinically significant change in the frequency or severity of expected reactogenicity of the quadrivalent inactivated split-virion influenza vaccine (IIV4) during routine immunisation in Finland for the 2020/21 season. The primary objective was to investigate the frequency of suspected adverse drug reactions (ADRs) occurring within 7 days following vaccination. Methods Enhanced passive safety surveillance of individuals vaccinated with IIV4 was conducted from October 9, 2020 to November 30, 2020 across seven sites in Finland. The vaccinee reporting rate and ADR reporting rate were calculated and compared with known or expected safety data in order to identify any clinically significant changes. Results Data were collected from 1008 individuals with 29 vaccinees reporting 82 suspected ADRs. Of these, 28 people reported 79 suspected ADRs within 7 days following vaccination, corresponding to a vaccinee reporting rate of 2.78% (95% CI: 1.85, 3.99) (ADR reporting rate, 7.84% [95% CI: 6.25, 9.67%]). The most frequently reported ADRs were injection site reactions (vaccination site pain, vaccination site erythema and vaccination site swelling) ( n  = 46, 2.28%), myalgia ( n  = 9, 0.89%) and headache ( n  = 8, 0.79%). No serious suspected adverse events were reported at any point post-vaccination and ADR reporting rates were in general lower compared to those reported for IIV4 in the 2019/20 surveillance study. Conclusion No clinically significant changes in what is known or expected for IIV4 were reported for the 2020/21 season which supports the safety profile of this vaccine and will help maintain public confidence in influenza vaccination.

Structural and functional analysis of physicochemical interactions of the hemagglutinin of influenza A viruses : evolutionary constraints and perspectives for the development of broad-spectrum therapeutic antivirals and universal vaccines;Analyse structurale et fonctionnelle des interactions physico-chimiques de l'hémagglutinine des virus de la grippe de type A : contraintes évolutives et perspectives pour le développement d'antiviraux thérapeutiques à large spectre et de vaccins universels

Influenza A viruses (IAVs) cause yearly epidemics and occasional pandemics that represent a global health threat of major concern. Virus entry within cells is carried out by the hemagglutinin (HA), a trimeric surface glycoprotein of which each protomer is constituted of two sub-units (HA1, HA2). The HA binds its receptor at the cell surface and induces the endocytosis of the particle. The exposure of the HA to acidic pH upon maturation of the endosome triggers a drastic conformational change (CC), which enables the fusion between endosomal and viral membranes. The HA is intrinsically extremely variable, it has been classified in subtypes, clades and groups. Based on 269 trimeric prefusion structures, we computed six interaction types, including electrostatic interactions, along with their distribution intra- and inter-protomer (ITA-p/ITR-p) and intra- and inter-sub-unit. We revealed distinct clade-dependent physicochemical profiles of interaction networks. These different profiles of networks enable the folding of highly similar trimeric metastable structures carrying identical function and thus suggested that HA evolution is constrained accordingly and that the function may be carried out by different CC molecular drivers. We then computed the conserved interactions across HA classifications. We characterized a buried complex consisting of two connected ITR-p salt-bridges, located at a crucial area for the CC. The first is the ITR-p HA2-HA2 (E742-R762) salt-bridge, strictly conserved among all HAs except clade 7 HAs and H17. The second is the ITR-p HA1-HA2 (E1041-R762) salt-bridge, conserved among almost every subtype of group 1 HAs, whereas the ITR-p HA2-HA2 (R762-E812) salt-bridge is strictly conserved among clade 3 HAs. The functional importance of the complex was analyzed through mutagenesis, fusion assays and reverse genetics in two viruses from clade 1 (H1) and 3 (H3), which displayed distinct physicochemical profiles. Mutations were tolerated in the H3 context, in vitro, whereas in the H1 context any mutation disrupting the complex resulted in a loss of function and unviable mutant viruses. The results therefore indicated that HA evolution is constrained according to the underlying physicochemical profile of interaction networks. The strict conservation of the complex in structures sharing the H1 physicochemical profile and several other factors, that include the identical geometric arrangement of the complex in space, suggested that the essential nature of the complex in H1 is likely to be extrapolated to other HAs sharing the same physicochemical profile. Finally, we tracked the modulation of interactions during the CC based of structures of fusion intermediates (H3). We revealed various molecular drivers, especially the disruption of highly conserved ITR-p HA2-HA2 salt-bridges located at the membrane-proximal domain, which might be key in the context of the clade 3 physicochemical profile. Overall, assessments of the functional importance of the conserved ITR-p salt-bridges complex in different environments highlighted that underlying clade-dependent physicochemical profile of interaction networks influences CC molecular drivers and how function is performed. These profiles influence the mutational tolerance of specific residues and thereby limit HA evolution. Our results demonstrated that comprehensive analysis of interaction networks in the context of HA variability enables to better understand its evolution as well as to take advantage of the variability to identify crucial conserved interactions. Most importantly, we highlighted the value of combining interaction conservation assessments and functional validation to provide insightful data for the research of broad and non-escapable therapeutic targets and vaccine epitopes. ; Les virus de la grippe provoquent chaque année des épidémies et parfois des pandémies qui représentent une menace mondiale. L'entrée du virus dans les cellules est assurée par l'hémagglutinine (HA), une glycoprotéine trimérique de surface dont chaque protomère est constitué de deux sous-unités (HA1, HA2). La HA se lie à son récepteur à la surface de la cellule, ce qui induit l'endocytose de la particule. L'exposition de la HA au pH acide lors de la maturation de l'endosome déclenche un changement conformationnel (CC) drastique, qui permet la fusion entre les membranes endosomale et virale. La HA est extrêmement variable, et a été classée en sous-types, clades et groupes. Sur la base de 269 structures trimériques pré-fusion, nous avons calculé six types d'interactions ainsi que leur distribution intra- et inter-protomères (ITA-p/ITR-p) et intra- et inter-sous-unités. Nous avons révélé des profils physico-chimiques de réseaux d'interactions distincts en fonction des clades. Ces réseaux permettent le repliement de structures métastables trimériques très similaires, ayant une fonction identique, ce qui suggère que l'évolution des HA est limitée en conséquence. Nous avons ensuite calculé les interactions conservées en fonction des classifications et caractérisé un complexe composé de deux ponts salins ITR-p connectés, situé dans une zone cruciale pour le CC. Le premier est le pont salin ITR-p HA2-HA2 (E742-R762), strictement conservé parmi toutes les HA sauf celles du clade 7 et H17. Le second est le pont salin ITR-p HA1-HA2 (E1041-R762), conservé parmi presque tous les sous-types de HA du groupe 1, tandis que le pont salin ITR-p HA2-HA2 (R762-E812) est strictement conservé au sein du clade 3. L'importance fonctionnelle du complexe a été caractérisée par mutagenèse, test de fusion et génétique inverse dans deux virus du clade 1 (H1) et 3 (H3), qui présentent des profils physico-chimiques distincts. Les mutations ont été tolérées dans le contexte H3, in vitro, tandis que dans le contexte H1, toute mutation perturbant le complexe a donné lieu à une perte de fonction et des virus mutants non viables. Les résultats indiquent donc que l'évolution de la HA est limitée par le profil physico-chimique des réseaux d'interactions et suggèrent différents moteurs moléculaires du CC. La conservation du complexe au sein des structures partageant le profil physico-chimique H1, et plusieurs autres facteurs dont l'arrangement géométrique identique du complexe dans l'espace, suggèrent que la nature essentielle du complexe est susceptible d'être extrapolée aux HA partageant le même profil physico-chimique. Enfin, nous avons suivi la modulation des interactions au cours du CC à partir des structures des intermédiaires de fusion (H3). Nous avons révélé divers moteurs moléculaires, notamment la rupture de nombreux ponts salins ITR-p HA2-HA2 très conservés, à la base du trimère. Dans l'ensemble, l'évaluation de l'importance fonctionnelle, dans différents contextes, du complexe de ponts salins ITR-p largement conservé a mis en évidence que le profil physico-chimique des réseaux d'interactions influence les moteurs moléculaires du CC et la façon dont la fonction est réalisée. Les profils physico-chimiques des réseaux d'interactions influencent la tolérance mutationnelle de résidus spécifiques, contraignant ainsi l'évolution de la HA. Nos résultats ont démontré que l'analyse des réseaux d'interactions dans le contexte hypervariable de la HA permet de mieux comprendre son évolution et de tirer parti de la variabilité pour identifier des interactions conservées essentielles. Enfin, nous avons souligné l'intérêt de combiner l'évaluation de la conservation des interactions et leur validation fonctionnelle afin de fournir des données bénéfiques pour la recherche de cibles thérapeutiques et d'épitopes vaccinaux larges ne pouvant muter.

Multiple independent introductions of highly pathogenic avian influenza H5 viruses during the 2020–2021 epizootic in France

International audience; During winter 2020-2021, France and other European countries were severely affected by highly pathogenic avian influenza H5 viruses of the Gs/GD/96 lineage, clade 2.3.4.4b. In total, 519 cases occurred, mainly in domestic waterfowl farms in Southwestern France. Analysis of viral genomic sequences indicated that 3 subtypes of HPAI H5 viruses were detected (H5N1, H5N3, H5N8), but most French viruses belonged to the H5N8 subtype genotype A, as Europe. Phylogenetic analyses of HPAI H5N8 viruses revealed that the French sequences were distributed in 9 genogroups, suggesting 9 independent introductions of H5N8 from wild birds, in addition to the 2 introductions of H5N1 and H5N3.

Vaccination against influenza among Lebanese health care workers in the era of coronavirus disease 2019

Background Health-care workers (HCWs) are at a higher occupational risk of contracting and transmitting influenza. Annual vaccination is an essential tool to prevent seasonal influenza infection. However, HCWs vaccine hesitancy remains a leading global health threat. This study aims to evaluate the flu vaccination coverage rates among Lebanese HCWs and to assess their knowledge, attitudes, practices, perceived barriers, and benefits toward the flu vaccine during the COVID-19 pandemic. In addition, we sought to identify the factors associated with flu vaccine uptake. Methods A cross-sectional study using an online survey was conducted in Lebanon among HCWs between 14 and 28 October 2020. Multivariable logistic regression was carried out to identify the factors associated with influenza vaccine uptake. Results A total of 560 HCWs participated in the survey of whom 72.9% were females, and 53.9% were aged between 30-49 years. Regarding Flu vaccination uptake, the rate has risen from 32.1% in 2019-2020 to 80.2% in 2020-2021 flu season. The majority of HCWs had a good knowledge level and a positive attitude toward flu vaccination. Regarding their practices, less than 50% of HCW were currently promoting the importance of getting the flu vaccine. The majority (83.3%) ranked the availability of a sufficient quantity of vaccines as the most significant barrier to flu vaccination. The main perceived flu vaccination benefits were enhancing patient safety, minimizing the viral reservoir in the population, decreasing hospital admission, and avoiding influenza and COVID-19 co-infection. The odds of influenza vaccine uptake was lower in unmarried compared to married HCWs (OR = 0.527, CI (0.284-0.978). However, HCWs having received the influenza vaccine in the previous season (OR = 6.812, CI (3.045-15.239)), those with good knowledge level (OR = 3.305, CI (1.155-9.457)), low perceived barriers (OR = 4.130, CI (1.827-9.334)) and high perceived level of the benefits (OR = 6.264, CI (2.919-13.442)) of the flu vaccination were found more prone to get the flu vaccine. Conclusion Flu vaccination uptake has increased among HCWs during the 2020-2021 flu season compared with the previous one. Continuing education as well as ensuring free, equitable, and convenient access to vaccination are still required to increase the annual flu vaccination uptake among HCWs.

IL-7 as an adjuvant in mucosal vaccination against viral respiratory infections;Utilisation de l'IL-7 comme adjuvant de vaccination muqueuse contre les infections virales respiratoires

Mucosae are a prime site for infections, since they are the gateway to many pathogens. However, few vaccines have been developed to specifically target mucosal immunity. Recent data from our laboratory evidenced a local expression of IL-7 in intestinal mucosa during the acute phase of SIV infection in macaques. Acting as a danger signal, this overexpression of IL-7 led to chemokine production and immune cell infiltration to this mucosa. In addition, systemic injection of IL-7 to macaques rapidly stimulated both the production of chemokines and immune cells homing toward various mucosae (lung, intestine, skin). These results altogether suggest that IL-7 could be used as a mucosal vaccine adjuvant. At first, I confirmed that IL-7 was a danger signal, but this time for the lung mucosa. Indeed, during the acute phase of influenza virus (IAV) infection, an overproduction of IL-7 was observed in the lung mucosa. In line with data from our laboratory, this overexpression is followed by the production of different chemokines. Then, we wanted to understand in more detail the mechanisms of action of IL-7 on the pulmonary mucosa. To do this, mice received an intratracheal administration of IL-7, then they were sacrificed at different post administration time points. The results showed that IL-7 rapidly induced the production of various proinflammatory chemokines and cytokines in the lung mucosa. The local inflammation was transient, since at D10 post administration, the expression level of the chemokines was close to the steady state. Chemokines are involved in the infiltration of different populations of immune cells into the lung. Within the lung mucosa, some immune cells, in particular T-cells and B-cells organized themselves into lymphoid aggregates homogeneously distributed in the lung. Finally, I studied the adjuvant potential of IL-7 in pulmonary immunization protocols. To do this, mice were pre-treated with an intratracheal administration of IL-7, then immunized by the same route, two days later. Analysis of the immune response at D14 showed that mice treated with IL-7 developed a significantly higher humoral response (IgA and IgG) compared to mice untreated mice. Moreover, in an influenza virus vaccination protocol chosen as a model, we showed that only IL-7-treated immunized mice were protected against influenza pathology. This protection seems to be related to the high level of IAV-specific antibodies, the development of lymphoid aggregate and the large number of IgA-plasma cells observed in the lung mucosa. IL-7 changes the pulmonary physiology by attracting immune cells into mucosae. Indeed, local administration of IL-7 prepares the mucosal immune system, gathering conditions that result in enhanced antigen-specific pulmonary immune responses upon antigenic stimulation. These data show that IL-7 can be used as a mucosal adjuvant and provide a promising tool for the development of mucosal vaccination strategies against respiratory infections. ; Les muqueuses sont la porte d'entrée de plusieurs pathogènes dans l'organisme, de ce fait, elles constituent un site privilégié d'infections. Cependant, très peu de vaccins ont été développés pour cibler de manière spécifique l'immunité muqueuse. Des données récentes publiées par notre laboratoire montrent que lors de la phase aiguë d'infection par le SIV chez le macaque, une surexpression d'IL-7 est observée dans la muqueuse intestinale. Cette cytokine agit comme un signal de danger, puisqu'elle conduit à la production de chimiokines et à l'infiltration de cellules immunitaires dans la muqueuse intestinale. De plus, l'injection systémique d'IL-7 à des macaques induit la production de chimiokines et le recrutement de cellules immunitaires vers les muqueuses (poumon, intestin, peau). Dans l'ensemble, ces résultats suggèrent que l'IL-7 possède un fort potentiel pour être utilisée comme adjuvant vaccinal muqueux. Tout d'abord, j'ai confirmé que l'IL-7 était un signal de danger, mais cette fois au niveau de la muqueuse pulmonaire. En effet, lors de la phase aiguë de l'infection par le virus de la grippe (IAV), une surproduction d'IL-7 est aussi observée dans la muqueuse pulmonaire. En concordance avec les données obtenues par notre laboratoire, cette surexpression est suivie par la production de différentes chimiokines. Afin de comprendre plus en détail les effets de l'IL-7 sur la muqueuse pulmonaire, des souris ont reçu une administration intratrachéale d'IL-7, puis elles ont été sacrifiées à différents temps. Les résultats montrent que l'IL-7 induit rapidement la production de diverses chimiokines et cytokines proinflammatoires dans la muqueuse pulmonaire. L'inflammation observée localement est transitoire, puisqu'à J10 post administration, le niveau d'expression des chimiokines tend à retourner vers l'état basal. Ces chimiokines sont impliquées dans l'infiltration de différentes populations de cellules immunitaires dans les poumons. Au sein de la muqueuse pulmonaire, certaines cellules immunitaires, en particulier les lymphocytes T et les lymphocytes B, s'organisent sous forme d'agrégats lymphoïdes répartis de manière homogène dans le poumon. Enfin, j'ai étudié le potentiel adjuvant de l'IL-7 dans des protocoles d'immunisation pulmonaire. Pour ce faire, des souris ont été prétraitées par une administration intratrachéale d'IL-7, puis immunisées par la même voie, deux jours plus tard. L'analyse de la réponse immunitaire à J14, montre que les souris traitées à l'IL-7 développent une réponse humorale significativement plus élevée (IgA et IgG) par rapport aux souris non traitées. De plus, dans un protocole de vaccination contre le virus de la grippe (IAV), choisi comme modèle, nous constatons que seules les souris traitées à l'IL-7 avant l'immunisation sont protégées contre la maladie grippale. Cette protection semble être liée au taux élevé d'anticorps spécifiques de l'IAV, au développement d'agrégats lymphoïdes et au grand nombre de plasmocytes à IgA observés dans la muqueuse pulmonaire. L'IL-7 modifie la physiologie pulmonaire ce qui permet une prise en charge antigénique efficace ainsi que la stimulation d'une réponse immunitaire muqueuse spécifique. Ces données montrent que l'IL-7 peut être utilisée comme adjuvant muqueux et constitue un outil prometteur pour le développement de stratégies vaccinales muqueuses contre les infections respiratoires.

Matrix-Encoding Gene Diversity of 624 Influenza A/H3N2 Genomes Does Not Show Association with Impaired Viral Detection by Commercialized qPCR Assays

As for the case of SARS-CoV-2, genome sequencing of influenza viruses is of potential interest to raise and address virological issues. Recently, false-negativity of real-time reverse transcription-PCR (qPCR) assays that detect influenza A/H3N2 virus RNA were reported and associated with two mutations (A37T and C161T) in the Matrix-encoding (M1) gene located on viral segment 7. This triggered a national alert in France. The present study sought to assess the association between the presence of these mutations and potential false negative results of influenza A/H3N2 virus RNA detection by commercialized qPCR assays at the clinical virology laboratory of our university hospitals in southern France. This study focused on the genetic diversity in the M1 gene and segment 7 of 624 influenza A/H3N2 virus genomes obtained from respiratory samples having tested qPCR-positive with M1 gene-targeting assays in our clinical virology laboratory. A total of 585 among the 624 influenza A/H3N2 virus genomes (93.7%) were of clade 3C.2a1b.2a.2, and 39 (6.3%) were of clade 3C.2a1b.1a. M1 gene substitutions A37T and C161T were both present in 582 (93.3%) genomes, only of clade 3C.2a1b.2a.2. Substitution A37T was present in 621 (99.5%) genomes. Substitution C161T was present in 585 genomes (93.8%), all of clade 3C.2a1b.2a.2. Moreover, 21 other nucleotide positions were mutated in ≥90% of the genomes. The present study shows that A37T/C and C161T mutations, and other mutations in the M1 gene and segment 7, were widely present in influenza A/H3N2 virus genomes recovered from respiratory samples diagnosed qPCR-positive with commercialized assays.

Spatiotemporal variations of “triple-demic” outbreaks of respiratory infections in the United States in the post-COVID-19 era

Background The US confronted a “triple-demic” of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies. Methods We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively. Results Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023. Conclusions Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.

COVID-19 is associated with higher risk of venous thrombosis, but not arterial thrombosis, compared with influenza: Insights from a large US cohort

INTRODUCTION: Infection with SARS-CoV-2 is typically compared with influenza to contextualize its health risks. SARS-CoV-2 has been linked with coagulation disturbances including arterial thrombosis, leading to considerable interest in antithrombotic therapy for Coronavirus Disease 2019 (COVID-19). However, the independent thromboembolic risk of SARS-CoV-2 infection compared with influenza remains incompletely understood. We evaluated the adjusted risks of thromboembolic events after a diagnosis of COVID-19 compared with influenza in a large retrospective cohort. METHODS: We used a US-based electronic health record (EHR) dataset linked with insurance claims to identify adults diagnosed with COVID-19 between April 1, 2020 and October 31, 2020. We identified influenza patients diagnosed between October 1, 2018 and April 31, 2019. Primary outcomes [venous composite of pulmonary embolism (PE) and acute deep vein thrombosis (DVT); arterial composite of ischemic stroke and myocardial infarction (MI)] and secondary outcomes were assessed 90 days post-diagnosis. Propensity scores (PS) were calculated using demographic, clinical, and medication variables. PS-adjusted hazard ratios (HRs) were calculated using Cox proportional hazards regression. RESULTS: There were 417,975 COVID-19 patients (median age 57y, 61% women), and 345,934 influenza patients (median age 47y, 66% women). Compared with influenza, patients with COVID-19 had higher venous thromboembolic risk (HR 1.53, 95% CI 1.38–1.70), but not arterial thromboembolic risk (HR 1.02, 95% CI 0.95–1.10). Secondary analyses demonstrated similar risk for ischemic stroke (HR 1.11, 95% CI 0.98–1.25) and MI (HR 0.93, 95% CI 0.85–1.03) and higher risk for DVT (HR 1.36, 95% CI 1.19–1.56) and PE (HR 1.82, 95% CI 1.57–2.10) in patients with COVID-19. CONCLUSION: In a large retrospective US cohort, COVID-19 was independently associated with higher 90-day risk for venous thrombosis, but not arterial thrombosis, as compared with influenza. These findings may inform crucial knowledge gaps regarding the specific thromboembolic risks of COVID-19.

Bias in control selection associated with the use of rapid tests in influenza vaccine effectiveness studies

In test-negative design studies that use rapid tests to estimate influenza vaccine effectiveness (VE) a common concern is case/control misclassification due to imperfect test sensitivity and specificity. However, an imperfect test can also fail to exclude from the control group people that do not represent the source population, including people infected with other influenza types or other vaccine-preventable respiratory viruses for which vaccination status is correlated. We investigated these biases by comparing the effectiveness of seasonal 2023/24 influenza vaccination against influenza A and B based on PCR versus rapid test results, excluding controls who tested positive for SARS-CoV-2 or the other type of influenza. By PCR, VE against influenza A was 49% (95%CI 26–65%) after exclusion of PCR-confirmed influenza B and SARS-CoV-2 controls. Corresponding VE against influenza B was 65% (95%CI 35–81%). VE estimated by adjusting for COVID-19 vaccination status yielded similar estimates to the scenario that excluded SARS-CoV-2-positive controls. When case/control status and exclusions from test-negative controls were determined by rapid test, VE was reduced by 5–15 percentage points. Bias correction methods were able to reduce these discrepancies. When estimating VE from a test-negative study using rapid test results, methods to correct misclassification bias are recommended.

Relationship between ventilator-associated pneumonia and mortality in COVID-19 patients: a planned ancillary analysis of the coVAPid cohort

International audience; Background Patients with SARS-CoV-2 infection are at higher risk for ventilator-associated pneumonia (VAP). No study has evaluated the relationship between VAP and mortality in this population, or compared this relationship between SARS-CoV-2 patients and other populations. The main objective of our study was to determine the relationship between VAP and mortality in SARS-CoV-2 patients. Methods Planned ancillary analysis of a multicenter retrospective European cohort. VAP was diagnosed using clinical, radiological and quantitative microbiological criteria. Univariable and multivariable marginal Cox’s regression models, with cause-specific hazard for duration of mechanical ventilation and ICU stay, were used to compare outcomes between study groups. Extubation, and ICU discharge alive were considered as events of interest, and mortality as competing event. Findings Of 1576 included patients, 568 were SARS-CoV-2 pneumonia, 482 influenza pneumonia, and 526 no evidence of viral infection at ICU admission. VAP was associated with significantly higher risk for 28-day mortality in SARS-CoV-2 group (adjusted HR 1.65 (95% CI 1.11–2.46), p = 0.013), but not in influenza (1.74 (0.99–3.06), p = 0.052), or no viral infection groups (1.13 (0.68–1.86), p = 0.63). VAP was associated with significantly longer duration of mechanical ventilation in the SARS-CoV-2 group, but not in the influenza or no viral infection groups. VAP was associated with significantly longer duration of ICU stay in the 3 study groups. No significant difference was found in heterogeneity of outcomes related to VAP between the 3 groups, suggesting that the impact of VAP on mortality was not different between study groups. Interpretation VAP was associated with significantly increased 28-day mortality rate in SARS-CoV-2 patients. However, SARS-CoV-2 pneumonia, as compared to influenza pneumonia or no viral infection, did not significantly modify the relationship between VAP and 28-day mortality. Clinical trial registration The study was registered at ClinicalTrials.gov, number NCT04359693.

Influenza Vaccination and Risk of Lung Cancer in Patients with Chronic Kidney Disease: A Nationwide, Population-Based Cohort Study

SIMPLE SUMMARY: The risk of lung cancer occurrence among patients with chronic kidney disease (CKD) is significantly higher than patients with normal renal function. Moreover, the long-term outcomes of CKD patients with lung cancer are poor. We conducted the present nationwide population based study to assess the association between the risk of lung cancer and influenza vaccination. The results showed that among CKD patients, potentially risk reduction of lung cancer was observed after influenza vaccination. In addition, the potentially protective effect of influenza vaccination on lung cancer risk reduction appeared to be dose dependent. The findings of present study provided the additional effect of influenza vaccination among patients with CKD. ABSTRACT: Chronic kidney disease (CKD) is significantly associated with lung cancer incidence. The aim of this study was to elucidate whether influenza vaccination reduces the incidence of lung cancer in patients with CKD. This cohort study enrolled patients with a record of CKD diagnosis from 2000 to 2012 in Taiwan’s National Health Insurance Research Database. Included patients were divided into vaccinated and unvaccinated groups. In total 12,985 patients with CKD were enrolled. Among these patients, 5495 were vaccinated and 7490 were unvaccinated. The risk of lung cancer was significantly lower in the influenza vaccination group after adjusting for age, sex, dialysis status, lung diseases, comorbidities, level of urbanization, and monthly income (adjusted hazard ratio (HR): 0.50, 95% confidence interval (CI; 0.38–0.65), p < 0.05). Lower risk of lung cancer was observed in both sexes, all age groups, dialysis status and co-existed lung diseases. The association between the risk of lung cancer and vaccination appeared to be dose-dependent (adjusted HRs: 0.91 (0.66–1.25), 0.49 (0.34–0.71), and 0.25 (0.17–0.38) for patients who received 1, 2 or 3, and ≥4 vaccinations during the follow-up period, respectively). In conclusion, Influenza vaccination decreased the risk of lung cancer in patients diagnosed with CKD. This potentially protective effect against lung cancer appeared to be dose dependent.

Altered gene expression in human brain microvascular endothelial cells in response to the infection of influenza H1N1 virus

Influenza viruses not only cause respiratory illness, but also have been reported to elicit neurological manifestations following acute viral infection. The central nervous system (CNS) has a specific defense mechanism against pathogens structured by cerebral microvasculature lined with brain endothelial cells to form the blood–brain barrier (BBB). To investigate the response of human brain microvascular endothelial cells (hBMECs) to the Influenza A virus (IAV), we inoculated the cells with the A/WSN/33 (H1N1) virus. We then conducted an RNAseq experiment to determine the changes in gene expression levels and the activated disease pathways following infection. The analysis revealed an effective activation of the innate immune defense by inducing the pattern recognition receptors (PRRs). Along with the production of proinflammatory cytokines, we detected an upregulation of interferons and interferon-stimulated genes, such as IFN-β/λ, ISG15, CXCL11, CXCL3 and IL-6, etc. Moreover, infected hBMECs exhibited a disruption in the cytoskeletal structure both on the transcriptomic and cytological levels. The RNAseq analysis showed different pathways and candidate genes associated with the neuroactive ligand-receptor interaction, neuroinflammation, and neurodegenerative diseases, together with a predicted activation of the neuroglia. Likewise, some genes linked with the mitochondrial structure and function displayed a significantly altered expression. En masse, this data supports that hBMECs could be infected by the IAV, which induces the innate and inflammatory immune response. The results suggest that the influenza virus infection could potentially induce a subsequent aggravation of neurological disorders.

Gasdermin D promotes hyperinflammation and immunopathology during severe influenza A virus infection

Excessive inflammation and tissue damage during severe influenza A virus (IAV) infection can lead to the development of fatal pulmonary disease. Pyroptosis is a lytic and pro-inflammatory form of cell death executed by the pore-forming protein gasdermin D (GSDMD). In this study, we investigated a potential role for GSDMD in promoting the development of severe IAV disease. IAV infection resulted in cleavage of GSDMD in vivo and in vitro in lung epithelial cells. Mice genetically deficient in GSDMD ( Gsdmd ^−/−) developed less severe IAV disease than wildtype mice and displayed improved survival outcomes. GSDMD deficiency significantly reduced neutrophil infiltration into the airways as well as the levels of pro-inflammatory cytokines TNF, IL-6, MCP-1, and IL-1α and neutrophil-attracting chemokines CXCL1 and CXCL2. In contrast, IL-1β and IL-18 responses were not largely impacted by GSDMD deficiency. In addition, Gsdmd ^−/− mice displayed significantly improved influenza disease resistance with reduced viral burden and less severe pulmonary pathology, including decreased epithelial damage and cell death. These findings indicate a major role for GSDMD in promoting damaging inflammation and the development of severe IAV disease.

Breathing and tilting: mesoscale simulations illuminate influenza glycoprotein vulnerabilities

Influenza virus has resurfaced recently from inactivity during the early stages of the COVID-19 pandemic, raising serious concerns about the nature and magnitude of future epidemics. The main antigenic targets of influenza virus are two surface glycoproteins, hemagglutinin (HA) and neuraminidase (NA). Whereas the structural and dynamical properties of both glycoproteins have been studied previously, the understanding of their plasticity in the whole-virion context is fragmented. Here, we investigate the dynamics of influenza glycoproteins in a crowded protein environment through mesoscale all-atom molecular dynamics simulations of two evolutionary-linked glycosylated influenza A whole-virion models. Our simulations reveal and kinetically characterize three main molecular motions of influenza glycoproteins: NA head tilting, HA ectodomain tilting, and HA head breathing. The flexibility of HA and NA highlights antigenically relevant conformational states, as well as facilitates the characterization of a novel monoclonal antibody, derived from human convalescent plasma, that binds to the underside of the NA head. Our work provides previously unappreciated views on the dynamics of HA and NA, advancing the understanding of their interplay and suggesting possible strategies for the design of future vaccines and antivirals against influenza.

Reconstructed influenza A/H3N2 infection histories reveal variation in incidence and antibody dynamics over the life course

Humans experience many influenza infections over their lives, resulting in complex and varied immunological histories. Although experimental and quantitative analyses have improved our understanding of the immunological processes defining an individual’s antibody repertoire, how these within-host processes are linked to population-level influenza epidemiology remains unclear. Here, we used a multi-level mathematical model to jointly infer antibody dynamics and individual-level lifetime influenza A/H3N2 infection histories for 1,130 individuals in Guangzhou, China, using 67,683 haemagglutination inhibition (HI) assay measurements against 20 A/H3N2 strains from repeat serum samples collected between 2009 and 2015. These estimated infection histories allowed us to reconstruct historical seasonal influenza patterns and to investigate how influenza incidence varies over time, space and age in this population. We estimated median annual influenza infection rates to be approximately 18% from 1968 to 2015, but with substantial variation between years. 88% of individuals were estimated to have been infected at least once during the study period (2009-2015), and 20% were estimated to have three or more infections in that time. We inferred decreasing infection rates with increasing age, and found that annual attack rates were highly correlated across all locations, regardless of their distance, suggesting that age has a stronger impact than fine-scale spatial effects in determining an individual’s antibody profile. Finally, we reconstructed each individual’s expected antibody profile over their lifetime and inferred an age-stratified relationship between probability of infection and HI titre. Our analyses show how multi-strain serological panels provide rich information on long term, epidemiological trends, within-host processes and immunity when analyzed using appropriate inference methods, and adds to our understanding of the life course epidemiology of influenza A/H3N2.

A systematic review of mechanistic models used to study avian influenza virus transmission and control

All data generated or analyzed during this study are included in this published article and its Additional files.;International audience; The global spread of avian influenza A viruses in domestic birds is causing dramatic economic and social losses. Various mechanistic models have been developed in an attempt to better understand avian influenza transmission and to evaluate the effectiveness of control measures. However, no comprehensive review of the mechanistic approaches used currently exists. To help fill this gap, we conducted a systematic review of mechanistic models applied to real-world epidemics to (1) describe the type of models and their epidemiological context, (2) synthetise estimated values of AIV transmission parameters and (3) review the control strategies most frequently evaluated and their outcome. Fourty-five articles qualified for inclusion, that fitted the model to data and estimated parameter values (n = 42) and/or evaluated the effectiveness of control strategies (n = 21). The majority were population-based models (n = 26), followed by individual-based models (n = 15) and a few metapopulation models (n = 4). Estimated values for the transmission rate varied substantially according to epidemiological settings, virus subtypes and epidemiological units. Other parameters such as the durations of the latent and infectious periods were more frequently assumed, limiting the insights brought by mechanistic models on these. Concerning control strategies, many models evaluated culling (n = 15), while vaccination received less attention (n = 7). According to the reviewed articles, optimal control strategies varied between virus subtypes and local conditions, and also depended on the objective. For instance, vaccination was optimal when the objective was to limit the overall number of culled flocks, while pre-emptive culling was preferred for reducing the epidemic size and duration. Earlier implementation of interventions consistently improved the efficacy of control strategies, highlighting the need for effective surveillance and epidemic preparedness. Potential improvements of mechanistic models include explicitly accounting for various transmission routes, and distinguishing poultry populations according to species and farm type. To provide insights to policy makers in a timely manner, aspects about the evaluation of control strategies that could deserve further attention include: economic evaluation, combination of strategies including vaccination, the use of optimization algorithm instead of comparing a limited set of scenarios, and real-time evaluation.

Prevalence and predictors of influenza vaccination in long-term care homes: a cross-national retrospective observational study

OBJECTIVE: To compare facility-level influenza vaccination rates in long-term care (LTC) homes from four countries and to identify factors associated with influenza vaccination among residents. DESIGN AND SETTING: Retrospective cross-sectional study of individuals residing in LTC homes in New Brunswick (Canada), New Zealand, Switzerland, and the Netherlands between 2017 and 2020. PARTICIPANTS: LTC home residents assessed with interRAI assessment system instruments as part of routine practice in New Brunswick (n=7006) and New Zealand (n=34 518), and national pilot studies in Switzerland (n=2760) and the Netherlands (n=1508). End-of-life residents were excluded from all country cohorts. OUTCOMES: Influenza vaccination within the past year. RESULTS: Influenza vaccination rates among LTC home residents were highest in New Brunswick (84.9%) and lowest in Switzerland (63.5%). For all jurisdictions where facility-level data were available, substantial interfacility variance was observed. There was approximately a fourfold difference in the coefficient of variation for facility-level vaccination rates with the highest in Switzerland at 37.8 and lowest in New Brunswick at 9.7. Resident-level factors associated with vaccine receipt included older age, severe cognitive impairment, medical instability, health conditions affecting a greater number of organ systems and social engagement. Residents who displayed aggressive behaviours and smoke tobacco were less likely to be vaccinated. CONCLUSION: There are opportunities to increase influenza vaccine uptake at both overall country and individual facility levels. Enhanced vaccine administration monitoring programmes in LTC homes that leverage interRAI assessment systems should be widely adopted.

Fractures and fall injuries after hospitalization for seasonal influenza—a national retrospective cohort study

Summary In this retrospective cohort study of 6604 adults, 65 years or older, admitted with seasonal influenza at Swedish hospitals, and 330,200 age- and sex-matched controls from the general population admitted for other reasons, were included. Patients with influenza had increased risk of fall injuries and fractures compared to controls. Introduction Fractures and fall injuries often lead to disability, increased morbidity, and mortality. Older adults are at higher risk of influenza-related complications such as pneumonia, cardiovascular events, and deaths, but the risk of fractures and fall injuries is unclear. The primary objective of this study was to investigate the risk of fractures and fall injuries in older patients after admission with seasonal influenza. Methods In this retrospective cohort study of 6604 adults, 65 years or older, admitted with seasonal influenza at Swedish hospitals (from December 1, 2015, to December 31, 2017) and 330,200 age- and sex-matched controls from the general population and admitted for other reasons, the risk of fracture or fall injury was investigated. Results The mean (SD) age of the 6604 influenza patients was 80.9 (8.1) years and 50.1% were women. During the first year after hospital discharge, there were 680 (10.3%) patients suffering from a fracture or fall injury among the patients with influenza, and 25,807 (7.8%) among the controls, corresponding to incident rates of 141 (95% CI, 131–152) and 111 (95% CI, 110–112) fractures or fall injuries per 1000 person-years respectively, translating to a significantly increased risk of fracture or fall injury in a Cox regression model (hazard ratio (HR) 1.28 (95% CI, 1.19–1.38)), a risk that was maintained after multivariable adjustment (HR 1.22 (95% CI 1.13–1.31)). Conclusions Older adults admitted with influenza diagnosis have an increased risk of fracture or fall injury during the first year after discharge.

A multidisciplinary journey to approach public health challenges

The focus of my research has been on the epidemiology of vaccine preventable diseases. This included the evaluation of pneumococcal conjugate vaccines in a high HIV-burden setting, epidemiology of respiratory viral associated-pneumonia, and the impact of vaccination during pregnancy on the foetus and newborn. Included in my research portfolio are two pivotal randomized placebo-controlled trials of influenza vaccination during pregnancy and a third trial, using different vaccination regimens among HIV-infected women. These trials demonstrated the effect of vaccinating pregnant women on the prevention of influenza illness in the vaccinated mothers and their infants. The findings from the studies were pivotal in the Scientific Advisory Group of Experts (SAGE) formulating World Health Organization (WHO) recommendations for the prioritisation of influenza vaccination of pregnant women in low-middle income countries, and for the change in indication and labelling of influenza vaccines in Europe to include pregnancy. I conducted large epidemiological studies on the burden of COVID-19 among pregnant women, hospitalized adults and healthcare workers. Furthermore, in a prospective hospital-based surveillance I investigated the burden of Influenza, Respiratory Syncytial Virus (RSV), and Pertussis infections in children. With the rollout of COVID-19 vaccines, I was involved in vaccine effectiveness evaluation studies in South Africa, Eswatini, Botswana and Mozambique.The COVID-19 pandemic has raised the attention on the substantial health burden respiratory viruses can cause to humans worldwide, emphasizing more than ever the importance to continue producing reliable burden of disease estimates. For the next steps of my research I will develop projects that investigate respiratory viruses’ seasonality and viral evolution, as this is extremely relevant and will allow the identification of the populations most at-risk. I will perform post-registration evaluation for new vaccines and other preventive measures aiming to establish the optimal use of vaccines in France and globally. I will leverage on data, infrastructures, and expertise available from several existing partners at local, national, and global levels to achieve the following objectives:1.Understand the burden and epidemiology of respiratory viruses that cause disease in very young and/or older age groups, which could lead to the development of improved or new vaccines.Justification: traditionally the burden of respiratory infections displays a U shape curve relative to age groups, protecting these groups will be most effective to prevent illness and transmission. Additionally, robust science and evidence-based information are needed to support policy decisions and industrial investment in new vaccines. 2.Monitor the genetic evolution of respiratory viruses and associated mutations.Justification: information on viruses’ genetic evolution dynamics will guide research and development for future vaccine targets and formulations. Moreover, viral genetic information can be used by sentinel systems to identify new viral threads (e.g., changes in phenotype representation of viruses that could lead to more severe diseases) or interpret why certain vaccines are not performing well.3.Evaluate the real-life performance of vaccines against different outcomes.Justification: real-world evidence can be used for post-registration evaluation.I will integrate and analyse large longitudinal data routinely collected by the Hospices Civils de Lyon (HCL). For the projects focused on French national information, I will use French National Health Insurance (SNDS) data. I will also use the Global Initiative on Sharing All Influenza Data (GISAID) platform to obtain genetic sequences and related clinical and epidemiological data from different geographical areas. This project is a multi-disciplinary applied research program that will use information from surveillance platforms, clinical records, viral phenotypic and genomic data to better understand the circulation of respiratory viruses, their clinical impact and inform future changes in epidemiology that might occur.

Distribution and functional perspective analysis of epiphytic and endophytic bacterial communities associated with marine seaweeds, Alexandria shores, Egypt

There is an enormous diversity of life forms present in the extremely intricate marine environment. The growth and development of seaweeds in this particular environment are controlled by the bacteria that settle on their surfaces and generate a diverse range of inorganic and organic chemicals. The purpose of this work was to identify epiphytic and endophytic bacterial populations associated with ten common marine macroalgae from various areas along the Mediterranean Sea coast in Alexandria. This was done to target their distribution and possible functional aspects. Examine the effects of the algal habitat on the counting and phenotypic characterization of bacteria, which involves grouping bacteria based on characteristics such as shape, colour, mucoid nature, type of Gram stain, and their ability to generate spores. Furthermore, studying the physiological traits of the isolates under exploration provides insight into the optimum environmental circumstances for bacteria associated with the formation of algae. The majority of the bacterial isolates exhibited a wide range of enzyme activities, with cellulase, alginase, and caseinase being the most prevalent, according to the data. Nevertheless, 26% of the isolates displayed amylolytic activity, while certain isolates from Miami, Eastern Harbor, and Montaza lacked catalase activity. Geographical variations with the addition of algal extract may impact on the enumeration of the bacterial population, and this might have a relationship with host phylogeny. The most significant observation was that endophytic bacteria associated with green algae increased in all sites, while those associated with red algae increased in Abu Qir and Miami sites and decreased in Eastern Harbor. At the species level, the addition of algal extract led to a ninefold increase in the estimated number of epiphytic bacteria for Cladophora pellucida in Montaza. Notably, after adding algal extract, the number of presented endophytic bacteria associated with Codium sp. increased in Abu Qir while decreasing with the same species in Montaza. In addition to having the most different varieties of algae, Abu Qir has the most different bacterial isolates.

Optimization of fermentation parameters to improve the biosynthesis of selenium nanoparticles by Bacillus licheniformis F1 and its comprehensive application

Background Selenium nanoparticles (SeNPs) are increasingly gaining attention due to its characteristics of low toxicity, high activity, and stability. Additionally, Bacillus licheniformis , as a probiotic, has achieved remarkable research outcomes in diverse fields such as medicine, feed processing, and pesticides, attracting widespread attention. Consequently, evaluating the activity of probiotics and SeNPs is paramount. The utilization of probiotics to synthesize SeNPs, achieving large-scale industrialization, is a current hotspot in the field of SeNPs synthesis and is currently the most promising synthetic method. To minimize production costs and maximize yield of SeNPs, this study selected agricultural by-products that are nutrient-rich, cost-effective, and readily available as culture medium components. This approach not only fulfills industrial production requirements but also mitigates the impact on downstream processes. Results The experimental findings revealed that SeNPs synthesized by B. licheniformis F1 exhibited a spherical morphology with diameters ranging from 110 to 170 nm and demonstrating high stability. Both the secondary metabolites of B. licheniformis F1 and the synthesized SeNPs possessed significant free radical scavenging ability. To provide a more robust foundation for acquiring large quantities of SeNPs via fermentation with B. licheniformis F1, key factors were identified through single-factor experiments and response surface methodology (RSM) include a 2% seed liquid inoculum, a temperature of 37 ℃, and agitation at 180 rpm. Additionally, critical factors during the optimization process were corn powder (11.18 g/L), soybean meal (10.34 g/L), and NaCl (10.68 g/L). Upon validating the optimized conditions and culture medium, B. licheniformis F1 can synthesize nearly 100.00% SeNPs from 5 mmol/L sodium selenite. Subsequently, pilot-scale verification in a 5 L fermentor using the optimized medium resulted in a shortened fermentation time, significantly reducing production costs. Conclusion In this study, the efficient production of SeNPs by the probiotic B. licheniformis F1 was successfully achieved, leading to a significant reduction in fermentation costs. The exploration of the practical applications of this strain holds significant potential and provides valuable guidance for facilitating the industrial-scale implementation of microbial synthesis of SeNPs.

Revealing the microbial composition changes and relationship with Fusarium caused by rot disease in the Crocus sativus L.

Purpose Rot disease caused by Fusarium poses a formidable threat to the growth of saffron ( Crocus sativus L.), resulting in substantial damage to both yield and quality. It is paramount to delve into the root causes of rot disease in saffron to optimize both yield and quality. Existing preventive and treatment modalities have exerted deleterious effects on corms and the natural environment. Consequently, the quest for efficacious and eco-friendly methods such as biological control agents has become an urgent imperative. Methods The disparate distribution of microbial communities between rhizospheric microorganisms and saffron serves as the foundational exploration for uncovering the underlying causes of rot disease. Samples from various saffron organs and rhizosphere soil were gathered, and the sequencing data from the microbial communities were interpreted using 16S rRNA and ITS gene sequencing methods. This facilitated an in-depth examination of the composition and changes of microorganisms in both healthy and diseased saffron plants. Results The findings indicated rot disease reduced the abundance and diversity of microorganisms in saffron, and the fungal co-occurrence networks were less stable and their communities were more sensitive to rot disease than the bacterial community. Fusarium was the predominant genus in diseased samples, accounting for 99.19% and 89.77% of the communities in diseased leaves and corms. With corms and leaves displaying heightened susceptibility to infection compared to other plant organs. Some of the beneficial bacterial taxa enriched in the diseased plants were also identified in networks, they showed an antagonistic relationship with Fusarium , suggesting a potential for these bacteria to be used in biologically based control strategies against rot disease. These insights could prove invaluable for the development of biocontrol agents aimed at combating this plant ailment. Conclusion These findings significantly advance our understanding of saffron-microbiome interactions and could provide fundamental and important data for improving saffron yield and quality in the process of sustainable development.

Fungi

Fungi still represent a largely unexplored biological frontier with extraordinary potential for the bioeconomy. This chapter reveals fungi as decomposers of matter, biochemical agents, or even manufacturing platforms whose unique capabilities are increasingly central to sustainable development. Through their exceptional enzymatic systems, mycelial networks, and metabolic versatility, fungi transform agricultural residues, forestry byproducts, and industrial waste streams into valuable new products, ranging from novel biomaterials and pharmaceuticals to biofuels. As we deepen our understanding of fungal biology and cultivation techniques, these organisms emerge as indispensable allies in creating truly circular systems that mimic nature’s waste-free paradigm.

Techniques in Detecting Fungal Elements in Biopsy Samples: Perspectives from Local Practices in Southeast Asia

Opportunistic infections, including mycotic diseases, are increasingly being recognized as a major public health concern in Southeast Asia. The diagnosis of fungal infections from clinical specimens relies mainly on culture and, at select institutions, molecular methods. In certain instances, it is possible to detect fungal elements, such as hyphae and yeast forms, in tissue samples submitted to the histopathology laboratory. Routine examination with hematoxylin and eosin (H and E) staining is sufficient to detect the presence of organisms in most cases. On occasion, special stains such as periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) may be utilized to highlight fungal structures. Definite species identification of fungi detected on routine histology still relies on correlation with clinical findings and ancillary tests. Among the challenges encountered in the region are the lack of facilities, equipment, and supplies in regular diagnostic laboratories; overreliance on referral laboratories; lack of skilled laboratory personnel; and few opportunities for training and subspecialization in diagnostic mycology. The introduction of newer techniques, such as MALDI-TOF and nucleic acid-based tests, may augment the services related to fungal diagnosis on biopsy specimens. Furthermore, international linkages in the region, especially in tropical medicine, may pave the way toward enhancing mycology services in the region.

A Rare Case of Non-Invasive Fungal Sinusitis Caused by Scedosporium Apiospermum in an Immunocompetent Patient

Fungal sinusitis are very often caused by Aspergillus spp. Dematiaceae spp. and mucomycoses. Sinusitis with Scedosporium spp is very rare, especially in immunocompetent humans. A 42-year-old female presented with long history of facial pain and headache. Isolated opacification of maxillary sinus with double density sign was noted on left side in the CT scan. Diagnosed as fungal rhino sinusitis and endoscopic sinus surgery was carried out to clear the disease of the sinus and further mycological evaluation proved the organism to be Scedosporium apiospermum. We report the rare case of a non-invasive isolated fungal sinusitis caused by fungi Scedosporium apiospermum.

Bio-digital feedback loop systems: a synergistic integration of predictive genomics, genome editing, and AI-driven phenomic synthesis for next-generation edible and medicinal mushroom breeding

Edible mushrooms face persistent challenges in yield optimization, bioactive compound production, and climate resilience that conventional breeding methods struggle to address. Traditional approaches such as cross-breeding, protoplast fusion, and mutagenesis are limited by genetic noise, laborious screening, and unstable trait inheritance. This review proposes a transformative paradigm built upon converging advances in molecular biology and data science: the bio-digital feedback loop (BDFL) framework, integrating multi-omics, CRISPR-engineered chassis strains, and predictive phenomics for precision mushroom breeding. Our framework employs multi-omics to decipher gene networks governing critical traits, such as substrate degradation enzymes, developmental synchrony regulators, and secondary metabolite pathways. CRISPR-Cas9 and synthetic biology tools then deploy these insights to verify and design modular gene circuits in pre-engineered "plug-and-play" chassis strains, enabling conflict-free stacking of desirable traits. Artificial intelligence serves as the linchpin, not only automating high-throughput phenotyping through advanced imaging but also accelerating the entire breeding cycle by predicting trait heritability from omics data and optimizing the design of CRISPR guide RNAs and genetic constructs for efficient editing. The BDFL we describe iteratively refines strains by feeding phenomics data back into AI algorithms, enabling rapid trait optimization cycles. This transcends the trial-and-error limitations of classical methods, accelerating development of climate-smart mushrooms for circular bioeconomies including strains engineered to thrive on agricultural waste, overproduce immunomodulatory compounds, or resist emerging pathogens. The integration of predictive genomics, AI-driven phenomics, and CRISPR-edited chassis strains heralds a new era of precision mycology, where mushrooms are computationally designed as sustainable solutions for global food security, pharmaceutical innovation, and ecological resilience, ultimately transforming fungi into programmable biological factories tailored to address pressing agricultural and ecological challenges.

Prevalence of onychomycosis among psoriasis patients: a clinico-mycological and dermoscopic comparative cross sectional study

Onychomycosis, a nail infection caused by dermatophytes, yeast, and molds makes up roughly half of all onychopathies and is the most prevalent nail condition in the world. Clinically, nail psoriasis and onychomycosis can frequently be difficult to distinguish from one another. To assess the prevalence of onychomycosis in patients with psoriasis. Fifty patients with psoriasis associated with nail disease were included in this study. After taking clinical history, nail samples were gathered for dermoscopic inspection, culture, direct microscopy with 20% KOH solution, and nail clipping with PAS stain. Of the 50 patients recruited, 43 were males and 7 were females, with mean age 6–71 years (mean ± SD 44.06 ± 16.2). Eleven patients (22%) tested positive for onychomycosis. Dermatophytes were isolated from 2% of patients, yeast from 14% of patients, and non-dermatophytic mold from 38% of patients. Histopathological results revealed fungal hyphae and spores in 18% of patients. The most prevalent dermoscopic sign in psoriatic patients with onychomycosis was spikes (81.8%) with statistical significance (P-value < 0.001), while nail pitting was the most prevalent dermoscopic feature in nail psoriasis. This study lays the way for an accurate diagnosis of nail lesions by highlighting the significance of cooperation between mycology, histology, and dermoscopy in the diagnosis of onychomycosis in patients with nail psoriasis.

Rhizosphere microbial community construction during the latitudinal spread of the invader Chromolaena odorata

The colonization of alien plants in new habitats is typically facilitated by microorganisms present in the soil environment. However, the diversity and structure of the archaeal, bacterial, and fungal communities in the latitudinal spread of alien plants remain unclear. In this study, the rhizosphere and bulk soil of Chromolaena odorata were collected from five latitudes in Pu’ er city, Yunnan Province, followed by amplicon sequencing of the soil archaeal, bacterial, and fungal communities. Alpha and beta diversity results revealed that the richness indices and the structures of the archaeal, bacterial, and fungal communities significantly differed along the latitudinal gradient. Additionally, significant differences were observed in the bacterial Shannon index, as well as in the structures of the bacterial and fungal communities between the rhizosphere and bulk soils. Due to the small spatial scale, trends of latitudinal variation in the archaeal, bacterial, and fungal communities were not pronounced. Total potassium, total phosphorus, available nitrogen, available potassium and total nitrogen were the important driving factors affecting the soil microbial community structure. Compared with those in bulk soil, co-occurrence networks in rhizosphere microbial networks presented lower complexity but greater modularity and positive connections. Among the main functional fungi, arbuscular mycorrhizae and soil saprotrophs were more abundant in the bulk soil. The significant differences in the soil microbes between rhizosphere and bulk soils further underscore the impact of C. odorata invasion on soil environments. The significant differences in the soil microbiota along latitudinal gradients, along with specific driving factors, demonstrate distinct nutrient preferences among archaea, bacteria, and fungi and indicate complex microbial responses to soil nutrient elements following the invasion of C. odorata .

Characterization of nuvita biosearch center (NBC) isolated lactic acid bacteria strains from human origin and determination of growth kinetic profiles of selected cultures under bioreactor

Backgorund In recent years, there has been an increasing interest in the field of research into the isolation and characterization of probiotics in the prevention of diseases and the need to maintain the continuity of healthy microbiota. Therefore, the aim of this study is to isolate and identify bacteria found in maternal colostrum, breast milk, adult and infant feces, analyze possible probiotic potential, and reveal the developmental kinetics of selected strains. Results We isolated 40 bacterial species from 4 different sources and identified 19 bacteria in the form of bacilli through molecular biology and carried out studies with 11 of them. Five of the selected strains showed the better results considering bile salt resistance and ability to survive at different pH and antimicrobial effect. When the adhesion capacity in cell culture is examined, the better 2 strains are; Lactobacillus pontis ZZ6780 and Lactobacillus reuteri NBC2680 were selected and the growth kinetics of these strains were demonstrated at the 3 L bioreactor scale. Finally, the growth kinetics of selected strains were determined and the maximum specific growth rate of selected Lactobacillus pontis ZZ6780 and Lactobacillus reuteri NBC2680 was calculated as 0.412 h^− 1 and 0.481 h^− 1, respectively. In addition, the dry cell matter amounts were found to be and 4.45 g/L and 5.23 g/L, respectively. Conclusion This study established the groundwork for the selection of safety probiotics for the development and application of LAB. It is thought that the two strains obtained as a result of this study can be considered as potential probiotic strains in the food, pharmaceutical and cosmetic industries.

Dry gel spinning of fungal hydrogels for the development of renewable yarns from food waste

Background Renewable materials made using environmentally friendly processes are in high demand as a solution to reduce the pollution created by the fashion industry. In recent years, there has been a growing trend in research on renewable materials focused on bio-based materials derived from fungi. Results Recently, fungal cell wall material of a chitosan producing fungus has been wet spun to monofilaments. This paper presents a modification for the fungal monofilament spinning process, by the development of a benign method, dry gel spinning, to produce continuous monofilaments and twisted multifilament yarns, from fungal cell wall, that can be used in textile applications. The fungal biomass of Rhizopus delemar , grown using bread waste as a substrate, was subjected to alkali treatment with a dilute sodium hydroxide solution to isolate alkali-insoluble material (AIM), which mainly consists of the fungal cell wall. The treatment of AIM with dilute lactic acid resulted in hydrogel formation. The morphology of the hydrogels was pH dependent, and they exhibited shear thinning viscoelastic behavior. Dry gel spinning of the fungal hydrogels was first conducted using a simple lab-scale syringe pump to inject the hydrogels through a needle to form a monofilament, which was directly placed on a rotating receiver and left to dry at room temperature. The resulting monofilament was used to make twisted multifilament yarns. The process was then improved by incorporating a heated chamber for the quicker drying of the monofilaments (at 30⁰C). Finally, the spinning process was scaled up using a twin-screw microcompounder instead of the syringe pump. The monofilaments were several meters long and reached a tensile strength of 63 MPa with a % elongation at break of 14. When spinning was performed in the heated chamber, the tensile strength increased to 80 MPa and further increased to 103 MPa when a micro-compounder was used for spinning. Conclusion The developed dry gel spinning method shows promising results in scalability and demonstrates the potential for renewable material production using fungi. This novel approach produces materials with mechanical properties comparable to those of conventional textile fibers.

Report of the Special-purpose Committee on Names of Fungi with the Same Epithet, established at the XIX International Botanical Congress in Shenzhen, China

A Special-purpose Committee on Fungal Names with the Same Epithet was established at the XIX International Botanical Congress (IBC) in Shenzhen, China in 2017, with a mandate to report to the 12th International Mycological Congress (IMC) with recommendations on a preferred course of action with respect to names of pleomorphic fungi sharing the same epithet under the International Code of Nomenclature for algae, fungi, and plants . This report provides a synthesis of the deliberations from the Special-purpose Committee. We discuss the arguments for and against the proposed solution to the problems that have arisen regarding the nomenclature of fungi described in multiple morphs using the same epithet. We also propose a gentler method of addressing the problem using existing procedures.

The Systematics and Phylogeny of Myxomycetes: Yesterday, Today, and Tomorrow

Abstract —Myxomycetes are amoeboid fungus-like organisms ( Amoebozoa ) with a unique life cycle characterized by a great morphological diversity of fruiting bodies. Due to the similarity of these structures to the fruiting bodies of some representatives of Ascomycota and Basidiomycota, myxomycetes have been classified as fungi since the first known scientific description in 1654. Only in the 19th century, when their life cycle was studied, did the difference of this group from fungi become clear. During the same period, microscopic structures of fruiting bodies, as well as ornamentation of the spore surface, began to be considered as diagnostic features. Due to this, in the period from the end of the 19th to the middle of the 20th century, a rather stable system was formed. However, as further studies have shown, both macro- and micromorphological characters are often quite variable, depend on environmental conditions, and often result from convergent evolution, which causes difficulties in defining species and taxonomic units of higher ranks. Since the first decade of the 21st century, due to the development of molecular genetic methods and the accumulation of data on nucleotide sequences of marker genes together with the improvement of microscopic studies, it has been possible to obtain data on the evolutionary relationships of different groups of myxomycetes. A milestone in this process was the publication of the first phylogenetic system of myxomycetes in 2019. This work was the starting point for a number of studies on the relationships between different groups of myxomycetes at a lower taxonomic level. Thus, there has been a surge in the number of studies that bring us closer to constructing a natural system.

Large-scale genetic correlation studies explore the causal relationship and potential mechanism between gut microbiota and COVID-19-associated risks

Recent observational studies suggest that gut microorganisms are involved in the onset and development of coronavirus disease 2019 (COVID-19), but the potential causal relationship behind them remains unclear. Exposure data were derived from the MiBioGen consortium, encompassing 211 gut microbiota ( n  = 18,340). The outcome data were sourced from the COVID-19 host genetics initiative (round 7), including COVID-19 severity ( n  = 1,086,211), hospitalization ( n  = 2,095,324), and susceptibility ( n  = 2,597,856). First, a two-sample Mendelian randomization (TSMR) was performed to investigate the causal effect between gut microbiota and COVID-19 outcomes. Second, a two-step MR was used to explore the potential mediators and underlying mechanisms. Third, several sensitivity analyses were performed to verify the robustness of the results. Five gut microbes were found to have a potential causality with COVID-19 severity, namely Betaproteobacteria (beta = 0.096, p  = 0.034), Christensenellaceae (beta = -0.092, p  = 0.023), Adlercreutzia (beta = 0.072, p  = 0.048), Coprococcus 1 (beta = 0.089, p  = 0.032), Eisenbergiella (beta = 0.064, p  = 0.024). Seven gut microbes were found to have a potential causality with COVID-19 hospitalization, namely Victivallaceae (beta = 0.037, p  = 0.028), Actinomyces (beta = 0.047, p  = 0.046), Coprococcus 2 (beta = -0.061, p  = 0.031), Dorea (beta = 0.067, p  = 0.016), Peptococcus (beta = -0.035, p  = 0.049), Rikenellaceae RC9 gut group (beta = 0.034, p  = 0.018), and Proteobacteria (beta = -0.069, p  = 0.035). Two gut microbes were found to have a potential causality with COVID-19 susceptibility, namely Holdemanella (beta = -0.024, p  = 0.023) and Lachnospiraceae FCS020 group (beta = 0.026, p  = 0.027). Multi-omics mediation analyses indicate that numerous plasma proteins, metabolites, and immune factors are critical mediators linking gut microbiota with COVID-19 outcomes. Sensitivity analysis suggested no significant heterogeneity or pleiotropy. These findings revealed the causal correlation and potential mechanism between gut microbiota and COVID-19 outcomes, which may improve our understanding of the gut-lung axis in the etiology and pathology of COVID-19 in the future.

The Asexual Morph of Seriascoma acutisporum (Occultibambusaceae, Pleosporales)

Abstract An investigation of freshwater fungi in Thailand resulted in the collection and isolation of a coelomycetous Seriascoma from submerged wood. The combined ITS and LSU sequence data placed the taxon within the sexual morphic Seriascoma acutisporum strains, ZHKUCC 22-0273 and ZHKUCC 22-0274, with high bootstrap support and confirmed the asexual morphic stage of the taxon. Seriascoma acutisporum MFLUCC 24-0091 shares the generic characteristics of having an immersed, eustromatic conidiomata and phialidic, cylindrical to ampulliform, hyaline, aseptate conidiogenous cells bearing hyaline conidia, but with smaller conidiomata and locules compared to other coelomycetous Seriascoma species.

Immunomodulatory and Antiviral Properties of Mushroom-Derived Compounds

Mushrooms, long recognized for their nutritional and medicinal value, are emerging as a critical resource in the development of next-generation immunomodulators and antiviral agents. This chapter synthesizes recent advances in understanding the bioactive potential of fungal metabolites, particularly β-glucans, terpenoids, and glycoproteins, which exhibit dual immunoenhancing and direct antiviral properties. These compounds orchestrate immune responses by activating macrophages, modulating cytokine networks, and enhancing natural killer (NK) cell cytotoxicity, while concurrently disrupting viral replication through mechanisms such as envelope protein inhibition and viral protease suppression. Notably, their efficacy against RNA viruses, including influenza, SARS-CoV-2, and HIV, highlights their relevance in combating pandemic threats and drug-resistant pathogens. Breakthroughs in biotechnological and nanotechnological approaches are revolutionizing the application of these compounds. Advances in nanoencapsulation (such as exosome-based delivery systems) address bioavailability challenges, enabling targeted immune cell engagement and sustained release of hydrophobic metabolites like ergosterol derivatives. Meanwhile, CRISPR-based metabolic engineering and fungal heterologous expression systems are overcoming barriers to the sustainable production of rare bioactives. This convergence of disciplines is accelerating the translation of mushroom-derived molecules into functional foods, adjuvants, and standalone therapeutics. Despite progress, challenges persist in standardization, pharmacokinetic optimization, and clinical validation. Future research must prioritize mechanistic elucidation of host–pathogen–bioactive interactions, coupled with robust clinical trials to establish efficacy and safety. By integrating mycological biodiversity with modern biotechnological tools, this field holds promise for delivering scalable, eco-conscious solutions to global health crises, bridging traditional medicine with twenty-first-century innovation.

Characterization of Bipolaris bicolor germination: effects of a physical factor on fungal adaptability

Studies on physiological responses to stimuli from physical factors are essential for understanding the dynamics of the microorganisms and higly important for the management of plant diseases. Besides, the development of an epidemiological model for pathogen populations requires studying their physiological responses to physical stimuli. The objective of this study was to evaluate the germination dynamics of spores from six isolates of Bipolaris bicolor under effects of light at 25 °C. Suspensions of 1.6 × 10^5 conidia mL^− 1 from the B. bicolor isolates were inoculated onto Petri dishes containing agar-water culture medium and incubated in a BOD chamber under two physical conditions: (a) constant darkness and (b) constant light for five hours. The study was conducted in a completely randomized design, with a 6 × 2 factorial arrangement (six B. bicolor isolates and two physical conditions) and five replications. The length of the germ tube was measured hourly. The constant darkness resulted in higher mean germ tube growth for the pathogen; however, differences in the final germination percentage were found among the isolates. The isolate F-24-02 exhibited the highest germination adaptability to constant darkness, presenting the longest germ tube length.

Gut microbiota in preterm infants with late-onset sepsis and pneumonia: a pilot case-control study

Background Late-onset sepsis (LOS) and pneumonia are common infectious diseases, with high morbidity and mortality in neonates. This study aimed to investigate the differences in the gut microbiota among preterm infants with LOS, or pneumonia, and full-term infants. Furthermore, this study aimed to determine whether there is a correlation between intestinal pathogenic colonization and LOS. Methods In a single-center case‒control study, 16 S rRNA gene sequencing technology was used to compare gut microbiota characteristics and differences among the LOS group, pneumonia group, and control group. Results Our study revealed that the gut microbiota in the control group was more diverse than that in the LOS group and pneumonia group ( P  < 0.05). No significant differences in diversity were detected between the LOS and pneumonia groups ( P >  0.05). Compared with the control group, the abundances of Akkermansia , Escherichia/Shigella , and Enterococcus increased, while the abundances of Bacteroides and Stenotrophomonas decreased in the LOS and pneumonia groups. The pathogenic bacteria in infants with LOS were consistent with the distribution of the main bacteria in the intestinal microbiota. An increase in Escherichia/Shigella abundance may predict a high risk of LOS occurrence, with an area under the curve (AUC) of 0.773. Conclusion Changes in the gut microbiota composition were associated with an increased risk of LOS and pneumonia. The dominant bacteria in the gut microbiota of the LOS group were found to be associated with the causative pathogen of LOS. Moreover, preterm infants exhibiting an elevated abundance of Escherichia/Shigella may be considered potential candidates for predicting the onset of LOS.

Inside-out, antimicrobial resistance mediated by efflux pumps in clinical strains of Acinetobacter baumannii isolated from burn wound infections

Abstract Acinetobacter baumannii belongs to the ESKAPE group. It is classified as a critical priority group by the World Health Organization and a global concern on account of its capacity to acquire and develop resistance mechanisms to multiple antibiotics. Data from the United States indicates 500 deaths annually. Resistance mechanisms of this bacterium include enzymatic pathways such as ß-lactamases, carbapenemases, and aminoglycoside-modifying enzymes, decreased permeability, and overexpression of efflux pumps. A. baumannii has been demonstrated to possess efflux pumps, which are classified as members of the MATE family, RND and MFS superfamilies, and SMR transporters. The aim of our work was to assess the distribution of efflux pumps and their regulatory gene expression in clinical strains of A. baumannii isolated from burned patients. Methods From the Clinical Microbiology Laboratory at the Instituto Nacional de Rehabilitación Luis Guillermo Ibarra Ibarra collection in Mexico, 199 strains were selected. Antibiotics susceptibilities were performed by broth microdilutions to determine minimal inhibitory concentrations. Phenotypic assays with efflux pump inhibitors were conducted using carbonyl cyanide 3-chlorophenylhydrazone (CCCP) and phenylalanine-arginine ß-naphthylamide (PAßN) in conjunction with amikacin, ceftazidime, imipenem, meropenem and levofloxacin. A search was conducted for structural genes that are linked to efflux pumps, and the relative expression of the adeR , adeS , and adeL genes was analyzed. Results Among a total of 199 strains, 186 exhibited multidrug resistance (MDR). Fluoroquinolones demonstrated the highest resistance rates, while minocycline and amikacin displayed comparatively reduced resistance rates (1.5 and 28.1, respectively). The efflux activity of fluorquinolones exhibited the highest phenotypic detection (from 85 to 100%), while IMP demonstrated the lowest activity of 27% with PAßN and 43.3% with CCCP. Overexpression was observed in adeS and adeL , with adeR exhibiting overexpression. Concluding that clinical strains of A. baumannii from our institution exhibited efflux pumps as one of the resistance mechanisms.

Genetic diversity and antifungal susceptibilities of environmental Cryptococcus neoformans and Cryptococcus gattii species complexes

Cryptococcosis is an opportunistic systemic mycosis caused by Cryptococcus neoformans and C. gattii species complexes and is of increasing global importance. Maintaining continued surveillance of the antifungal susceptibility of environmental C. neoformans and C. gattii isolates is desirable for better managing cryptococcosis by identifying resistant isolates and revealing the emergence of intrinsically resistant species. Relevant research data from Egypt are scarce. Thus, this study aimed to report the genetic diversity of C. neoformans and C. gattii species complexes originating from different environmental sources in Egypt, antifungal susceptibility profiles, antifungal combinations, and correlations of susceptibility with genotypes. A total of 400 environmental samples were collected, 220 from birds and 180 from trees. Cryptococcus spp. were found in 58 (14.5%) of the samples, 44 (75.9%) of the isolates were recovered from birds and 14 (24.1%) from trees. These isolates were genotyped using M13 polymerase chain reaction-fingerprinting and URA 5 gene restriction fragment length polymorphism analysis. Of the 31 C. neoformans isolates, 24 (77.4%), 6 (19.4%) and one (4.4%) belonged to VNI, VNII, and VNIII genotypes, respectively. The 27 C. gattii isolates belonged to VGI (70.4%), VGII (18.5%), and VGIII (11.1%) genotypes. Non-wild type C. neoformans and C. gattii isolates that may have acquired resistance to azoles, amphotericin B (AMB), and terbinafine (TRB) were observed. C. gattii VGIII was less susceptible to fluconazole (FCZ) and itraconazole (ITZ) than VGI and VGII. C. neoformans isolates showed higher minimum inhibitory concentrations (MICs) to FCZ, ITZ, and voriconazole (VRZ) than those of C. gattii VGI and VGII. Significant ( P  < 0.001) correlations were found between the MICs of VRZ and ITZ ( r  = 0.64) in both C . neoformans and C. gattii isolates, FCZ and TRB in C. neoformans isolates, and FCZ and TRB ( r  = 0.52) in C. gattii isolates. There is no significant differences in the MICs of TRB in combination with FCZ ( P  = 0.064) or in combination with AMB ( P  = 0.543) and that of TRB alone against C. gattii genotypes. By calculating the fractional inhibitory concentration (FIC) index, the combination of FCZ + AMB was synergistic against all tested genotypes. These findings expand our knowledge of ecological niches, genetic diversity, and resistance traits of C. neoformans and C. gattii genotypes in Egypt. Further investigations into how they are related to clinical isolates in the region are warranted.

Growth Curve Preparation for the Calculation of Fungal Cell Count Over Time

Establishing a growth curve to calculate fungal cell count is a systematic approach in which a fungal culture is inoculated into a fresh medium and its growth is monitored over a period of time. This process begins with the selection of a suitable fungal strain and the preparation of a liquid medium that provides optimal growth conditions (Meletiadis et al, J Clin Microbiol 39:478–484, 2001). After inoculation, samples are taken at regular intervals, e.g. every few hours or days, depending on the expected growth rate of the fungus. Once the samples are collected, they are serially diluted to ensure that the resulting colonies are countable when plated on agar. This step is crucial as it allows for a more accurate determination of colony forming units (CFU/mL). After plating, the samples are incubated under controlled conditions and the colonies are counted as soon as they are visible. The CFU/mL is then calculated taking into account the dilution factor (DF) and the volume of the plated samples. To analyze the growth dynamics, the collected data is logarithmically transformed and displayed graphically. This helps to visualize the different growth phases: the lag, exponential, stationary and dying phases. Each of these phases provides valuable insights into the growth behavior of the fungus. For example, the delayed lag phase indicates adaptation to new conditions, while the exponential phase reflects rapid cell division. Statistical analysis allows you to evaluate differences in growth under different experimental conditions, such as changes in temperature, pH or nutrient availability. This rigorous method provides an accurate and comprehensive understanding of fungal growth dynamics over time, allowing researchers to draw meaningful conclusions about the physiology and ecology of fungi, as well as potential applications in biotechnology or medicine. Ultimately, growth curve analysis is an important tool in mycology that facilitates the study of fungal behavior in different environments (Salari, Salari, Electron Physician 9:3592–3597, 2017).

Anticandidal activity of a wild Bacillus subtilis NAM against clinical isolates of pathogenic Candida albicans

Background Resistance to antifungal medications poses a significant obstacle in combating fungal infections. The development of novel therapeutics for Candida albicans is necessary due to the increasing resistance of candidiasis to the existing medications. The utilization of biological control is seen as a more advantageous and less hazardous strategy therefore the objective of this study is to identify the antifungal properties of Bacillus subtilis against pathogenic C. albicans. Results We conducted a study to evaluate the antifungal properties of three bacterial isolates against the human pathogen Candida albicans . One of the bacterial isolates exhibited a potent antifungal activity against this fungal pathogen. This bacterium was identified as Bacillus subtilis based on the 16Sr RNA gene sequence. It exhibited inhibitory efficacy ranging from 33.5 to 44.4% against 15 Candida isolates. The optimal incubation duration for achieving the maximum antifungal activity was determined to be 48 h, resulting in a mean inhibition zone diameter of 29 ± 0.39 mm. The Potato Dextrose agar (PDA) medium was the best medium for the most effective antifungal activity. Incubation temperature of 25^oC and medium pH value of 8.0 were the most favorable conditions for maximum antagonistic activity that resulted fungal growth inhibition of 40 ± 0.16 and 36 ± 0.94 mm respectively. Furthermore, the addition of 10.5 mg/ml of bacterial filtrate to C. albicans colonies resulted in 86.51%. decrease in the number of germinated cells. The fungal cell ultrastructural responses due to exposure to B. subtilis filtrate after 48 h were investigated using transmission electron microscopy (TEM). It revealed primary a drastic abnormality that lead to cellular disintegration including folding and lysis of the cell wall, total collapse of the yeast cells, and malformed germ tube following the exposure to the filtrate. However, the control culture treatment had a characteristic morphology of the normal fungal cells featuring a consistently dense central region, a well-organized nucleus, and a cytoplasm containing several components of the endomembrane system. The cells were surrounded by a uniform and intact cell wall. Conclusion The current study demonstrates a notable antifungal properties of B. subtilis against C. albicans as a result of production of bioactive components of the bacterial exudate. This finding could be a promising natural antifungal agent that could be utilized to combat C. albicans .

Addressing Critical Fungal Pathogens Under a One Health Perspective: Key Insights from the Portuguese Association of Medical Mycology

Fungal infections have emerged as a significant public health concern, especially with the increasing incidence of severe mycoses caused by pathogens such as Aspergillus fumigatus , Candida auris , Candida albicans , and Cryptococcus neoformans . These fungi, listed as critical priorities by the World Health Organization, pose a heightened risk due to rising antifungal resistance and their severe impact on immunocompromised individuals. This article, coordinated by the Portuguese Association of Medical Mycology, highlights the importance of adopting a One Health perspective to address fungal threats comprehensively. Drawing on interdisciplinary collaboration, the association aims to foster greater awareness, improve diagnostic capabilities, and stimulate research and public health policies in Portugal but also at global level. The paper outlines key strategies for surveillance, prevention, and innovation in fungal diagnostics and therapeutics. Moreover, it emphasizes the urgent need for national coordination and international cooperation in managing fungal infections, advocating for integrative approaches that link human, animal, and environmental health. By presenting a consolidated overview of current challenges and future priorities, this work seeks to enhance preparedness and response mechanisms in the face of escalating fungal threats.

Investigation of cross-opsonic effect leads to the discovery of PPIase-domain containing protein vaccine candidate to prevent infections by Gram-positive ESKAPE pathogens

Background Enterococcus faecium and Staphylococcus aureus are the Gram-positive pathogens of the ESKAPE group, known to represent a great threat to human health due to their high virulence and multiple resistances to antibiotics. Combined, enterococci and S. aureus account for 26% of healthcare-associated infections and are the most common organisms responsible for blood stream infections. We previously showed that the peptidyl-prolyl cis/trans isomerase (PPIase) PpiC of E. faecium elicits the production of specific, opsonic, and protective antibodies that are effective against several strains of E. faecium and E. faecalis . Due to the ubiquitous characteristics of PPIases and their essential function within Gram-positive cells, we hypothesized a potential cross-reactive effect of anti-PpiC antibodies. Results Opsonophagocytic assays combined with bioinformatics led to the identification of the foldase protein PrsA as a new potential vaccine antigen in S. aureus . We show that PrsA is a stable dimeric protein able to elicit opsonic antibodies against the S. aureus strain MW2, as well as cross-binding and cross-opsonic in several S. aureus , E. faecium and E. faecalis strains. Conclusions Given the multiple antibiotic resistances S. aureus and enterococci present, finding preventive strategies is essential to fight those two nosocomial pathogens. The study shows the potential of PrsA as an antigen to use in vaccine formulation against the two dangerous Gram-positive ESKAPE bacteria. Our findings support the idea that PPIases should be further investigated as vaccine targets in the frame of pan-vaccinomics strategy.

Epiphytic lichens in old castle parks in Southwest Germany: A case study

Old cultural landscapes, such as old parks and gardens, can be expected to be rich lichen habitats, as they offer otherwise rare microstructures and a high habitat continuity. Yet, old parks are not well studied with regard to their lichen flora. In this study, we investigated three old castle parks in southwest Germany and recorded the epiphytic (bark-inhabiting) lichen flora on 205 native and non-native trees, belonging to 63 tree species. We surveyed lichens on tree stems and assessed tree diameter, bark furrow depth, bark type, and bark pH. We investigated species diversity patterns using a rarefaction/extrapolation approach and community composition using non-metric multidimensional scaling (NMDS). Then, we analysed patterns in species richness and community composition using generalized linear mixed effect models (GLMMs) and a permutation-based, multivariate ANOVA. Species richness was mainly affected by tree species identity and tree functional group, with highest species numbers recorded for Tilia spp., and deciduous trees hosting significantly more species than evergreen trees. Community composition was mainly affected by tree species identity and marginally by bark pH and bark furrow depth. Most lichen species were generally common epiphytes, with many species being indicative for eutrophication or high temperatures. In contrast to our expectation, rare or red-listed species were only rarely encountered. In summary, our results show that the epiphytic lichen communities in old parks are shaped by internal factors (i.e. tree species identity, tree microhabitats) and external factors (i.e. eutrophication, macroclimate). Based on our study results, we recommend maintaining a high (deciduous) tree species diversity, as different tree species hosted different lichen communities.

Fast Inhibition Slows and Desynchronizes Mouse Auditory Efferent Neuron Activity

The encoding of acoustic stimuli requires precise neuron timing. Auditory neurons in the cochlear nucleus (CN) and brainstem are well suited for accurate analysis of fast acoustic signals, given their physiological specializations of fast membrane time constants, fast axonal conduction, and reliable synaptic transmission. The medial olivocochlear (MOC) neurons that provide efferent inhibition of the cochlea reside in the ventral brainstem and participate in these fast neural circuits. However, their modulation of cochlear function occurs over time scales of a slower nature. This suggests the presence of mechanisms that reduce MOC inhibition of cochlear function. To determine how monaural excitatory and inhibitory synaptic inputs integrate to affect the timing of MOC neuron activity, we developed a novel in vitro slice preparation (“wedge-slice”). The wedge-slice maintains the ascending auditory nerve root, the entire CN and projecting axons, while preserving the ability to perform visually guided patch-clamp electrophysiology recordings from genetically identified MOC neurons. The “in vivo-like” timing of the wedge-slice demonstrates that the inhibitory pathway accelerates relative to the excitatory pathway when the ascending circuit is intact, and the CN portion of the inhibitory circuit is precise enough to compensate for reduced precision in later synapses. When combined with machine learning PSC analysis and computational modeling, we demonstrate a larger suppression of MOC neuron activity when the inhibition occurs with in vivo-like timing. This delay of MOC activity may ensure that the MOC system is only engaged by sustained background sounds, preventing a maladaptive hypersuppression of cochlear activity.

Brain Biopsies and Cerebrospinal Fluid Collection in Preclinical Rodent Models

Brain biopsies and cerebrospinal fluid collection are frequently used in neuroscience research and require precise execution. The objective of this chapter was to provide updated protocols for brain biopsies and other stereotactic procedures in adult mice. The described technique employing a stereotactic arm enables greater precision than freehand techniques. Stereotactic procedures in adult mice can be performed expeditiously and demonstrate high efficacy in reaching the target site. Efficacy is contingent upon the complexity of precisely targeting the biopsy site, owing to the diminutive size of the animal. This chapter describes an innovative, efficient, and reproducible surgical protocol for brain biopsy and cerebrospinal fluid collection in adult mice.

Saussure, Bergson, and the Future of Literary Theory

Suppose twentieth-century literary theory had been based on the works of Henri Bergson rather than Ferdinand de Saussure. Would structuralist and poststructuralist theories of language have withstood a Bergsonian critique? Would the institution of literary study be radically different from what it is today? What does this suggest about the future of literary theory?

Quantitative Systems Pharmacology Development and Application in Neuroscience

Successful clinical development of therapeutics in neurology and psychiatry is challenging due to the complexity of the brain, the lack of validated surrogate markers and the nature of clinical assessments. On the other hand, tremendous advances have been made in unraveling the neurophysiology of the human brain thanks to technical developments in noninvasive biomarkers in both healthy and pathological conditions. Quantitative systems pharmacology (QSP) aims to integrate this increasing knowledge into a mechanistic model of key biological processes that drive clinical phenotypes with the objective to support research and development of successful therapies. This chapter describes both modeling of molecular pathways resulting in measurable biomarker changes, similar to modeling in other indications, as well as extrapolating in a mechanistic way these biomarker outcomes to predict changes in relevant functional clinical scales. Simulating the effect of therapeutic interventions on clinical scales uses the modeling methodology of computational neurosciences, which is based on the premise that human behavior is driven by firing activity of specific neuronal networks. While driven by pathology, the clinical behavior can also be influenced by various medications and common genotype variants. To address this occurrence, computational neuropharmacology QSP models can be developed and, in principle, applied as virtual twins, which are in silico clones of real patients. Overall, central nervous system (CNS) QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice. Overall, CNS QSP is an important additional tool for supporting research and development from the preclinical stage to post-marketing studies and clinical practice.

The Promise and Pitfalls of Studying the Neurophysiological Correlates of Automatic Imitation

The automatic copying of other people—automatic imitation—is one of the most widely studied topics in psychology. In this chapter, we review current evidence for the neurophysiological correlates of automatic imitation. To do so, we focus on one heavily used and influential paradigm that manipulates stimulus-response compatibility and is conducive to investigation using neuroscientific methods. We distinguish between two distinct mental processes that occur during imitation: action representation and action selection. Observed actions are perceived and represented and a particular action needs to be selected to be executed. Neuroscientific evidence concerning action representation appears relatively robust and engages a widespread and distributed network of visual and motor regions that span the ventral visual stream, as well as frontoparietal cortex. In contrast, the neurophysiological correlates of action selection during imitation are far from clear. The dominant view in the literature is that this particular task indexes processes relating to the control of automatic imitative tendencies, which rely on a self-other distinction mechanism that is uniquely tied to human social interaction and engages the theory-of-mind network. However, our analysis shows that this claim lacks essential evidence for four forms of validity (construct, internal, external, and statistical-conclusion validities). Instead, given current evidence, the best estimate is that this task engages domain-general forms of control that are underpinned by the multiple-demand network. For claims to be supported regarding socially specific forms of control when using this task, there is a burden of proof on researchers to show robust evidence for each of the four validities that we have outlined. Fortunately, with the emergence of the meta-science movement over the past 10 years, there are more resources than ever to help achieve this aim. More generally, even though we focus on one imitation task to provide a thorough test-case example, given the widespread and well-established lack of validity in psychology in general, we fully expect our analysis to be relevant across the full range of imitation tasks covered in this book, including imitation of speech, gestures, and emotions.

Constitutive and Conditional Epitope Tagging of Endogenous G-Protein–Coupled Receptors in Drosophila

To visualize the cellular and subcellular localization of neuromodulatory G-protein–coupled receptors in Drosophila, we implement a molecular strategy recently used to add epitope tags to ionotropic receptors at their endogenous loci. Leveraging evolutionary conservation to identify sites more likely to permit insertion of a tag, we generated constitutive and conditional tagged alleles for Drosophila 5-HT1A, 5-HT2A, 5-HT2B, Octβ1R, Octβ2R, two isoforms of OAMB, and mGluR. The conditional alleles allow for the restricted expression of tagged receptor in specific cell types, an option not available for any previous reagents to label these proteins. We show expression patterns for these receptors in female brains and that 5-HT1A and 5-HT2B localize to the mushroom bodies (MBs) and central complex, respectively, as predicted by their roles in sleep. By contrast, the unexpected enrichment of Octβ1R in the central complex and of 5-HT1A and 5-HT2A to nerve terminals in lobular columnar cells in the visual system suggest new hypotheses about their functions at these sites. Using an additional tagged allele of the serotonin transporter, a marker of serotonergic tracts, we demonstrate diverse spatial relationships between postsynaptic 5-HT receptors and presynaptic 5-HT neurons, consistent with the importance of both synaptic and volume transmission. Finally, we use the conditional allele of 5-HT1A to show that it localizes to distinct sites within the MBs as both a postsynaptic receptor in Kenyon cells and a presynaptic autoreceptor.

Neuropredictive Models of Personality Pathology

Personality disorders are characterized by persistent deviations in inner experience and behavior that do not align with societal norms, according to the American Psychiatric Association (2013). These conditions significantly affect the quality of life and give rise to problems in emotion regulation, strong impulsivity, identity disturbances, and other severe symptoms. The aim of this chapter is to present recent advancements in the understanding of the neural bases of personality pathology and the possibility to extract neuropredictive models of such disorders. To begin with, the reader will be introduced to some of the available machine learning methods used inside the field of personality neuroscience. Then specific personality pathologies will be presented: borderline, narcissistic, and antisocial in the form of a dark triad of personality. Finally, trait anxiety defined as a stable personality condition will be discussed. By integrating findings from neuroimaging methods and artificial intelligence techniques, this chapter aims to highlight the potential of neuropredictive models to revolutionize the diagnosis, treatment, and theoretical conceptualization of personality disorders.

Arterial Drug Delivery in the Future: Addressing Complex Problems as Acute Ischemic Stroke

We’ve long viewed neurological injuries as a black box. Our treatment outcome assessments are often binary, overlooking the many pathogenic mechanisms and their modulators that influence results, such as genetic factors, age, and comorbidities. Even for a given drug, there are numerous variables to consider, including pharmacodynamic specificity, dose, timing, and the effectiveness of the delivery method. This complexity creates a “complex problem mess” that is daunting and intriguing for research. It’s time to develop strategies to track the evolution of neuropathology, ensure drug delivery, and assess the treatment’s impact on the specific injury pathway rather than the net outcome of injury. This chapter describes how we could treat complex neurological diseases by delving into the black box of pathology by using specific injury-guided arterial interventions.

Inhibition of the cGAS‑STING Pathway Reduces Cisplatin-Induced Inner Ear Hair Cell Damage

Although cisplatin is a widely used chemotherapeutic agent, it is severely toxic and causes irreversible hearing loss, restricting its application in clinical settings. This study aimed to determine the molecular mechanism underlying cisplatin-induced ototoxicity. Here, we established in vitro and in vivo ototoxicity models of cisplatin-induced hair cell loss, and our results showed that reducing STING levels decreased inflammatory factor expression and hair cell death. In addition, we found that cisplatin-induced mitochondrial dysfunction was accompanied by cytosolic DNA, which may act as a critical linker between the cyclic GMP-AMP synthesis−stimulator of interferon genes (cGAS-STING) pathway and the pathogenesis of cisplatin-induced hearing loss. H-151, a specific inhibitor of STING, reduced hair cell damage and ameliorated the hearing loss caused by cisplatin in vivo. This study underscores the role of cGAS-STING in cisplatin ototoxicity and presents H-151 as a promising therapeutic for hearing loss. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01334-8.

Pain Catastrophizing: Features and Profiles in Chronic Migraine

Migraine is a prevalent neurological disorder which encompasses severe headaches accompanied by various sensory sensitivities. When it becomes chronic, characterized by frequent attacks, it can be a very disabling disease with significant social and economic burdens. The typical treatment options for chronic migraine are pharmacological and non-pharmacological, with mixed and unsatisfying outcomes. Indeed, the effective management involves identifying triggers, modifying risk factors, and balancing pharmacological and non-pharmacological treatments to minimize medication overuse and address other possible associated factors, included mental health issues. In this chapter, we will analyze the topic of catastrophizing in migraine. Catastrophizing is a psychological characteristic of migraineurs. It consists of a disadaptive mindset made of irrational beliefs where negative events are exaggerated leading to heightened emotional distress and a belief in worst-case scenarios, while doubting one’s ability to cope. Catastrophic thinking can result in feelings of helplessness, depression, and anxiety, representing an extreme form of worrying that extends into pathological symptoms. Many literature studies documented catastrophizing in migraine, in both clinical and experimental settings, and some studies also revealed that some social variables, such as stigma for chronic illness, can mediate the relationship between migraine symptoms and catastrophizing. This negative mindset impacts not only adults but also children with migraines, with consequences on their overall quality of life, and parental catastrophizing can worsen their tendency toward catastrophic thoughts. Recently, also neuroscience investigated the relation between migraine and catastrophizing, finding changes in brain connectivity and central sensitization mechanisms as possible contributors. Nevertheless, the exact underlying mechanisms remain incompletely understood. To date, pharmacological treatments such as erenumab and OnabotulinumtoxinA and non-pharmacological treatments such as neurostimulation or behavioral approaches showed efficacy in relieving both migraine symptoms and catastrophizing, sometimes revealing some placebo effects. In this case as well, further studies delving into techniques such as mindfulness, psychoeducational interventions, and cognitive-behavioral therapy will be useful in determining the effectiveness of these treatments. To conclude, migraine is not the only chronic pain disease characterized by the catastrophic thinking. Indeed, many studies documented the presence of this cognitive feature in rheumatic diseases such as fibromyalgia, or in painful neuropathies. Also in this case, neuroimaging helped in revealing some cortical abnormalities which may contribute to the relation between catastrophizing and chronic pain symptoms. Further studies are needed to explore this relation while psychological intervention aimed at this cognitive schema would be helpful for patients in getting out from the negative loop of catastrophizing and awfulizing, possibly developing a more optimistic outlook, or at least acceptance, toward debilitating conditions such as those characterized by chronic pain.

Neurotransmitters: Foundations of Cognition

This chapter delves into the foundational aspects of neuroscience, focusing on the neurological correlates of consciousness and their relevance to spiritual wellness. It explores how the quantification of mindfulness and consciousness can contribute to overall well-being through integrative healthcare approaches. The chapter further examines the role of neurotransmitters in influencing cognitive abilities and mental health, highlighting the methods used in the literature to detect and measure low levels of neurotransmitters. This discussion underscores the intersection of neuroscience and spirituality, emphasizing the importance of understanding these biological processes to enhance mental and spiritual health.

Effects of Breast Cancer Treatment on Neural Noise: a Longitudinal Design

OBJECTIVE: Cognitive dysfunction has been observed consistently in a subset of breast cancer survivors. Yet the precise neurophysiological origins of cancer-related cognitive decline remain unknown. The current study assessed neural noise (1/f activity in electroencephalogram [EEG]) in breast cancer survivors as a potential contributor to observed cognitive dysfunction from pre- to post-treatment. METHODS: We measured EEG in a longitudinal design during performance of the paired-click task and the revised Attention Network Test (ANT-R) to investigate pre- versus post-treatment effects of neural noise in breast cancer patients (n = 20 in paired click; n = 19 in ANT-R) compared with healthy controls (n = 32 in paired click; n = 29 in ANT-R). RESULTS: In both paradigms, one sensory (paired click) and one cognitive (ANT-R), we found that neural noise was significantly elevated after treatment in patients, remaining constant from pretest to posttest in controls. In the ANT-R, patients responded more slowly than controls on invalid cuing trials. Increased neural noise was associated with poorer alerting and poorer inhibitory control of attention (as measured by behavioral network scores), particularly for patients after treatment. CONCLUSIONS: The current study is the first to show a deleterious effect of breast cancer and/or cancer treatment on neural noise, pointing to alterations in the relative balance of excitatory and inhibitory synaptic inputs, while also suggesting promising approaches for cognitive rehabilitation.

Electrical Synapses Mediate Embryonic Hyperactivity in a Zebrafish Model of Fragile X Syndrome

Although hyperactivity is associated with a wide variety of neurodevelopmental disorders, the early embryonic origins of locomotion have hindered investigation of pathogenesis of these debilitating behaviors. The earliest motor output in vertebrate animals is generated by clusters of early-born motor neurons (MNs) that occupy distinct regions of the spinal cord, innervating stereotyped muscle groups. Gap junction electrical synapses drive early spontaneous behavior in zebrafish, prior to the emergence of chemical neurotransmitter networks. We use a genetic model of hyperactivity to gain critical insight into the consequences of errors in motor circuit formation and function, finding that Fragile X syndrome model mutant zebrafish are hyperexcitable from the earliest phases of spontaneous behavior, show altered sensitivity to blockade of electrical gap junctions, and have increased expression of the gap junction protein Connexin 34/35. We further show that this hyperexcitable behavior can be rescued by pharmacological inhibition of electrical synapses. We also use functional imaging to examine MN and interneuron (IN) activity in early embryogenesis, finding genetic disruption of electrical gap junctions uncouples activity between mnx1(+) MNs and INs. Taken together, our work highlights the importance of electrical synapses in motor development and suggests that the origins of hyperactivity in neurodevelopmental disorders may be established during the initial formation of locomotive circuits.

Dissociable Representations of Decision Variables within Subdivisions of the Macaque Orbital and Ventrolateral Frontal Cortex

The ventral frontal cortex (VFC) in macaques is involved in many affective and cognitive processes and has a key role in flexibly guiding reward-based decision-making. VFC is composed of a set of anatomically distinct subdivisions that are within the orbitofrontal cortex, ventrolateral prefrontal cortex, and anterior insula. In part, because prior studies have lacked the resolution to test for differences, it is unclear if neural representations related to decision-making are dissociable across these subdivisions. Here we recorded the activity of thousands of neurons within eight anatomically defined subdivisions of VFC in male macaque monkeys performing a two-choice probabilistic task for different fruit juice outcomes. We found substantial variation in the encoding of decision variables across these eight subdivisions. Notably, ventrolateral Area 12l was unique relative to the other areas that we recorded from as the activity of single neurons integrated multiple attributes when monkeys evaluated the different choice options. Activity within Area 12o, in contrast, more closely represented reward probability and whether reward was received on a given trial. Orbitofrontal Area 11m/l contained more specific representations of the quality of the outcome that could be earned later on. We also found that reward delivery encoding was highly distributed across all VFC subdivisions, while the properties of the reward, such as its flavor, were more strongly represented in Areas 11m/l and 13m. Taken together, our work reveals the diversity of encoding within the various anatomically distinct subdivisions of VFC in primates.

Acute Ongoing Nociception Delays Recovery of Consciousness from Sevoflurane Anesthesia via a Midbrain Circuit

Although anesthesia provides favorable conditions for surgical procedures, recent studies have revealed that the brain remains active in processing noxious signals even during anesthesia. However, whether and how these responses affect the anesthesia effect remains unclear. The ventrolateral periaqueductal gray (vlPAG), a crucial hub for pain regulation, also plays an essential role in controlling general anesthesia. Hence, it was hypothesized that the vlPAG may be involved in the regulation of general anesthesia by noxious stimuli. Here, we found that acute noxious stimuli, including capsaicin-induced inflammatory pain, acetic acid-induced visceral pain, and incision-induced surgical pain, significantly delayed recovery from sevoflurane anesthesia in male mice, whereas this effect was absent in the spared nerve injury-induced chronic pain. Pretreatment with peripheral analgesics could prevent the delayed recovery induced by acute nociception. Furthermore, we found that acute noxious stimuli, induced by the injection of capsaicin under sevoflurane anesthesia, increased c-Fos expression and activity in the GABAergic neurons of the ventrolateral periaqueductal gray. Specific reactivation of capsaicin-activated vlPAG(GABA) neurons mimicked the effect of capsaicin and its chemogenetic inhibition prevented the delayed recovery from anesthesia induced by capsaicin. Finally, we revealed that the vlPAG(GABA) neurons regulated the recovery from anesthesia through the inhibition of ventral tegmental area dopaminergic neuronal activity, thus decreasing dopamine (DA) release and activation of DA D(1)-like receptors in the brain. These findings reveal a novel, cell- and circuit-based mechanism for regulating anesthesia recovery by nociception, and it is important to provide new insights for guiding the management of the anesthesia recovery period.

The Vestibulospinal Nucleus Is a Locus of Balance Development

Mature vertebrates maintain posture using vestibulospinal neurons that transform sensed instability into reflexive commands to spinal motor circuits. Postural stability improves across development. However, due to the complexity of terrestrial locomotion, vestibulospinal contributions to postural refinement in early life remain unexplored. Here we leveraged the relative simplicity of underwater locomotion to quantify the postural consequences of losing vestibulospinal neurons during development in larval zebrafish of undifferentiated sex. By comparing posture at two timepoints, we discovered that later lesions of vestibulospinal neurons led to greater instability. Analysis of thousands of individual swim bouts revealed that lesions disrupted movement timing and corrective reflexes without impacting swim kinematics, and that this effect was particularly strong in older larvae. Using a generative model of swimming, we showed how these disruptions could account for the increased postural variability at both timepoints. Finally, late lesions disrupted the fin/trunk coordination observed in older larvae, linking vestibulospinal neurons to postural control schemes used to navigate in depth. Since later lesions were considerably more disruptive to postural stability, we conclude that vestibulospinal contributions to balance increase as larvae mature. Vestibulospinal neurons are highly conserved across vertebrates; we therefore propose that they are a substrate for developmental improvements to postural control.

Cerebral Gray Matter May Not Explain Sleep Slow-Wave Characteristics after Severe Brain Injury

Sleep slow waves are the hallmark of deeper non-rapid eye movement sleep. It is generally assumed that gray matter properties predict slow-wave density, morphology, and spectral power in healthy adults. Here, we tested the association between gray matter volume (GMV) and slow-wave characteristics in 27 patients with moderate-to-severe traumatic brain injury (TBI, 32.0 ± 12.2 years old, eight women) and compared that with 32 healthy controls (29.2 ± 11.5 years old, nine women). Participants underwent overnight polysomnography and cerebral MRI with a 3 Tesla scanner. A whole-brain voxel–wise analysis was performed to compare GMV between groups. Slow-wave density, morphology, and spectral power (0.4–6 Hz) were computed, and GMV was extracted from the thalamus, cingulate, insula, precuneus, and orbitofrontal cortex to test the relationship between slow waves and gray matter in regions implicated in the generation and/or propagation of slow waves. Compared with controls, TBI patients had significantly lower frontal and temporal GMV and exhibited a subtle decrease in slow-wave frequency. Moreover, higher GMV in the orbitofrontal cortex, insula, cingulate cortex, and precuneus was associated with higher slow-wave frequency and slope, but only in healthy controls. Higher orbitofrontal GMV was also associated with higher slow-wave density in healthy participants. While we observed the expected associations between GMV and slow-wave characteristics in healthy controls, no such associations were observed in the TBI group despite lower GMV. This finding challenges the presumed role of GMV in slow-wave generation and morphology.

Dorsoventral Heterogeneity of Synaptic Connectivity in Hippocampal CA3 Pyramidal Neurons

The hippocampal CA3 region plays an important role in learning and memory. CA3 pyramidal neurons (PNs) receive two prominent excitatory inputs—mossy fibers (MFs) from dentate gyrus (DG) and recurrent collaterals (RCs) from CA3 PNs—that play opposing roles in pattern separation and pattern completion, respectively. Although the dorsoventral heterogeneity of the hippocampal anatomy, physiology, and behavior has been well established, nothing is known about the dorsoventral heterogeneity of synaptic connectivity in CA3 PNs. In this study, we performed Timm's sulfide silver staining, dendritic and spine morphological analyses, and ex vivo electrophysiology in mice of both sexes to investigate the heterogeneity of MF and RC pathways along the CA3 dorsoventral axis. Our morphological analyses demonstrate that ventral CA3 (vCA3) PNs possess greater dendritic lengths and more complex dendritic arborization, compared with dorsal CA3 (dCA3) PNs. Moreover, using ChannelRhodopsin2 (ChR2)-assisted patch-clamp recording, we found that the ratio of the RC-to-MF excitatory drive onto CA3 PNs increases substantially from dCA3 to vCA3, with vCA3 PNs receiving significantly weaker MFs, but stronger RCs, excitation than dCA3 PNs. Given the distinct roles of MF versus RC inputs in pattern separation versus completion, our findings of the significant dorsoventral variations of MF and RC excitation in CA3 PNs may have important functional implications for the contribution of CA3 circuit to the dorsoventral difference in hippocampal function.

Small Differences and Big Changes: The Many Variables of MicroRNA Expression and Function in the Brain

MicroRNAs are emerging as crucial regulators within the complex, dynamic environment of the synapse, and they offer a promising new avenue for the treatment of neurological disease. These small noncoding RNAs modify gene expression in several ways, including posttranscriptional modulation via binding to complementary and semicomplementary sites on target mRNAs. This rapid, finely tuned regulation of gene expression is essential to meet the dynamic demands of the synapse. Here, we provide a detailed review of the multifaceted world of synaptic microRNA regulation. We discuss the many mechanisms by which microRNAs regulate gene expression at the synapse, particularly in the context of neuronal plasticity. We also describe the various factors, such as age, sex, and neurological disease, that can influence microRNA expression and activity in neurons. In summary, microRNAs play a crucial role in the intricate and quickly changing functional requirements of the synapse, and context is essential in the study of microRNAs and their potential therapeutic applications.

Characterization of Mice Carrying a Neurodevelopmental Disease-Associated GluN2B(L825V) Variant

N-Methyl-d-aspartate receptors (NMDARs), encoded by GRIN genes, are ionotropic glutamate receptors playing a critical role in synaptic transmission, plasticity, and synapse development. Genome sequence analyses have identified variants in GRIN genes in patients with neurodevelopmental disorders, but the underlying disease mechanisms are not well understood. Here, we have created and evaluated a transgenic mouse line carrying a missense variant Grin2b(L825V), corresponding to a de novo GRIN2B variant encoding GluN2B(L825V) found in a patient with intellectual disability (ID) and autism spectrum disorder (ASD). We used HEK293T cells expressing recombinant receptors and primary hippocampal neurons prepared from heterozygous Grin2b(L825V/+) (L825V/+) and wild-type (WT) Grin2b(+/+) (+/+) male and female mice to assess the functional impact of the variant. Whole-cell NMDAR currents were reduced in neurons from L825V/+ compared with +/+ mice. The peak amplitude of NMDAR-mediated evoked excitatory postsynaptic currents (NMDAR-eEPSCs) was unchanged, but NMDAR-eEPSCs in L825V/+ neurons had faster deactivation compared with +/+ neurons and were less sensitive to a GluN2B-selective antagonist ifenprodil. Together, these results suggest a decreased functional contribution of GluN2B subunits to synaptic NMDAR currents in hippocampal neurons from L825V/+ mice. The analysis of the GluN2B(L825V) subunit surface expression and synaptic localization revealed no differences compared with WT GluN2B. Behavioral testing of mice of both sexes demonstrated hypoactivity, anxiety, and impaired sensorimotor gating in the L825V/+ strain, particularly affecting males, as well as cognitive symptoms. The heterozygous L825V/+ mouse offers a clinically relevant model of GRIN2B-related ID/ASD, and our results suggest synaptic-level functional changes that may contribute to neurodevelopmental pathology.

Cognitive Load and Online Customer Decisions: The Role of Visual Perception, Memory, and Brain Activity

Due to the use of new technologies and ongoing digitization, there is a significant increase in the popularity of online stores. More and more, consumers appreciate the ease of online shopping, enjoying the ability to explore a diverse array of products from the comfort of their own homes. The wide range of products available in online stores allows consumers to precisely tailor their purchases to their individual preferences and needs. However, too much information on a page can lead to cognitive load, affecting visual attention and purchasing decisions. The article focuses on an innovative approach to measuring the impact of cognitive load on the purchasing decision-making process in online stores. The authors focus on psychological aspects, including reducing the influence of external factors, to obtain more reliable results. Understanding how cognitive load affects online shopping decisions is crucial for designing effective, user-friendly e-commerce platforms.

Endoplasmic Reticulum and Mitochondrial Calcium Handling Dynamically Shape Slow Afterhyperpolarizations in Vasopressin Magnocellular Neurons

Many neurons including vasopressin (VP) magnocellular neurosecretory cells (MNCs) of the hypothalamic supraoptic nucleus (SON) generate afterhyperpolarizations (AHPs) during spiking to slow firing, a phenomenon known as spike frequency adaptation. The AHP is underlain by Ca(2+)-activated K(+) currents, and while slow component (sAHP) features are well described, its mechanism remains poorly understood. Previous work demonstrated that Ca(2+) influx through N-type Ca(2+) channels is a primary source of sAHP activation in SON oxytocin neurons, but no obvious channel coupling was described for VP neurons. Given this, we tested the possibility of an intracellular source of sAHP activation, namely, the Ca(2+)-handling organelles endoplasmic reticulum (ER) and mitochondria in male and female Wistar rats. We demonstrate that ER Ca(2+) depletion greatly inhibits sAHPs without a corresponding decrease in Ca(2+) signal. Caffeine sensitized AHP activation by Ca(2+). In contrast to ER, disabling mitochondria with CCCP or blocking mitochondria Ca(2+) uniporters (MCUs) enhanced sAHP amplitude and duration, implicating mitochondria as a vital buffer for sAHP-activating Ca(2+). Block of mitochondria Na(+)-dependent Ca(2+) release via triphenylphosphonium (TPP(+)) failed to affect sAHPs, indicating that mitochondria Ca(2+) does not contribute to sAHP activation. Together, our results suggests that ER Ca(2+)-induced Ca(2+) release activates sAHPs and mitochondria shape the spatiotemporal trajectory of the sAHP via Ca(2+) buffering in VP neurons. Overall, this implicates organelle Ca(2+), and specifically ER–mitochondria-associated membrane contacts, as an important site of Ca(2+) microdomain activity that regulates sAHP signaling pathways. Thus, this site plays a major role in influencing VP firing activity and systemic hormonal release.

Hierarchical Gradients of Encoded Spatial and Sensory Information in the Neocortex Are Attenuated by Dorsal Hippocampal Lesions

During navigation, the neocortex actively integrates learned spatial context with current sensory experience to guide behaviors. However, the relative encoding of spatial and sensorimotor information among cortical cells, and whether hippocampal feedback continues to modify these properties after learning, remains poorly understood. Thus, two-photon microscopy of male and female Thy1-GCaMP6s mice was used to longitudinally image neurons spanning superficial retrosplenial cortex and layers II–Va of primary and secondary motor cortices before and after bilateral dorsal hippocampal lesions. During behavior on a familiar cued treadmill, the locations of two obstacles were interchanged to decouple place-tuning from cue-tuning among position-correlated cells with fields at those locations. Subpopulations of place and cue cells each formed interareal gradients such that higher-level cortical regions exhibited higher fractions of place cells, whereas lower-level regions exhibited higher fractions of cue cells. Position-correlated cells in the motor cortex also formed translaminar gradients; more superficial cells were more likely to exhibit fields and were more sparsely and precisely tuned than deeper cells. After dorsal hippocampal lesions, a neural representation of the learned environment persisted, but retrosplenial cortex exhibited significantly increased cue-tuning, and, in motor cortices, both position-correlated cell recruitment and population activity at the unstable obstacle locations became more homogeneously elevated across laminae. Altogether, these results support that the hippocampus continues to modulate cortical responses in familiar environments, and the relative impact of descending feedback obeys hierarchical interareal and interlaminar gradients opposite to the flow of ascending sensory inputs.

Deciphering the Role of Shank3 in Dendritic Morphology and Synaptic Function Across Postnatal Developmental Stages in the Shank3B KO Mouse

Autism Spectrum Disorder (ASD) is marked by early-onset neurodevelopmental anomalies, yet the temporal dynamics of genetic contributions to these processes remain insufficiently understood. This study aimed to elucidate the role of the Shank3 gene, known to be associated with monogenic causes of autism, in early developmental processes to inform the timing and mechanisms for potential interventions for ASD. Utilizing the Shank3B knockout (KO) mouse model, we examined Shank3 expression and its impact on neuronal maturation through Golgi staining for dendritic morphology and electrophysiological recordings to measure synaptic function in the anterior cingulate cortex (ACC) across different postnatal stages. Our longitudinal analysis revealed that, while Shank3B KO mice displayed normal neuronal morphology at one week postnatal, significant impairments in dendritic growth and synaptic activity emerged by two to three weeks. These findings highlight the critical developmental window during which Shank3 is essential for neuronal and synaptic maturation in the ACC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12264-024-01330-y.

A Historical and Global Overview of Neuropsychology

Neuropsychology had its semi-structured foundations in the eighteenth century with phrenology, a pseudoscience that proposed a relationship between facial characteristics and cognitive abilities. In the nineteenth century, neuropsychology established itself as a scientific discipline, with the work of Franz Joseph Gall and others. The localizationist perspective sought, during this period, to address brain areas responsible for specific functions, such as language and memory, through postmortem identification and correlation of individuals who clinically exhibited motor and language deficits associated with acquired brain lesions. In the twentieth century, advances in neuroimaging, such as computed tomography (CT) and magnetic resonance imaging (MRI), allowed for the visualization of the living brain and the study of real-time brain activity. Standardized neuropsychological tests were developed to assess specific cognitive functions, and new theoretical approaches, such as cognitive neuropsychology and social neuropsychology, emerged. In the twenty-first century, neuropsychology integrated with other disciplines, such as neuroscience, cognitive psychology, and neurology. The understanding of brain plasticity and the role of neuropsychological rehabilitation advanced significantly. Advanced neuroimaging techniques, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), are used to study brain activity during cognitive tasks. Ongoing developments include integration with artificial intelligence (AI) and machine learning, the development of new evidence-based therapeutic interventions for neuropsychological disorders, and the use of neuropsychology to enhance understanding and treatment of mental health conditions, such as depression and anxiety. This chapter aims to outline the historical development of neuropsychology, explore the main theoretical and methodological contributions that have shaped the discipline, and discuss contemporary and future advances in the field, highlighting the importance of neuroimaging techniques and integrated approaches for the study of the brain and cognition.

Electroacupuncture for motor dysfunction and constipation in patients with Parkinson's disease: a randomised controlled multi-centre trial

BACKGROUND: Motor disturbances and non-motor disturbances such as constipation are the main factors affecting the quality of life in patients with Parkinson's disease (PD). We investigated the efficacy and safety of electroacupuncture combined with conventional pharmacological treatment on motor dysfunction and constipation in PD. METHODS: In this multi-centre randomised controlled trial, we enrolled 166 eligible participants between September 19, 2018 and September 25, 2019 in four hospitals in China. Participants were randomly assigned (1:1) to the electroacupuncture (EA) group and the waitlist control group. Each participant in both groups received the conventional pharmacological treatment, EA group received 3 sessions of electroacupuncture per week for 12 weeks. The primary outcome was the change in the Unified Parkinson's Disease Rating Scale (UPDRS) score from baseline to week 12. The secondary outcomes included the evaluation of functional disability in motor symptoms and constipation, the adherence and adverse events were also recorded. Registered with Chictr.org.cn, ChiCTR1800019517. FINDINGS: At week 12, the change in the UPDRS score of the EA group was significantly higher than that of the control group, with a difference of −9.1 points (95% CI, −11.8 to −6.4), and this difference continued into weeks 16 and 24. From baseline to week 12, the 39-item Parkinson Disease Question (PDQ-39) decreased by 10 points (interquartile range, IQR −26.0 to 0.0) in the EA group and 2.5 points (IQR: −11.0 to 4.0) in the control group, the difference was statistically significant. The time and steps for the 20-m walk at week 12, as well as the changes from baseline in the EA group, were comparable with that in the control group. But the EA group had a greater decrease than the control group from baseline in the times for 20-m walks at weeks 16 and 24. From week 4 to week 24, the median values of spontaneous bowel movements (SBMs) per week in the EA group were higher than that in the control group, the differences were all statistically significant. The incidence of EA-related adverse events during treatment was low, and they are mild and transient. INTERPRETATION: The findings of our study suggested that compared with conventional pharmacological treatment, conventional pharmacological treatment combined with electroacupuncture significantly enhances motor function and increased bowel movements in patients with PD, electroacupuncture is a safe and effective treatment for PD. FUNDING: Shanghai “Science and Technology Innovation Action Plan” Clinical Medicine Field Project (18401970700), Shanghai Special Project on Aging and Women's and Children's Health Research (020YJZX0134), Shanghai Clinical Research Centre for Acupuncture and Moxibustion (20MC1920500).

Gastrointestinal Symptoms and Dopamine Transporter Asymmetry in Early Parkinson's Disease

BACKGROUND: The neurophysiological correlates of gastrointestinal symptoms (GISs) in Parkinson's disease (PD) are not well understood. It has been proposed that in patients with a gastrointestinal origin of PD dopaminergic neurodegeneration would be more symmetric. OBJECTIVES: The aim is to assess the associations between GISs and asymmetry of nigrostriatal dopaminergic neurodegeneration in PD. METHODS: Ninety PD patients were assessed using motor and GIS scales and (123)I‐FP‐CIT SPECT. We calculated the asymmetry index and the predominant side of motor symptoms and dopamine transporter (DAT) imaging defect and assessed their association with GISs. RESULTS: There were no significant differences in GISs between symmetric and asymmetric dopaminergic defect. Left predominant defect was related to more GIS and higher constipation scores. CONCLUSIONS: GISs were associated with left predominant reduction in putaminal DAT binding but not asymmetry per se. It remains open whether left‐sided DAT deficit is related to more pronounced GI involvement or symptom perception in PD. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson Movement Disorder Society.

Sphingolipid changes in Parkinson L444P GBA mutation fibroblasts promote α-synuclein aggregation

Intraneuronal accumulation of aggregated α-synuclein is a pathological hallmark of Parkinson’s disease. Therefore, mechanisms capable of promoting α-synuclein deposition bear important pathogenetic implications. Mutations of the glucocerebrosidase 1 (GBA) gene represent a prevalent Parkinson’s disease risk factor. They are associated with loss of activity of a key enzyme involved in lipid metabolism, glucocerebrosidase, supporting a mechanistic relationship between abnormal α-synuclein–lipid interactions and the development of Parkinson pathology. In this study, the lipid membrane composition of fibroblasts isolated from control subjects, patients with idiopathic Parkinson’s disease and Parkinson's disease patients carrying the L444P GBA mutation (PD-GBA) was assayed using shotgun lipidomics. The lipid profile of PD-GBA fibroblasts differed significantly from that of control and idiopathic Parkinson’s disease cells. It was characterized by an overall increase in sphingolipid levels. It also featured a significant increase in the proportion of ceramide, sphingomyelin and hexosylceramide molecules with shorter chain length and a decrease in the percentage of longer-chain sphingolipids. The extent of this shift was correlated to the degree of reduction of fibroblast glucocerebrosidase activity. Lipid extracts from control and PD-GBA fibroblasts were added to recombinant α-synuclein solutions. The kinetics of α-synuclein aggregation were significantly accelerated after addition of PD-GBA extracts as compared to control samples. Amyloid fibrils collected at the end of these incubations contained lipids, indicating α-synuclein–lipid co-assembly. Lipids extracted from α-synuclein fibrils were also analysed by shotgun lipidomics. Data revealed that the lipid content of these fibrils was significantly enriched by shorter-chain sphingolipids. In a final set of experiments, control and PD-GBA fibroblasts were incubated in the presence of the small molecule chaperone ambroxol. This treatment restored glucocerebrosidase activity and sphingolipid levels and composition of PD-GBA cells. It also reversed the pro-aggregation effect that lipid extracts from PD-GBA fibroblasts had on α-synuclein. Taken together, the findings of this study indicate that the L444P GBA mutation and consequent enzymatic loss are associated with a distinctly altered membrane lipid profile that provides a biological fingerprint of this mutation in Parkinson fibroblasts. This altered lipid profile could also be an indicator of increased risk for α-synuclein aggregate pathology.

α-Synuclein Pathology and Reduced Neurogenesis in the Olfactory System Affect Olfaction in a Mouse Model of Parkinson's Disease

Parkinson's disease (PD) is characterized by multiple symptoms including olfactory dysfunction, whose underlying mechanisms remain unclear. Here, we explored pathologic changes in the olfactory pathway of transgenic (Tg) mice of both sexes expressing the human A30P mutant α-synuclein (α-syn; α-syn-Tg mice) at 6–7 and 12–14 months of age, representing early and late-stages of motor progression, respectively. α-Syn-Tg mice at late stages exhibited olfactory behavioral deficits, which correlated with severe α-syn pathology in projection neurons (PNs) of the olfactory pathway. In parallel, olfactory bulb (OB) neurogenesis in α-syn-Tg mice was reduced in the OB granule cells at six to seven months and OB periglomerular cells at 12–14 months, respectively, both of which could contribute to olfactory dysfunction. Proteomic analyses showed a disruption in endocytic and exocytic pathways in the OB during the early stages which appeared exacerbated at the synaptic terminals when the mice developed olfactory deficits at 12–14 months. Our data suggest that (1) the α-syn-Tg mice recapitulate the olfactory functional deficits seen in PD; (2) olfactory structures exhibit spatiotemporal disparities for vulnerability to α-syn pathology; (3) α-syn pathology is restricted to projection neurons in the olfactory pathway; (4) neurogenesis in adult α-syn-Tg mice is reduced in the OB; and (5) synaptic endocytosis and exocytosis defects in the OB may further explain olfactory deficits. SIGNIFICANCE STATEMENT Olfactory dysfunction is a characteristic symptom of Parkinson's disease (PD). Using the human A30P mutant α-synuclein (α-syn)-expressing mouse model, we demonstrated the appearance of olfactory deficits at late stages of the disease, which was accompanied by the accumulation of α-syn pathology in projection neurons (PNs) of the olfactory system. This dysfunction included a reduction in olfactory bulb (OB) neurogenesis as well as changes in synaptic vesicular transport affecting synaptic function, both of which are likely contributing to olfactory behavioral deficits.

Decision making under uncertainty in Parkinson's disease with Rem sleep behavior disorder

International audience; Background; REM sleep behavior disorder (RBD) is associated with an increased risk to develop Impulse control disorders (ICDs) in Parkinson's disease (PD), however the mechanisms underlying this putative association are still poorly understood. Decision-making impairment, one major neuro-psychological dimension that may lead to ICDs, has been reported in idiopathic RBD, but has never been assessed in RBD associated with PD.Objective: We aimed to assess decision-making abilities under ambiguous situations associated with the presence of RBD in PD patients.Methods: In this cross-sectional study, 60 non-demented PD patients with (PD-RBD, n = 40) and without (PD-nRBD, n = 20) video polysomnography-confirmed RBD, and 20 healthy controls matched for gender and age were included. All subjects underwent neurological and neuropsychological examination, including Iowa Gambling task designed to asses decision-making under uncertainty.Results: IGT total score did not differ between groups (p = 0.851), however PD-RBD presented, more disadvantageous choices for the last blocks of IGT compared to PD-nRBD (p = 0.001) and to HC (p = 0.012). Progression of scores did not differ between HC and PD-nRBD. Multivariate analyses taking into account the value of scores at baseline, as well as the duration of PD, the duration of treatment, the presence of ICDs and MMSE confirmed those results.Conclusion: Decision making under uncertainty is impaired in PD-RBD compared to PD-nRBD and healthy controls, regardless to the duration of disease, treatment, cognitive status and the presence of ICDs. This could reflect an inability to learn from punishment or reward in PD-RBD, and could explain the increased risk to develop ICDs reported in those patients..

Patient-Specific Game-Based Transfer Method for Parkinson's Disease Severity Prediction

Dysphonia is one of the early symptoms of Parkinson's disease (PD). Most existing methods use feature selection methods to find the optimal subset of voice features for all PD patients. Few have considered the heterogeneity between patients, which implies the need to provide specific prediction models for different patients. However, building the specific model faces the challenge of small sample size, which makes it lack generalization ability. Instance transfer is an effective way to solve this problem. Therefore, this paper proposes a patient-specific game-based transfer (PSGT) method for PD severity prediction. First, a selection mechanism is used to select PD patients with similar disease trends to the target patient from the source domain, which greatly reduces the risk of negative transfer. Then, the contribution of the transferred subjects and their instances to the disease estimation of the target subject is fairly evaluated by the Shapley value, which improves the interpretability of the method. Next, the proportion of valid instances in the transferred subjects is determined, and the instances with higher contribution are transferred to further reduce the difference between the transferred instance subset and the target subject. Finally, the selected subset of instances is added to the training set of the target subject, and the extended data is fed into the random forest to improve the performance of the method. Parkinson's telemonitoring dataset is used to evaluate the feasibility and effectiveness. Experiment results show that the PSGT has better performance in both prediction error and stability over compared methods.

Eight‐year follow‐up outcome of subthalamic deep brain stimulation for Parkinson’s disease: Maintenance of therapeutic efficacy with a relatively low levodopa dosage and stimulation intensity

AIMS: This follow‐up study aimed to examine the 8‐year efficacy and safety of subthalamic nucleus (STN) deep brain stimulation (DBS) for patients with Parkinson's disease (PD) in southern China. METHODS: The follow‐up data of 10 patients with PD undergoing STN‐DBS were analyzed. Motor symptoms were assessed before and 1, 3, 5, and 8 years after the surgery with stimulation‐on in both off‐medication (off‐med) and on‐medication (on‐med) status using the Unified Parkinson's disease Rating Scale Part III. The quality of life was assessed using the 39‐item Parkinson's Disease Questionnaire. The sleep, cognition, and emotion were evaluated using a series of nonmotor scales. Levodopa equivalent daily dose (LEDD) and stimulation parameters were recorded at each follow‐up. RESULTS: The motor symptoms were improved by 50.9%, 37.7%, 36.7%, and 37.3% in 1, 3, 5, and 8 years, respectively, in the off‐med / stimulation‐on status compared with the baseline. The quality of life improved by 39.7% and 56.1% in 1 and 3 years, respectively, but declined to the preoperative level thereafter. The sleep, cognition, and emotion were mostly unchanged. LEDD reduced from 708.1 ± 172.5 mg to 330 ± 207.8 mg in 8 years. The stimulation parameters, including amplitude, pulse width, and frequency, were 2.77 ± 0.49 V, 71.3 ± 12.8 μs, and 121.5 ± 21 Hz, respectively, in 8 years. CONCLUSION: Long‐term therapeutic efficacy of STN‐DBS could be achieved even with relatively low stimulation intensity and medication dosage for PD patients in southern China. Motor improvement and medication reduction were maintained through the 8‐year follow‐up, but improvement in quality of life lasted for only 3 years. No definite changes was found in nonmotor symptoms after STN‐DBS.

Functional Analyses of Two Novel LRRK2 Pathogenic Variants in Familial Parkinson's Disease.