Publicaciones científicas recientes por área de investigación

Large Language Model-Based Agents for Automated Research Reproducibility: An Exploratory Study in Alzheimer's Disease

Objective: To demonstrate the capabilities of Large Language Models (LLMs) as autonomous agents to reproduce findings of published research studies using the same or similar dataset. Materials and Methods: We used the "Quick Access" dataset of the National Alzheimer's Coordinating Center (NACC). We identified highly cited published research manuscripts using NACC data and selected five studies that appeared reproducible using this dataset alone. Using GPT-4o, we created a simulated research team of LLM-based autonomous agents tasked with writing and executing code to dynamically reproduce the findings of each study, given only study Abstracts, Methods sections, and data dictionary descriptions of the dataset. Results: We extracted 35 key findings described in the Abstracts across 5 Alzheimer's studies. On average, LLM agents approximately reproduced 53.2% of findings per study. Numeric values and range-based findings often differed between studies and agents. The agents also applied statistical methods or parameters that varied from the originals, though overall trends and significance were sometimes similar. Discussion: In some cases, LLM-based agents replicated research techniques and findings. In others, they failed due to implementation flaws or missing methodological detail. These discrepancies show the current limits of LLMs in fully automating reproducibility assessments. Still, this early investigation highlights the potential of structured agent-based systems to provide scalable evaluation of scientific rigor. Conclusion: This exploratory work illustrates both the promise and limitations of LLMs as autonomous agents for automating reproducibility in biomedical research.

Align-cDAE: Alzheimer's Disease Progression Modeling with Attention-Aligned Conditional Diffusion Auto-Encoder

Generative AI framework-based modeling and prediction of longitudinal human brain images offer an efficient mechanism to track neurodegenerative progression, essential for the assessment of diseases like Alzheimer's. Among the existing generative approaches, recent diffusion-based models have emerged as an effective alternative to generate disease progression images. Incorporating multi-modal and non-imaging attributes as conditional information into diffusion frameworks has been shown to improve controllability during such generations. However, existing methods do not explicitly ensure that information from non-imaging conditioning modalities is meaningfully aligned with image features to introduce desirable changes in the generated images, such as modulation of progression-specific regions. Further, more precise control over the generation process can be achieved by introducing progression-relevant structure into the internal representations of the model, lacking in the existing approaches. To address these limitations, we propose a diffusion autoencoder-based framework for disease progression modeling that explicitly enforces alignment between different modalities. The alignment is enforced by introducing an explicit objective function that enables the model to focus on the regions exhibiting progression-related changes. Further, we devise a mechanism to better structure the latent representational space of the diffusion auto-encoding framework. Specifically, we assign separate latent subspaces for integrating progression-related conditions and retaining subject-specific identity information, allowing better-controlled image generation. These results demonstrate that enforcing alignment and better structuring of the latent representational space of diffusion auto-encoding framework leads to more anatomically precise modeling of Alzheimer's disease progression.

Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation

Early detection of Alzheimer's disease from spontaneous speech has emerged as a promising non-invasive screening approach. However, the influence of automatic speech recognition (ASR) quality on downstream clinical language modeling remains insufficiently understood. In this study, we investigate Alzheimer's disease detection using lexical features derived from Whisper ASR transcripts on the ADReSSo 2021 diagnosis dataset. We evaluate interpretable machine-learning models, including Logistic Regression and Linear Support Vector Machines, using TF-IDF text representations under repeated 5x5 stratified cross-validation. Our results demonstrate that transcript quality has a statistically significant impact on classification performance. Models trained on Whisper-small transcripts consistently outperform those using Whisper-base transcripts, achieving balanced accuracy above 0.7850 with Linear SVM. Paired statistical testing confirms that the observed improvements are significant. Importantly, classifier complexity contributes less to performance variation than ASR transcription quality. Feature analysis reveals that cognitively normal speakers produce more semantically precise object- and scene-descriptive language, whereas Alzheimer's speech is characterized by vagueness, discourse markers, and increased hesitation patterns. These findings suggest that high-quality ASR can enable simple, interpretable lexical models to achieve competitive Alzheimer's detection performance without explicit acoustic modeling. The study provides a reproducible benchmark pipeline and highlights ASR selection as a critical modeling decision in clinical speech-based artificial intelligence systems. ;22 pages, 7 figures

Hybrid Topological and Deep Feature Fusion for Accurate MRI-Based Alzheimer's Disease Severity Classification

Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this work, we propose a novel hybrid deep learning framework that integrates Topological Data Analysis (TDA) with a DenseNet121 backbone for four-class Alzheimer's disease classification using structural MRI data from the OASIS dataset. TDA is employed to capture complementary topological characteristics of brain structures that are often overlooked by conventional neural networks, while DenseNet121 efficiently learns hierarchical spatial features from MRI slices. The extracted deep and topological features are fused to enhance class separability across the four AD stages. Extensive experiments conducted on the OASIS-1 Kaggle MRI dataset demonstrate that the proposed TDA+DenseNet121 model significantly outperforms existing state-of-the-art approaches. The model achieves an accuracy of 99.93% and an AUC of 100%, surpassing recently published CNN-based, transfer learning, ensemble, and multi-scale architectures. These results confirm the effectiveness of incorporating topological insights into deep learning pipelines and highlight the potential of the proposed framework as a robust and highly accurate tool for automated Alzheimer's disease diagnosis. ;20 pages, 6 Figures

Cross-modal Causal Intervention for Alzheimer's Disease Prediction

Mild Cognitive Impairment (MCI) serves as a prodromal stage of Alzheimer's Disease (AD), where early identification and intervention can effectively slow the progression to dementia. However, diagnosing AD remains a significant challenge in neurology due to the confounders caused mainly by the selection bias of multi-modal data and the complex relationships between variables. To address these issues, we propose a novel visual-language causality-inspired framework named Cross-modal Causal Intervention with Mediator for Alzheimer's Disease Diagnosis (MediAD) for diagnostic assistance. Our MediAD employs Large Language Models (LLMs) to summarize clinical data under strict templates, therefore enriching textual inputs. The MediAD model utilizes Magnetic Resonance Imaging (MRI), clinical data, and textual data enriched by LLMs to classify participants into Cognitively Normal (CN), MCI, and AD categories. Because of the presence of confounders, such as cerebral vascular lesions and age-related biomarkers, non-causal models are likely to capture spurious input-output correlations, generating less reliable results. Our framework implicitly mitigates the effect of both observable and unobservable confounders through a unified causal intervention method. Experimental results demonstrate the outstanding performance of our method in distinguishing CN/MCI/AD cases, outperforming other methods in most evaluation metrics. The study showcases the potential of integrating causal reasoning with multi-modal learning for neurological disease diagnosis.

A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer's Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques' robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features' necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.

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.

MedOrch: Medical Diagnosis with Tool-Augmented Reasoning Agents for Flexible Extensibility

Healthcare decision-making represents one of the most challenging domains for Artificial Intelligence (AI), requiring the integration of diverse knowledge sources, complex reasoning, and various external analytical tools. Current AI systems often rely on either task-specific models, which offer limited adaptability, or general language models without grounding with specialized external knowledge and tools. We introduce MedOrch, a novel framework that orchestrates multiple specialized tools and reasoning agents to provide comprehensive medical decision support. MedOrch employs a modular, agent-based architecture that facilitates the flexible integration of domain-specific tools without altering the core system. Furthermore, it ensures transparent and traceable reasoning processes, enabling clinicians to meticulously verify each intermediate step underlying the system's recommendations. We evaluate MedOrch across three distinct medical applications: Alzheimer's disease diagnosis, chest X-ray interpretation, and medical visual question answering, using authentic clinical datasets. The results demonstrate MedOrch's competitive performance across these diverse medical tasks. Notably, in Alzheimer's disease diagnosis, MedOrch achieves an accuracy of 93.26%, surpassing the state-of-the-art baseline by over four percentage points. For predicting Alzheimer's disease progression, it attains a 50.35% accuracy, marking a significant improvement. In chest X-ray analysis, MedOrch exhibits superior performance with a Macro AUC of 61.2% and a Macro F1-score of 25.5%. Moreover, in complex multimodal visual question answering (Image+Table), MedOrch achieves an accuracy of 54.47%. These findings underscore MedOrch's potential to advance healthcare AI by enabling reasoning-driven tool utilization for multimodal medical data processing and supporting intricate cognitive tasks in clinical decision-making.

Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification

Alzheimer's Disease is a progressive neurological disorder that is one of the most common forms of dementia. It leads to a decline in memory, reasoning ability, and behavior, especially in older people. The cause of Alzheimer's Disease is still under exploration and there is no all-inclusive theory that can explain the pathologies in each individual patient. Nevertheless, early intervention has been found to be effective in managing symptoms and slowing down the disease's progression. Recent research has utilized electroencephalography (EEG) data to identify biomarkers that distinguish Alzheimer's Disease patients from healthy individuals. Prior studies have used various machine learning methods, including deep learning and graph neural networks, to examine electroencephalography-based signals for identifying Alzheimer's Disease patients. In our research, we proposed a Flexible and Explainable Gated Graph Convolutional Network (GGCN) with Multi-Objective Tree-Structured Parzen Estimator (MOTPE) hyperparameter tuning. This provides a flexible solution that efficiently identifies the optimal number of GGCN blocks to achieve the optimized precision, specificity, and recall outcomes, as well as the optimized area under the Receiver Operating Characteristic (AUC). Our findings demonstrated a high efficacy with an over 0.9 Receiver Operating Characteristic score, alongside precision, specificity, and recall scores in distinguishing health control with Alzheimer's Disease patients in Moderate to Severe Dementia using the power spectrum density (PSD) of electroencephalography signals across various frequency bands. Moreover, our research enhanced the interpretability of the embedded adjacency matrices, revealing connectivity differences in frontal and parietal brain regions between Alzheimer's patients and healthy individuals.

A novel mathematical and computational framework of amyloid-beta triggered seizure dynamics in Alzheimer's disease

The association of epileptic activity and Alzheimer's disease (AD) has been increasingly reported in both clinical and experimental studies, suggesting that amyloid-$β$ accumulation may directly affect neuronal excitability. Capturing these interactions requires a quantitative description that bridges the molecular alterations of AD with the fast electrophysiological dynamics of epilepsy. We introduce a novel mathematical model that extends the Barreto-Cressman ionic formulation by incorporating multiple mechanisms of calcium dysregulation induced by amyloid-$β$, including formation of $\mathrm{Ca}^{2+}$-permeable pores, overactivation of voltage-gated $\mathrm{Ca}^{2+}$ channels, and suppression of $\mathrm{Ca}^{2+}$-sensitive potassium currents. The resulting ionic model is coupled with the monodomain equation and discretized using a $p$-adaptive discontinuous Galerkin method on polytopal meshes, providing an effective balance between efficiency and accuracy in capturing the sharp spatiotemporal electrical wavefronts associated with epileptiform discharges. Numerical simulations performed on idealized and realistic brain geometries demonstrate that progressive amyloid-\textbeta{} accumulation leads to severe alterations in calcium homeostasis, increased neuronal hyperexcitability, and pathological seizure propagation. Specifically, high amyloid-$β$ concentrations produce secondary epileptogenic sources and spatially heterogeneous wavefronts, indicating that biochemical inhomogeneities play a critical role in shaping seizure dynamics. These results illustrate how multiscale modeling provides new mechanistic insights into the interplay between neurodegeneration and epilepsy in Alzheimer's disease.

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

Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls

Alzheimer's disease (AD) is a neurodegenerative disorder marked by memory loss and cognitive decline, making early detection vital for timely intervention. However, early diagnosis is challenging due to the heterogeneous presentation of symptoms. Resting-state functional magnetic resonance imaging (rs-fMRI) captures spontaneous brain activity and functional connectivity, which are known to be disrupted in AD and mild cognitive impairment (MCI). Traditional methods, such as Pearson's correlation, have been used to calculate association matrices, but these approaches often overlook the dynamic and non-stationary nature of brain activity. In this study, we introduce a novel method that integrates discrete wavelet transform (DWT) and graph theory to model the dynamic behavior of brain networks. Our approach captures the time-frequency representation of brain activity, allowing for a more nuanced analysis of the underlying network dynamics. Machine learning was employed to automate the discrimination of different stages of AD based on learned patterns from brain network at different frequency bands. We applied our method to a dataset of rs-fMRI images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, demonstrating its potential as an early diagnostic tool for AD and for monitoring disease progression. Our statistical analysis identifies specific brain regions and connections that are affected in AD and MCI, at different frequency bands, offering deeper insights into the disease's impact on brain function.

Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset

Background: Alzheimer's disease (AD) affects over 55 million people worldwide. Accurate, interpretable detection of normal cognition (NC), mild cognitive impairment (MCI), and AD from routine clinical assessments remains a critical unmet need. Methods: An XGBoost classifier was developed for three-class detection using eight clinical features from the Alzheimer's Disease Neuroimaging Initiative (ADNI): MMSE, CDR Global, CDR Sum of Boxes (CDR-SB), MoCA, FAQ, age, sex, and education. Hyperparameters were optimised using Optuna (50 trials); class imbalance was addressed with SMOTE. Performance was evaluated by macro AUC-ROC with 1,000-iteration bootstrap 95% confidence intervals, macro F1, balanced accuracy, and Cohen's kappa. SHAP values provided feature-level explainability. Results: The dataset comprised 1,641 baseline subjects (608 NC, 767 MCI, 266 AD). On five-fold cross-validation, mean macro AUC was 0.983 (SD 0.007), accuracy 0.944 (SD 0.006), and macro F1 0.929 (SD 0.008). On the held-out test set (n = 247), macro AUC was 0.982 (95% CI: 0.965--0.995), accuracy 0.943, balanced accuracy 0.932, macro F1 0.927, and Cohen's kappa 0.909. SHAP analysis identified CDR Global as the dominant predictor for NC and MCI, while CDR-SB and MMSE together drove AD classification. Conclusion: An explainable machine learning model trained on routine clinical assessments achieves near-perfect three-class Alzheimer's detection. SHAP analysis reveals clinically plausible, class-specific feature importance patterns supporting clinical validity. Future work will extend this framework with speech biomarkers for multimodal detection.

The mild cognitive impairment window for optimal Alzheimer's disease intervention

The FDA approval of disease-modifying Alzheimer's disease therapies marks a major shift in treatment but exposes a critical challenge: identifying patients during the mild cognitive impairment (MCI) stage when intervention is most effective. Despite early biological changes, most diagnoses occur after significant decline. Drawing from over 180 stakeholder interviews conducted through the NSF I-Corps program reveal major detection gaps across primary care, specialty access, and available tools. This commentary highlights the consequences of delayed diagnosis and proposes translational strategies to align early detection with therapeutic opportunity, positioning MCI as the critical window for Alzheimer's disease intervention.

Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

MEG are non invasive neuroimaging techniques with excellent temporal and spatial resolution, crucial for studying brain function in dementia and Alzheimer Disease. They identify changes in brain activity at various Alzheimer stages, including preclinical and prodromal phases. MEG may detect pathological changes before clinical symptoms, offering potential biomarkers for intervention. This study evaluates classification techniques using MEG features to distinguish between healthy controls and mild cognitive impairment participants from the BioFIND study. We compare MEG based biomarkers with MRI based anatomical features, both independently and combined. We used 3 Tesla MRI and MEG data from 324 BioFIND participants;158 MCI and 166 HC. Analyses were performed using MATLAB with SPM12 and OSL toolboxes. Machine learning analyses, including 100 Monte Carlo replications of 10 fold cross validation, were conducted on sensor and source spaces. Combining MRI with MEG features achieved the best performance; 0.76 accuracy and AUC of 0.82 for GLMNET using LCMV source based MEG. MEG only analyses using LCMV and eLORETA also performed well, suggesting that combining uncorrected MEG with z-score-corrected MRI features is optimal. ;Comment: 28 pages, 9 figures, 3 tables, 19 supplimetary material

An optimal control problem for anti-inflammatory treatments of Alzheimer's disease

We present and analyze an optimal control problem to model anti-inflammatory treatment strategies for Alzheimer's disease, using a system of differential equations that captures interactions between Aβ-peptides, microglial cells, interleukins, and neurons. These interactions operate through mechanisms such as protein polymerization, inflammation processes, and neural stress responses. In particular, inflammation is highlighted as a key factor in the onset and progression of Alzheimer's disease, driven by a hysteresis effect related to the degradation rate d of monomers and the initial concentration of interleukins. This implies a critical inflammation threshold that determines whether the disease persists over the long term. The optimal control problem we propose seeks to minimize the concentration of toxic oligomers by modulating interleukin production and degradation rates, representing potential anti-inflammatory treatment effects. Under natural constraints on treatment dose efficacy and cumulative exposure, our goal is to assess whether it is possible to shift the system from a persistent disease state to a disease-free equilibrium. We provide a characterization of the optimal solution and supplement our theoretical findings with numerical simulations, which illustrate the system's behavior under different parameter settings and the imposed constraints of the optimal control problem.

A Multimodal Approach to Alzheimer's Diagnosis: Geometric Insights from Cube Copying and Cognitive Assessments

Early and accessible detection of Alzheimer's disease (AD) remains a critical clinical challenge, and cube-copying tasks offer a simple yet informative assessment of visuospatial function. This work proposes a multimodal framework that converts hand-drawn cube sketches into graph-structured representations capturing geometric and topological properties, and integrates these features with demographic information and neuropsychological test (NPT) scores for AD classification. Cube drawings are modeled as graphs with node features encoding spatial coordinates, local graphlet-based topology, and angular geometry, which are processed using graph neural networks and fused with age, education, and NPT features in a late-fusion model. Experimental results show that graph-based representations provide a strong unimodal baseline and substantially outperform pixel-based convolutional models, while multimodal integration further improves balanced classification performance and discriminative ability. SHAP-based interpretability analysis identifies specific graphlet motifs associated with corner integrity and edge continuity as key predictors, closely aligning with clinical observations of distorted cube drawings in AD. Together, these findings establish graph-based analysis of cube-copying behavior as an interpretable, non-invasive, and scalable framework for Alzheimer's disease screening.

Assessing the clinical meaningfulness of slowing CDR-SB progression with disease-modifying therapies for Alzheimer disease

INTRODUCTION: For many patients and caregivers, a major goal of disease-modifying treatments (DMT) for Alzheimer disease (AD) dementia is to extend independence in instrumental and basic activities of daily living (IADLs and BADLs). The goal of this study was to estimate the effect of treatments on the time remaining independent in IADLs and BADLs. METHODS: Participants at the Knight Alzheimer Disease Research Center were selected who were potentially eligible for recent DMT trials: age ≥ 60 years at baseline, clinical diagnosis of very mild or mild AD dementia (global Clinical Dementia Rating® (CDR®) score 0.5 or 1), biomarker confirmation of amyloid pathology, and at least one follow-up CDR assessment within 5 years. For IADLs, a subset of the Functional Assessment Questionnaire (FAQ) was examined that rated the degree of independence in the following: paying bills, driving, remembering medications and appointments, and preparing meals. For BADLs, the Personal Care domain of the CDR was used. Mixed-effects logistic and ordinal regression models were used to examine the relationship between CDR Sum Boxes (CDR-SB) and the individual functional outcomes and their components. The change in CDR-SB over time was estimated with linear mixed effects models. RESULTS: 282 participants were followed for an average of 2.9 years (SD 1.3 years). For 50% of individuals, loss of independence in IADLs occurred at CDR-SB>4.5 and in BADLs at CDR-SB>11.5. For individuals with a baseline CDR-SB=2, treatment with lecanemab would extend independence in IADLs for 10 months (95% CI 4-18 months) and treatment with donanemab in the low/medium tau group would extend independence in IADLs by 13 months (95% CI 6-24 months). DISCUSSION: Independence in ADLs can be related to CDR-SB and used to demonstrate the effect of AD treatments in extending the time of independent function, a meaningful outcome for patients and their families.

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

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

Chain-of-Thought Reasoning with Large Language Models for Clinical Alzheimer's Disease Assessment and Diagnosis

Alzheimer's disease (AD) has become a prevalent neurodegenerative disease worldwide. Traditional diagnosis still relies heavily on medical imaging and clinical assessment by physicians, which is often time-consuming and resource-intensive in terms of both human expertise and healthcare resources. In recent years, large language models (LLMs) have been increasingly applied to the medical field using electronic health records (EHRs), yet their application in Alzheimer's disease assessment remains limited, particularly given that AD involves complex multifactorial etiologies that are difficult to observe directly through imaging modalities. In this work, we propose leveraging LLMs to perform Chain-of-Thought (CoT) reasoning on patients' clinical EHRs. Unlike direct fine-tuning of LLMs on EHR data for AD classification, our approach utilizes LLM-generated CoT reasoning paths to provide the model with explicit diagnostic rationale for AD assessment, followed by structured CoT-based predictions. This pipeline not only enhances the model's ability to diagnose intrinsically complex factors but also improves the interpretability of the prediction process across different stages of AD progression. Experimental results demonstrate that the proposed CoT-based diagnostic framework significantly enhances stability and diagnostic performance across multiple CDR grading tasks, achieving up to a 15% improvement in F1 score compared to the zero-shot baseline method.

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.

AgenticAD: A Specialized Multiagent System Framework for Holistic Alzheimer Disease Management

Alzheimer's disease (AD) presents a complex, multifaceted challenge to patients, caregivers, and the healthcare system, necessitating integrated and dynamic support solutions. While artificial intelligence (AI) offers promising avenues for intervention, current applications are often siloed, addressing singular aspects of the disease such as diagnostics or caregiver support without systemic integration. This paper proposes a novel methodological framework for a comprehensive, multi-agent system (MAS) designed for holistic Alzheimer's disease management. The objective is to detail the architecture of a collaborative ecosystem of specialized AI agents, each engineered to address a distinct challenge in the AD care continuum, from caregiver support and multimodal data analysis to automated research and clinical data interpretation. The proposed framework is composed of eight specialized, interoperable agents. These agents are categorized by function: (1) Caregiver and Patient Support, (2) Data Analysis and Research, and (3) Advanced Multimodal Workflows. The methodology details the technical architecture of each agent, leveraging a suite of advanced technologies including large language models (LLMs) such as GPT-4o and Gemini, multi-agent orchestration frameworks, Retrieval-Augmented Generation (RAG) for evidence-grounded responses, and specialized tools for web scraping, multimodal data processing, and in-memory database querying. This paper presents a detailed architectural blueprint for an integrated AI ecosystem for AD care. By moving beyond single-purpose tools to a collaborative, multi-agent paradigm, this framework establishes a foundation for developing more adaptive, personalized, and proactive solutions. This methodological approach aims to pave the way for future systems capable of synthesizing diverse data streams to improve patient outcomes and reduce caregiver burden.

Graph Attention Based Prioritization of Disease Responsible Genes from Multimodal Alzheimer's Network

Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer's disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We propose NETRA (Node Evaluation through Transformer-based Representation and Attention), a multimodal graph transformer framework that replaces heuristic centrality metrics with attention-driven relevance scoring. Using AD as a case study, gene regulatory networks are independently constructed from microarray, single-cell RNA-seq, and single-nucleus RNA-seq data. Random-walk sequences derived from these networks are used to train a BERT-based model for learning global gene embeddings, while modality-specific gene expression profiles are compressed using variational autoencoders. These representations are integrated with auxiliary biological networks, including protein-protein interactions, Gene Ontology semantic similarity, and diffusion-based gene similarity, into a unified multimodal graph. A graph transformer assigns NETRA scores that quantify gene relevance in a disease-specific and context-aware manner. Gene set enrichment analysis shows that NETRA achieves a normalized enrichment score of about 3.9 for the Alzheimer's disease pathway, substantially outperforming classical centrality measures and diffusion models. Top-ranked genes enrich multiple neurodegenerative pathways, recover a known late-onset AD susceptibility locus at chr12q13, and reveal conserved cross-disease gene modules. The framework preserves biologically realistic heavy-tailed network topology and is readily extensible to other complex disorders.

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.

The role of Th17 cells/IL-17A in AD, PD, ALS and the strategic therapy targeting on IL-17A

Neurodegenerative diseases are a group of disorders characterized by progressive loss of certain populations of neurons, which eventually lead to dysfunction. These diseases include Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS). Immune pathway dysregulation is one of the common features of neurodegeneration. Recently, there is growing interest in the specific role of T helper Th 17 cells and Interleukin-17A (IL-17A), the most important cytokine of Th 17 cells, in the pathogenesis of the central nervous system (CNS) of neurodegenerative diseases. In the present study, we summarized current knowledge about the function of Th17/IL-17A, the physiology of Th17/IL-17A in diseases, and the contribution of Th17/IL-17A in AD, PD, and ALS. We also update the findings on IL-17A-targeting drugs as potentially immunomodulatory therapeutic agents for neurodegenerative diseases. Although the specific mechanism of Th17/IL-17A in this group of diseases is still controversial, uncovering the molecular pathways of Th17/IL-17A in neurodegeneration allows the identification of suitable targets to modulate these cellular processes. Therapeutics targeting IL-17A might represent potentially novel anti-neurodegeneration drugs.

Presymptomatic grey matter alterations in ALS kindreds: a computational neuroimaging study of asymptomatic C9orf72 and SOD1 mutation carriers

Background The characterisation of presymptomatic disease-burden patterns in asymptomatic mutation carriers has a dual academic and clinical relevance. The understanding of disease propagation mechanisms is of considerable conceptual interests, and defining the optimal time of pharmacological intervention is essential for improved clinical trial outcomes. Methods In a prospective, multimodal neuroimaging study, 22 asymptomatic C9orf72 GGGGCC hexanucleotide repeat carriers, 13 asymptomatic subjects with SOD1 , and 54 “gene-negative” ALS kindreds were enrolled. Cortical and subcortical grey matter alterations were systematically appraised using volumetric, morphometric, vertex, and cortical thickness analyses. Using a Bayesian approach, the thalamus and amygdala were further parcellated into specific nuclei and the hippocampus was segmented into anatomically defined subfields. Results Asymptomatic GGGGCC hexanucleotide repeat carriers in C9orf72 exhibited early subcortical changes with the preferential involvement of the pulvinar and mediodorsal regions of the thalamus, as well as the lateral aspect of the hippocampus. Volumetric approaches, morphometric methods, and vertex analyses were anatomically consistent in capturing focal subcortical changes in asymptomatic C9orf72 hexanucleotide repeat expansion carriers. SOD1 mutation carriers did not exhibit significant subcortical grey matter alterations. In our study, none of the two asymptomatic cohorts exhibited cortical grey matter alterations on either cortical thickness or morphometric analyses. Discussion The presymptomatic radiological signature of C9orf72 is associated with selective thalamic and focal hippocampal degeneration which may be readily detectable before cortical grey matter changes ensue. Our findings confirm selective subcortical grey matter involvement early in the course of C9orf72 -associated neurodegeneration.

Assessment of bidirectional relationships between 98 genera of the human gut microbiota and amyotrophic lateral sclerosis: a 2-sample Mendelian randomization study

Background Growing evidence suggests a mutual interaction between gut microbiome alterations and ALS pathogenesis. However, previous studies were susceptible to potential confounding factors and reverse causation bias, likely leading to inconsistent and biased results. Objectives To decipher the potentially mutual relationship between gut microbiota and ALS, we used a bidirectional two-sample MR approach to examine the associations between the gut microbiome and ALS. Results Using the inverse variance-weighted method, OTU10032 unclassified Enterobacteriaceae species-level OTU and unclassified Acidaminococcaceae were associated with a higher risk of ALS (per relative abundance: OR, 1.04; 95% CI, 1.01–1.07; P  = 0.011 and OR, 1.02; 95% CI, 1.01–1.04; P  = 0.009, respectively). Importantly, Gamma-Glu-Phe was showed potential deleterious effects on the risk of ALS (genetically predicted per a 1-standard deviation increase in the level of Gamma-Glu-Phe: OR, 1.96; 95% CI, 1.50–2.55; P  = 0.012). Sensitivity analysis of the two candidate genera and metabolites using the MR-Egger and weighted-median methods produced similar estimates, and no horizontal pleiotropy or outliers were observed. Intriguingly, genetically predicted ALS was associated with an increase in the relative abundance of OTU4607_Sutterella (per 1-unit higher log odds: β, 2.23; 95% CI, 1.27–3.18; P  = 0.020) and Lactobacillales_ORDER (per 1-unit higher log odds: β, 0.51; 95% CI, 0.09–0.94; P  = 0.019). Conclusions Our findings provide novel evidence supporting the bidirectional relationship between the gut microbiota and ALS. These results may contribute to designing microbiome- and microbiome-dependent metabolite interventions in future ALS clinical trials.

Blood-based biomarkers of inflammation in amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating neurodegenerative disease in which many processes are detected including (neuro)inflammation. Many drugs have been tested for ALS in clinical trials but most have failed to reach their primary endpoints. The development and inclusion of different types of biomarkers in diagnosis and clinical trials can assist in determining target engagement of a drug, in distinguishing between ALS and other diseases, and in predicting disease progression rate, drug responsiveness, or an adverse event. Ideally, among other characteristics, a biomarker in ALS correlates highly with a disease process in the central nervous system or with disease progression and is conveniently obtained in a peripheral tissue. Here, we describe the state of biomarkers of inflammation in ALS by focusing on peripherally detectable and cellular responses from blood cells, and provide new (combinatorial) directions for exploration that are now feasible due to technological advancements.

Real-time ultrasound-guided sacral plexus block combined with mild sedation for hemorrhoidectomy and hemorrhoidal artery ligation in a patient with amyotrophic lateral sclerosis: a case report

Background Patients with amyotrophic lateral sclerosis present perioperative challenges for clinical anesthesiologists for anesthesia-associated complications. Case presentation A 54-year-old Han woman with a 2-year history of amyotrophic lateral sclerosis was scheduled for hemorrhoidectomy and hemorrhoidal artery ligation. We performed real-time ultrasound-guided sacral plexus block with dexmedetomidine under standard monitoring. The anesthesia method met the surgical demands and avoided respiratory complications during the procedures. There was no neurological deterioration after the surgery and 3 months after, the patient was discharged. Conclusions Real-time ultrasound-guided sacral plexus block combined with mild sedation may be an effective and safe technique in patients with amyotrophic lateral sclerosis undergoing hemorrhoidectomy and hemorrhoidal artery ligation.

Non-motor symptoms in patients with amyotrophic lateral sclerosis: current state and future directions

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease characterized by the progressive degeneration of both upper and lower motor neurons. A defining histopathological feature in approximately 97% of all ALS cases is the accumulation of phosphorylated trans-activation response (TAR) DNA-binding protein 43 protein (pTDP-43) aggregates in the cytoplasm of neurons and glial cells within the central nervous system. Traditionally, it was believed that the accumulation of TDP-43 aggregates and subsequent neurodegeneration primarily occurs in motor neurons. However, contemporary evidence suggests that as the disease progresses, other systems and brain regions are also affected. Despite this, there has been a limited number of clinical studies assessing the non-motor symptoms in ALS patients. These studies often employ various outcome measures, resulting in a wide range of reported frequencies of non-motor symptoms in ALS patients. The importance of assessing the non-motor symptoms reflects in a fact that they have a significant impact on patients’ quality of life, yet they frequently go underdiagnosed and unreported during clinical evaluations. This review aims to provide an up-to-date overview of the current knowledge concerning non-motor symptoms in ALS. Furthermore, we address their diagnosis and treatment in everyday clinical practice.

FUS mutations dominate TBK1 mutations in FUS/TBK1 double-mutant ALS/FTD pedigrees

Mutations in FUS and TBK1 often cause aggressive early-onset amyotrophic lateral sclerosis (ALS) or a late-onset ALS and/or frontotemporal dementia (FTD) phenotype, respectively. Co-occurrence of mutations in two or more Mendelian ALS/FTD genes has been repeatedly reported. However, little is known how two pathogenic ALS/FTD mutations in the same patient interact to shape the final phenotype. We screened 28 ALS patients with a known FUS mutation by whole-exome sequencing and targeted evaluation for mutations in other known ALS genes followed by genotype–phenotype correlation analysis of FUS/TBK1 double-mutant patients. We report on new and summarize previously published FUS and TBK1 double-mutant ALS/FTD patients and their families. We found that, within a family, mutations in FUS cause ALS while TBK1 single mutations are observed in FTD patients. FUS/TBK1 double mutations manifested as ALS and without a manifest difference regarding age at onset and disease duration when compared to FUS single-mutant individuals. In conclusion, TBK1 and FUS variants do not seem to interact in a simple additive way. Rather, the phenotype of FUS/TBK1 double-mutant patients appears to be dominated by the FUS mutation.

Sequential alterations in diffusion metrics as correlates of disease severity in amyotrophic lateral sclerosis

Background and objective The neuropathology of amyotrophic lateral sclerosis (ALS) follows a regional distribution pattern in the brain with four stages. Using diffusion tensor imaging (DTI), this pattern can be translated into a tract-based staging scheme to assess cerebral progression in vivo. This study investigates the association between the sequential alteration pattern and disease severity in patients with ALS. Methods DTI data of 325 patients with ALS and 130 healthy controls were analyzed in a tract of interest (TOI)-based approach. Patients were categorized according to their ALS-FRS-R scores into groups with declining functionality. The fractional anisotropy (FA) values in the tracts associated with neuropathological stages were group-wise compared with healthy controls. Results The FA in the tracts associated with ALS stages showed a decrease which could be related to the disease severity stratification, i.e., at the group level, the lower the ALS-FRS-R of the categorized patient group, the higher was the effect size of the stage-related tract. In the patient group with the highest ALS-FRS-R, Cohen’s d showed a medium effect size in the corticospinal tract and small effect sizes in the other stage-related tracts. Overall, the lower the ALS-FRS-R of the categorized patient group the higher was the effect size of the comparison with healthy controls. Conclusion The progression of white matter alterations across tracts according to the model of sequential tract involvement is associated with clinical disease severity in patients with ALS, suggesting the use of staging-based DTI as a technical marker for disease progression.

The value of routine blood work-up in clinical stratification and prognosis of patients with amyotrophic lateral sclerosis

Background There is an unmet need in amyotrophic lateral sclerosis (ALS) to provide specific biomarkers for the disease. Due to their easy availability, we aimed to investigate whether routine blood parameters provide useful clues for phenotypic classification and disease prognosis. Methods We analyzed a large inpatient cohort of 836 ALS patients who underwent deep phenotyping with evaluation of the clinical and neurophysiological burden of upper (UMN) and lower (LMN) motor neuron signs. Disability and progression rate were measured through the revised ALS Functional Rating Scale (ALSFRS-R) and its changes during time. Cox regression analysis was performed to assess survival associations. Results Creatinine significantly correlated with LMN damage ( r  = 0.38), active ( r  = 0.18) and chronic ( r  = 0.24) denervation and baseline ALSFRS-R ( r  = 0.33). Creatine kinase (CK), alanine (ALT) and aspartate (AST) transaminases correlated with active ( r  = 0.35, r  = 0.27, r  = 0.24) and chronic ( r  = 0.37, r  = 0.20, r  = 0.19) denervation, while albumin and C-reactive protein significantly correlated with LMN score ( r  = 0.20 and r  = 0.17). Disease progression rate showed correlations with chloride ( r  = −0.19) and potassium levels ( r  = −0.16). After adjustment for known prognostic factors, total protein [HR 0.70 (95% CI 0.57–0.86)], creatinine [HR 0.86 (95% CI 0.81–0.92)], chloride [HR 0.95 (95% CI 0.92–0.99)], lactate dehydrogenase [HR 0.99 (95% CI 0.99–0.99)], and AST [HR 1.02 (95% CI 1.01–1.02)] were independently associated with survival. Conclusions Creatinine is a reliable biomarker for ALS, associated with clinical features, disability and survival. Markers of nutrition/inflammation may offer additional prognostic information and partially correlate with clinical features. AST and chloride could further assist in predicting progression rate and survival.

Ensemble-imbalance-based classification for amyotrophic lateral sclerosis prognostic prediction: identifying short-survival patients at diagnosis

Prognosticating Amyotrophic Lateral Sclerosis (ALS) presents a formidable challenge due to patients exhibiting different onset sites, progression rates, and survival times. In this study, we have developed and evaluated Machine Learning (ML) algorithms that integrate Ensemble and Imbalance Learning techniques to classify patients into Short and Non-Short survival groups based on data collected during diagnosis. We aimed to identify individuals at high risk of mortality within 24 months of symptom onset through analysis of patient data commonly encountered in daily clinical practice. Our Ensemble-Imbalance approach underwent evaluation employing six ML algorithms as base classifiers. Remarkably, our results outperformed those of individual algorithms, achieving a Balanced Accuracy of 88% and a Sensitivity of 96%. Additionally, we used the Shapley Additive Explanations framework to elucidate the decision-making process of the top-performing model, pinpointing the most important features and their correlations with the target prediction. Furthermore, we presented helpful tools to visualize and compare patient similarities, offering valuable insights. Confirming the obtained results, our approach could aid physicians in devising personalized treatment plans at the time of diagnosis or serve as an inclusion/exclusion criterion in clinical trials.

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.

Reasons and experience for patients with amyotrophic lateral sclerosis using traditional Chinese medicine: a CARE-TCM based mixed method study

Background and aim Traditional Chinese medicine (TCM) is widely used by patients with amyotrophic lateral sclerosis (ALS). However, their reasons and experience in using TCM have received insufficient attention. Therefore, we conducted a mixed method study to gain insights into this issue. Materials and methods This study was conducted on the basis of the China Amyotrophic Lateral Sclerosis Registry of Patients with Traditional Chinese Medicine (CARE-TCM). Data were collected from Dongzhimen Hospital through a mixed method approach, including a questionnaire and a semi-structured interview. Patients with ALS who were using TCM when they were initially registered with CARE-TCM and who had been followed-up for over six months were recruited. The questionnaires’ outcomes were statistically outlined, and the interview transcripts were thematically analysed to identify themes and sub-themes. Results Fifty-two and sixteen patients were included in the questionnaire and semi-structured interview groups, respectively. Patients used TCM with the hope of regulating their body holistically to improve nonmotor symptoms and quality of life (QOL). Those who recognised TCM as ineffective tended to discontinue it after a three-month trial period. Although quality was a major concern, herbal medicine (HM) was the most frequently used modality among all participants ( n  = 52), with the majority ( n  = 44, 84.6%) continuing to use it. Patients emphasised in-person consultations as a crucial part of TCM treatment. However, the disability caused by disease often made this interaction unattainable. Conclusion Nonmotor symptoms and QOL hold substantial importance for patients with ALS using TCM. HM is a more suitable modality than other TCM treatment modalities, but patients are facing challenges in seeking HM treatment. It is necessary to promote the implementation of hierarchical diagnosis and treatment, thus making TCM more accessible. Trial registration ClinicalTrials.gov identifier: NCT04885374 (registered on May 13, 2021).

Sensory Changes Related to Swallowing in Motor Neurone Disease

Dysphagia is common in motor neurone disease (MND) and associated with negative health and psychosocial outcomes. Although largely considered a motor disease, a growing body of evidence suggests that MND can also affect the sensory system. As intact sensation is vital for safe swallowing, and sensory changes can influence the clinical management of dysphagia in people living with MND, this review evaluated and summarised the current evidence for sensory changes related to swallowing in MND. Of 3,481 articles originally identified, 29 met the inclusion criteria. Of these, 20 studies reported sensory changes, which included laryngeal sensation, taste, gag reflex, cough reflex, tongue sensation, smell, palatal and pharyngeal sensation, silent aspiration, and undefined sensation of the swallowing mechanism. Sensory changes were either described as decreased ( n  = 16) or heightened ( n  = 4). In the remaining nine studies, sensory function was reported as unaffected. The presence of changes to sensory function related to swallowing in MND remains inconclusive, although an increasing number of studies report sensory changes in some sensory domains. Future research is needed to evaluate the prevalence of sensory changes in MND and how such changes may influence dysphagia and its management.

Withdrawal of mechanical ventilation in amyotrophic lateral sclerosis patients: a multicenter Italian survey

Background Law 219/2017 was approved in Italy in December 2017, after a years-long debate on the autonomy of healthcare choices. This Law, for the first time in Italian legislation, guarantees the patient’s right to request for withdrawal of life-sustaining treatments, including mechanical ventilation (MV). Objective To investigate the current status of MV withdrawal in amyotrophic lateral sclerosis (ALS) patients in Italy and to assess the impact of Law 219/2017 on this practice. Methods We conducted a Web-based survey, addressed to Italian neurologists with expertise in ALS care, and members of the Motor Neuron Disease Study Group of the Italian Society of Neurology. Results Out of 40 ALS Italian centers, 34 (85.0%) responded to the survey. Law 219/2017 was followed by an increasing trend in MV withdrawals, and a significant increase of neurologists involved in this procedure ( p 0.004). However, variations across Italian ALS centers were observed, regarding the inconsistent involvement of community health services and palliative care (PC) services, and the intervention and composition of the multidisciplinary team. Conclusions Law 219/2017 has had a positive impact on the practice of MV withdrawal in ALS patients in Italy. The recent growing public attention on end-of-life care choices, along with the cultural and social changes in Italy, requires further regulatory frameworks that strengthen tools for self-determination, increased investment of resources in community and PC health services, and practical recommendations and guidelines for health workers involved.

Presymptomatic geographical distribution of ALS patients suggests the involvement of environmental factors in the disease pathogenesis

Background Given that the pathogenetic process of ALS begins many years prior to its clinical onset, examining patients’ residential histories may offer insights on the disease risk factors. Here, we analyzed the spatial distribution of a large ALS cohort in the 50 years preceding the disease onset. Methods Data from the PARALS register were used. A spatial cluster analysis was performed at the time of disease onset and at 1-year intervals up to 50 years prior to that. Results A total of 1124 patients were included. The analysis revealed a higher-incidence cluster in a large area (435,000 inhabitants) west of Turin. From 9 to 2 years before their onset, 105 cases were expected and 150 were observed, resulting in a relative risk of 1.49 ( P  = 0.04). We also found a surprising high number of patients pairs (51) and trios (3) who lived in the same dwelling while not being related. Noticeably, these occurrences were not observed in large dwellings as we would have expected. The probability of this occurring in smaller buildings only by chance was very low ( P  = 0.01 and P  = 0.04 for pairs and trios, respectively). Conclusions We identified a higher-incidence ALS cluster in the years preceding the disease onset. The cluster area being densely populated, many exposures could have contributed to the high incidence ALS cluster, while we could not find a shared exposure among the dwellings where multiple patients had lived. However, these findings support that exogenous factors are likely involved in the ALS pathogenesis.

Association of blood lipids with onset and prognosis of amyotrophic lateral sclerosis: results from the ALS Swabia registry

Background To date, the role of blood lipid levels and their association with the onset and prognosis of ALS is controversial. We explored these associations in a large, population-based case–control study. Methods Between October 2010 and June 2014, 336 ALS patients (mean age 65.7 ± 10.7; 57.7% male) and 487 sex- and age-matched controls from the same geographic region were recruited within the ALS registry in Southwest Germany. Triglycerides and cholesterol (high-density lipoprotein (HDL), low-density lipoprotein (LDL), total) were measured. The ALS cohort was followed up for vital status. Conditional logistic regression models were applied to calculate odds ratio (OR) for risk of ALS associated with serum lipid concentrations. In ALS patients only, survival models were used to appraise the prognostic value. Results High concentration of total cholesterol (OR 1.60, 95% confidence interval (CI) 1.03–2.49, top vs. bottom quartile), but not HDL, LDL, LDL–HDL ratio, or triglycerides, was positively associated with the risk of ALS. During the median follow-up time of 88.9 months, 291 deaths occurred among 336 ALS patients. In the adjusted survival analysis, higher HDL (HR 1.72, 95% CI 1.19–2.50) and LDL cholesterol levels (HR 1.58, 95% CI 1.11–2.26) were associated with higher mortality in ALS patients. In contrast, higher triglyceride levels were associated with lower mortality (HR 0.68, 95% CI 0.48–0.96). Conclusion The results highlight the importance to distinguish cholesterol from triglycerides when considering the prognostic role of lipid metabolism in ALS. It further strengthens the rationale for a triglyceride-rich diet, while the negative impact of cholesterol must be further explored.

Cerebral glucose metabolic correlates of cognitive and behavioural impairments in amyotrophic lateral sclerosis

Objective Half of ALS patients are cognitively and/or behaviourally impaired. As cognition/behaviour and cerebral glucose metabolism can be correlated by means of ^18F-Fluorodeoxyglucose positron emission tomography (FDG-PET), we aimed to utilise FDG-PET, first, to replicate group-level differences in glucose metabolism between non-demented ALS patients separated into non-impaired (ALSni), cognitively impaired (ALSci), behaviourally impaired (ALSbi), and cognitively and behaviourally impaired (ALScbi) groups; second, to investigate glucose metabolism and performance in various cognitive domains; and third, to examine the impact of partial volume effects correction (PVEC) of the FDG-PET data on the results. Methods We analysed neuropsychological, clinical, and imaging data from 67 ALS patients (30 ALSni, 21 ALSci, 5 ALSbi, and 11 ALScbi). Cognition was assessed with the Edinburgh Cognitive and Behavioural ALS Screen, and two social cognition tests. FDG-PET and structural MRI scans were acquired for each patient. Voxel-based statistical analyses were undertaken on grey matter volume (GMV) and non-corrected vs. PVE-corrected FDG-PET scans. Results ALSci and ALScbi had lower cognitive scores than ALSni. In contrast to both ALSni and ALSci, ALScbi showed widespread hypometabolism in the superior- and middle-frontal gyri in addition to the right temporal pole. Correlations were observed between the GMV, the FDG-PET signal, and various cognitive scores. The FDG-PET results were largely unaffected by PVEC. Interpretation Our study identified widespread differences in hypometabolism in the ALScbi-ni but not in the ALSci-ni group comparison, raising the possibility that cerebral metabolism may be more closely related to the presence of behavioural changes than to mild cognitive deficits.

Potential role of vitamin D levels in amyotrophic lateral sclerosis cognitive impairment

Cognitive impairment (CI) is common in amyotrophic lateral sclerosis (ALS): a keystone is identifying factors that could potentially modify the CI course. In recent years, vitamin D is becoming a potential modificatory factor for CI in many neurological disorders. This study aimed to highlight if vitamin D deficiency correlated with CI and clinical features in a cohort of ALS patients. We included 55 ALS patients with a neuropsychological evaluation (classified with the Strong Criteria) and a vitamin D dosage at the diagnosis. We also reviewed medical records and completed data for medical history, physical and neurological examination, and functional scales. At the diagnosis, 30 patients (54%) had CI. Most patients (82%) displayed low vitamin D levels (19.87 ± 9.80 ng/ml). Comparing the vitamin D level between patients with and without CI, we observed significantly lower values in the first group (15.8 ± 8.2 vs. 22.0 ± 9.7 ng/ml, p : 0.04). In the spinal female subgroup ( n = 15), we found an inverse correlation between vitamin D and bizarreness score in the cognitive estimates test ( r = 0.58; p : 0.04) and a positive correlation with the Corrected Raven’s Standard Progressive Matrices ( r = 0.53, p : 0.04). Conversely, in the bulbar female group, we observed a correlation with the corrected direct span ( r = 0.84, p : 0.03). With the log-rank survival analysis, we found that the patients with vitamin D < 10 ng/ml had a shorter disease duration (Chi: 5.78, p : 0.02). Our results indicate that levels of vitamin D can influence the cognitive status of people living with ALS and that severe deficits might be an adverse prognostic survival factor.

Factors impacting trial participation in people with motor neuron disease

Motor neuron disease (MND) is a rapidly progressive neurodegenerative disorder with limited treatment options. Historically, neurological trials have been plagued by suboptimal recruitment and high rates of attrition. The Motor Neuron Disease–Systematic Multi-Arm Randomised Adaptive Trial (MND–SMART) seeks to identify effective disease modifying drugs. This study investigates person-specific factors affecting recruitment and retention. Improved understanding of these factors may improve trial protocol design, optimise recruitment and retention. Participants with MND completed questionnaires and this was supplemented with clinical data. 12 months after completing the questionnaires we used MND–SMART recruitment data to establish if members of our cohort engaged with the trial. 120 people with MND completed questionnaires for this study. Mean age at participation was 66 (SD = 9), 14% ( n =  17) were categorised as long survivors, with 68% ( n =  81) of participants male and 60% ( n =  73) had the ALS sub-type. Of the 120 study participants, 50% ( n =  60) were randomised into MND–SMART and 78% ( n =  94) expressed interest an in participating. After the 1-year follow-up period 65% ( n =  39) of the 60 randomised participants remained in MND–SMART. Older age was significantly associated with reduced likelihood of participation (OR = 0.92, 95% CI = 0.88–0.96, p =  0.000488). The findings show that people with MND are highly motivated to engage in research, but older individuals remain significantly less likely to participate. We recommend the inclusion of studies to explore characteristics of prospective and current participants alongside trials.

Fasciculation intensity and limb dominance in amyotrophic lateral sclerosis: a muscle ultrasonographic study

Background and purpose Muscle ultrasonography has been increasingly recognized as a useful tool for detection of fasciculations. Separately, concordance between dominant hand and onset side has been reported in amyotrophic lateral sclerosis (ALS). The aim of this study was to reveal the distribution of fasciculations in the whole body, focusing on handedness. Methods In 106 consecutive patients with ALS, muscle ultrasonography was systematically performed in 11 muscles (the tongue, and bilateral biceps brachii, 1st dorsal interosseous [FDI], T10-paraspinalis, vastus lateralis and tibialis anterior muscles). The fasciculation intensity was scored from 0 to 3 for each muscle. Results Fasciculations were more frequently found in the limb muscles than the tongue and paraspinalis. Side and handedness analyses revealed that fasciculation intensity in FDI was significantly more prominent on the right (median [inter-quartile range] 2 [0 - 3]) than left (1.5 [0 - 3]; p = 0.016), and in the dominant hand (2 [1 - 3]) than non-dominant side (1.5 [0 - 3]; p = 0.025). The differences were greater in patients with upper limb onset. There were no side differences in the lower limb muscles. Multivariate analyses showed that male patients had more frequent fasciculations in the dominant FDI (β = 0.22, p < 0.05). Conclusion More intensive fasciculations are present in the FDI in the dominant hand and gender might be associated with fasciculation intensities. This distribution pattern of fasciculations might be associated with pathogenesis of ALS.

Home-monitoring of vital capacity in people with a motor neuron disease

Background Home-monitoring of spirometry has the potential to improve care for patients with a motor neuron disease (MND) by enabling early detection of respiratory dysfunction and reducing travel burden. Our aim was to evaluate the validity and feasibility of home-monitoring vital capacity (VC) in patients with MND. Methods We included 33 patients with amyotrophic lateral sclerosis, progressive muscular atrophy or primary lateral sclerosis who completed a 12-week home-monitoring protocol, consisting of 4-weekly unsupervised home assessments of VC and a functional rating scale. At baseline, during a home visit, patients/caregivers were trained in performing a VC test, and the investigator performed a supervised VC test, which was repeated at final follow-up during a second home visit. Validity of the unsupervised VC tests was evaluated by the differences between supervised and unsupervised VC tests, and through Bland–Altman 95% limits-of-agreement. Feasibility was assessed by means of a survey of user-experiences. Results The 95% limits-of-agreement were [− 14.3; 11.7] %predicted VC, and 88% of unsupervised VC tests fell within 10%predicted of supervised VC. 88% of patients experienced VC testing as easy and not burdensome, however, 15% patients did not think their VC test was performed as well as in the clinic. 94% of patients would like home-monitoring of VC in MND care. Discussion Unsupervised VC testing at home, with prior face-to-face training, is a valid and time-efficient method for the remote monitoring of respiratory function, and well-accepted by patients with MND and their caregivers.

A perspective on persistent toxicants in veterans and amyotrophic lateral sclerosis: identifying exposures determining higher ALS risk

Multiple studies indicate that United States veterans have an increased risk of developing amyotrophic lateral sclerosis (ALS) compared to civilians. However, the responsible etiological factors are unknown. In the general population, specific occupational (e.g. truck drivers, airline pilots) and environmental exposures (e.g. metals, pesticides) are associated with an increased ALS risk. As such, the increased prevalence of ALS in veterans strongly suggests that there are exposures experienced by military personnel that are disproportionate to civilians. During service, veterans may encounter numerous neurotoxic exposures (e.g. burn pits, engine exhaust, firing ranges). So far, however, there is a paucity of studies investigating environmental factors contributing to ALS in veterans and even fewer assessing their exposure using biomarkers. Herein, we discuss ALS pathogenesis in relation to a series of persistent neurotoxicants (often emitted as mixtures) including: chemical elements, nanoparticles and lipophilic toxicants such as dioxins, polycyclic aromatic hydrocarbons and polychlorinated biphenyls. We propose these toxicants should be directly measured in veteran central nervous system tissue, where they may have accumulated for decades. Specific toxicants (or mixtures thereof) may accelerate ALS development following a multistep hypothesis or act synergistically with other service-linked exposures (e.g. head trauma/concussions). Such possibilities could explain the lower age of onset observed in veterans compared to civilians. Identifying high-risk exposures within vulnerable populations is key to understanding ALS etiopathogenesis and is urgently needed to act upon modifiable risk factors for military personnel who deserve enhanced protection during their years of service, not only for their short-term, but also long-term health.

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.

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.

Rare variant analyses validate known ALS genes in a multi-ethnic population and identifies ANTXR2 as a candidate in PLS

Background Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting over 300,000 people worldwide. It is characterized by the progressive decline of the nervous system that leads to the weakening of muscles which impacts physical function. Approximately, 15% of individuals diagnosed with ALS have a known genetic variant that contributes to their disease. As therapies that slow or prevent symptoms continue to develop, such as antisense oligonucleotides, it is important to discover novel genes that could be targets for treatment. Additionally, as cohorts continue to grow, performing analyses in ALS subtypes, such as primary lateral sclerosis (PLS), becomes possible due to an increase in power. These analyses could highlight novel pathways in disease manifestation. Methods Building on our previous discoveries using rare variant association analyses, we conducted rare variant burden testing on a substantially larger multi-ethnic cohort of 6,970 ALS patients, 166 PLS patients, and 22,524 controls. We used intolerant domain percentiles based on sub-region Residual Variation Intolerance Score (subRVIS) that have been described previously in conjunction with gene based collapsing approaches to conduct burden testing to identify genes that associate with ALS and PLS. Results A gene based collapsing model showed significant associations with SOD1 , TARDBP , and TBK1 (OR = 19.18, p  = 3.67 × 10^–39; OR = 4.73, p  = 2 × 10^–10; OR = 2.3, p  = 7.49 × 10^–9, respectively). These genes have been previously associated with ALS. Additionally, a significant novel control enriched gene, ALKBH3 ( p  = 4.88 × 10^–7), was protective for ALS in this model. An intolerant domain-based collapsing model showed a significant improvement in identifying regions in TARDBP that associated with ALS (OR = 10.08, p  = 3.62 × 10^–16). Our PLS protein truncating variant collapsing analysis demonstrated significant case enrichment in ANTXR2 ( p  = 8.38 × 10^–6). Conclusions In a large multi-ethnic cohort of 6,970 ALS patients, collapsing analyses validated known ALS genes and identified a novel potentially protective gene, ALKBH3 . A first-ever analysis in 166 patients with PLS found a candidate association with loss-of-function mutations in ANTXR2 .

What is the nature and impact of cognitive difficulties following hormonal treatments for prostate cancer?: An interpretative phenomenological analysis

Objective Prostate cancer hormonal treatments (e.g. androgen deprivation therapy) yield clinical benefits. However, there is increasing evidence these treatments may adversely impact cognitive functioning. This study aimed to qualitatively characterise the nature and impact of cognitive difficulties following these treatments. Methods Prostate cancer survivors (PCS) self-reporting cognitive difficulties following hormonal treatments (via an online survey) and their partners were invited to participate in semi-structured interviews. Telephone or videoconferencing interviews were conducted, then transcribed, double-coded and analysed using the Framework Method, following the principles of Interpretative Phenomenological Analysis. Results Eleven participants (six PCS and five partners) were interviewed. PCS reported a range of cognitive difficulties, verified by their partners, including forgetfulness, “fogginess”, fatigue and slowed processing speed. For some PCS, word-finding difficulties, tangential speech and memory problems were apparent during interviews. The aetiology of the reported cognitive difficulties was unclear as it was attributed to a possible combination of cancer treatments, compounding side-effects (e.g. fatigue, sleep problems, hot flashes), exacerbation of pre-existing conditions and/or age-related changes. Cognitive difficulties were reported to have led to shifts in self-perception, interpersonal dynamics and increased emotionality. Engagement in cognitively-stimulating activities and reliance on compensatory strategies were reported to be helpful in managing some cognitive difficulties. All participants endorsed the potential benefits of neuropsychological intervention. Conclusions There are a diverse range of cognitive difficulties following hormonal treatments for prostate cancer experienced by PCS and their partners. Understanding the impact of these difficulties is important for the development of targeted neuropsychological interventions.

The Treatment Experience of Culturally and Linguistically Diverse Cancer Patients: A Scoping Review To Inform Radiotherapy Communication

Effective communication is a critical aspect of cancer care delivery. Specific communication challenges exist for culturally and linguistically diverse (CALD) patients and healthcare professionals throughout cancer treatment. This literature review aimed to examine the cancer treatment experience of CALD patients. A systematic scoping review was conducted in January 2025 using the databases; OVID Medline, EMBASE, and Cochrane Central. Search parameters included cancer, cultural diversity, language, and patient experience. Adhering to PRISMA guidelines and an established PICOS framework, 47 articles were selected for review. Exclusion criteria omitted duplicates, non-English language publications, articles published prior to 2010, and those unrelated to oncology and/or CALD communities. Three overarching themes were established from the literature; (1) education & empowerment, (2) quality care provision, and (3) system-level change. Communication barriers impacted the experience of cancer care and compromised treatment outcomes. Translated resources and staff cultural awareness training were observed to be beneficial for CALD patients. There is justified need to improve communication supports and resources for CALD patients and their clinicians. However, radiotherapy (RT) specific research is required to safely implement, contextualise and validate findings – informing bespoke communication improvement opportunities within RT practice.

Resource-Adapted Approach in Cancer Care

Childhood cancer care and outcomes differ widely from country to country due to the inequitable global distribution of medical and surgical resources, sociocultural factors, and biological tumor characteristics. Adapting treatment strategies to the local healthcare system and population is vital to achieve the largest number of children in disease remission, while minimizing the rate of treatment related mortality and complications. By 2050, the total number of new cases of childhood cancer globally is estimated to be 13.7 million, of which 6.1 million (45%) will remain undiagnosed with the current health system disparities. Survival rates for childhood cancer in North America are more than tenfold that in Eastern Africa. Without implementing effective measures to address global barriers to childhood cancer care, there will be approximately 11.1 million preventable deaths in the next 30 years, of which 84% will be in low- and middle-income countries (LMICs). Surgically amenable childhood cancers, such as central nervous system (CNS) tumors, retinoblastoma, and renal tumors, represent a large proportion of the disability-adjusted life years (DALYs) for childhood cancers. Parallel to this, an estimated 1.7 billion children and adolescents do not have access to safe, effective, and timely surgical care. In LMICs, less than 8% of children have access to surgical care, with a critical shortage of surgeons with specialist experience in pediatric cancer surgery. In addition, adjuncts such as chemotherapy, radiotherapy, immunotherapy, and comprehensive multidisciplinary children’s oncology care are often deficient in the LMIC setting. A comprehensive scale-up of interventions is required to address these global inequalities in cancer care, including raising public awareness, offering social support and financial protection to caregivers, improving access to primary care, strengthening referral services, and ensuring the availability of quality care.

Development and evaluation of imiquimod-loaded nanoemulsion-based gel for the treatment of skin cancer

Background The human skin, as the body’s largest organ, is particularly sensitive to many chemical mutagens and carcinogens encountered in daily life. Skin cancer has become a notable global health concern, partly due to increased exposure to environmental pollutants and UV rays. Various treatments are available to treat skin cancer. Imiquimod is approved for the treatment of actinic keratosis and basal cell carcinoma. The present investigation aimed to develop nanoemulsion-based gel with imiquimod (2.5% w/w) and carbopol ultrez 10 NF using a modified method to enhance the solubility, permeation, and therapeutic effectiveness of imiquimod to treat skin cancer. Combinations of rose oil and oleic acid, with Tween 20/Propylene glycol as Smix, were used in the formulation. The formulation underwent evaluation for parameters such as % drug content, in vitro drug diffusion studies, viscosity, skin irritation, in vitro cytotoxicity assay (MTT assay) and the DMBA/ croton oil skin cancer in vivo model. Results The formulation showed a minimum globule size of 118 nm, a zeta potential– 56.26 mV, a PDI of 0.378 and a drug content of 99.77%. In vitro drug release exhibited 45.00% of imiquimod release within 8 h, while approximately 34.32% release was found from the commercial cream. The imiquimod-loaded nanoemulsion-based gel showed significant cytotoxicity ( p  < 0.001) against the A431 cell line compared to Imiquad cream . The IC_50 value of the imiquimod-loaded nanoemulsion-based gel was noted to be 10.76 ± 2.54 µg/mL. In vivo results showed a significant reduction in tumor incidence (16.66%), tumor volume (140.26 ± 3.48 mm^3), tumor burden (5.50 mm^3) and tumor mass (0.66 ± 0.05 g) compared with the DMBA/croton oil carcinogen treatment control group. Histopathological finding showed the absence of keratinized pearls, epidermal hyperplasia, and acanthosis in the formulation treated group. Conclusion The results revealed that the nanoemulsion-based gel, with half the IMQ concentration of the commercial cream and incorporating Carbopol Ultrez 10NF, is a promising method for treating skin carcinogenesis. It potentially reduces dose-dependent side effects and demonstrating enhanced efficacy. Graphical abstract

Cancer systems immunology reveals myeloid—T cell interactions and B cell activation mediate response to checkpoint inhibition in metastatic breast cancer

Sensitization of the immune-suppressed tumor microenvironment (TME) of breast cancer by histone deacetylase inhibition shows promise, but the mechanisms of sensitization are unknown. We investigated the TME of breast-to-lung metastases by combining experimental and clinical data with theory. Knowledge-guided subclustering of single-cell RNA-sequencing data and cell circuits analysis identified 39 cell states and salient interactions, of which myeloid, T cell and B cell subpopulations were most affected by treatment. Using functional immunologic assays, we verified that inhibition of the ICAM pathway partially recapitulated treatment effects. Mathematical modeling of tumor-immune dynamics confirmed that tumor reduction required simultaneous modulation of multiple TME interactions. We found evidence that treatment affected anti-tumor antibody production. Analysis of patient biopsies via spatial proteomics corroborated preclinical findings: in responders we observed increased B cell activation, mature tertiary lymphoid structures, and increased CD8+ T cell—macrophage distances with treatment. Overall, this study provides a framework for the discovery of cell-cell interactions that govern responses in complex TMEs. STATEMENT OF SIGNIFICANCE: This study provides a framework for the discovery of cell-cell interactions that control responses in complex TMEs. We not only identify impactful tumor immunologic interactions that facilitate sensitization of the metastatic TME but also demonstrate how interdisciplinary data integration fuels cancer systems immunology to accelerate discovery of mechanisms of successful immunotherapeutic response in breast cancers and other previously unresponsive solid tumor types.

Detection of skin cancer through hybrid color features and soft voting ensemble classifier

Recent diagnostic observations have raised concerns regarding malignancy issues in untreated skin cancer. Over the last two decades, dermoscopy has led to non-invasive clinical diagnosis of cancer types, typically Melanoma, Basal Cell Carcinoma (BCC), and Squamous Cell Carcinoma (SCC). Raising concerns coupled with clinical (image) data motivated this study which focuses on a Machine Learning (ML) based approach to properly classify the skin cancer into one of the three types mentioned earlier. In this regard, the study also uses a unique image pre-processing network which cleans and processes the images prior to injection into the ML models. In this study, the image pre-processing network primarily comprises of Gaussian noise removal, blackhat operation, and Otsu segmentation. Post processing, a novel Hybrid Color Features (HCF) algorithm has been used for feature extraction in terms of texture, shape and color features. Outlier handling was done using the z-score rescaling approach. In comparison to the traditional ML approach, it had been observed that the novel soft voting ensemble classifier (SVEC) exhibited better results. The SVEC along with boosting fetched an accuracy of 95.5%, 96.3%, and 96.7% for Melanoma, BCC, and SCC skin cancers, respectively. Peer comparison with prior ML initiatives further confirmed the novelty of the SVEC approach.

Towards Global AI-Driven Cervical Cancer Screening

The global elimination of cervical cancer is a key public health goal set by the World Health Organization (WHO), with screening programs reducing mortality by up to 80%. However, access to experts and biopsy services is limited in low- to middle-income countries (LMICs). Deep learning (DL)-based algorithms offer promising support for screening, but most existing approaches have been developed and validated on private datasets from single countries. We present the first DL-based approach to cervical cancer screening validated on data from multiple countries. Technically, we phrase the problem of detecting and classifying lesions in colposcopy images as a multi-task learning problem, in which we simultaneously perform image-level classification and lesion segmentation. Our model was trained on a private data set of acid stain colposcopy images with manually generated lesion segmentation masks and corresponding histopathological results, employing extensive data augmentation to address image variability. In an in-distribution validation with pathology results serving as ground truth, our algorithm outperformed medical experts (Balanced Accuracy: 0.68 vs 0.64) in CIN1- (Cervical intraepithelial neoplasia grade 1 or lower) versus CIN2+ (grade 2 or higher) classification. External validation on four colposcopy data sets from four countries featuring radical differences in prevalence and patient characteristics yielded superior performance of our method compared to baseline methods. Performance variability across countries was high with AUC values ranging from 0.54 - 0.80. Overall, algorithm performance varied with age, transformation zone (cervical area most prone to lesion development), presence of comorbidities and pathognomonic signs, with comorbidities having by far the largest negative effect. Future work should focus on improving model robustness and generalizability. ;20 pages, 9 figures

Dynamic Hypergraph Representation for Bone Metastasis Cancer Analysis

Bone metastasis analysis is a significant challenge in pathology and plays a critical role in determining patient quality of life and treatment strategies. The microenvironment and specific tissue structures are essential for pathologists to predict the primary bone cancer origins and primary bone cancer subtyping. By digitizing bone tissue sections into whole slide images (WSIs) and leveraging deep learning to model slide embeddings, this analysis can be enhanced. However, tumor metastasis involves complex multivariate interactions with diverse bone tissue structures, which traditional WSI analysis methods such as multiple instance learning (MIL) fail to capture. Moreover, graph neural networks (GNNs), limited to modeling pairwise relationships, are hard to represent high-order biological associations. To address these challenges, we propose a dynamic hypergraph neural network (DyHG) that overcomes the edge construction limitations of traditional graph representations by connecting multiple nodes via hyperedges. A low-rank strategy is used to reduce the complexity of parameters in learning hypergraph structures, while a Gumbel-Softmax-based sampling strategy optimizes the patch distribution across hyperedges. An MIL aggregator is then used to derive a graph-level embedding for comprehensive WSI analysis. To evaluate the effectiveness of DyHG, we construct two large-scale datasets for primary bone cancer origins and subtyping classification based on real-world bone metastasis scenarios. Extensive experiments demonstrate that DyHG significantly outperforms state-of-the-art (SOTA) baselines, showcasing its ability to model complex biological interactions and improve the accuracy of bone metastasis analysis. ;Comment: 12 pages,11 figures

Unraveling the Anticancer Potential of SSRIs in Prostate Cancer by Combining Computational Systems Biology and In Vitro Analyses

[Image: see text] Selective serotonin reuptake inhibitors (SSRIs) are known to have anticancer activity against different types of cancer. In this study, an integrative informatics approach was applied to identify compound and genetic perturbations that produce similar effects to SSRIs to formulate systems biology hypotheses and identify biological pathways involved in the putative anticancer effects of SSRIs in prostate cancer. An 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay assessed the antiproliferative effects of SSRIs and drug combinations. Cell death mechanisms were studied using annexin V-FITC/PI staining, and the cell cycle analysis was carried out by counterstaining with propidium iodide. Relative gene expression was assessed using a real-time polymerase chain reaction (PCR). Computational results hypothesized that SSRIs could potentially exert anticancer effects in prostate cancer cell lines by modulating apoptotic and tumorigenesis pathways and significantly inhibiting the growth of prostate cancer cells in a time and concentration-dependent manner. The combination of SSRIs with cisplatin, 5-fluorouracil, and raloxifene resulted in either synergistic or additive effects. SSRIs resulted in a significant increase in the early and late apoptotic activity in PC3 cells. Dapoxetine, paroxetine, and sertraline resulted in cell cycle arrest at the G0/G1 phase. Treatment with either dapoxetine or paroxetine decreases the expression of Bcl-2, CASP8, DR5, and VEGF. At the same time, sertraline decreases the expression of Bcl-2 and VEGF and increases the expression of CASP8 and DR5. Results revealed that SSRIs can potentially act as antiproliferative agents against prostate cancer cells, and their activity is mediated through different signaling pathways.

RIG-I-dependent tumor-intrinsic type I interferon signaling restricts growth in breast cancer 3D culture

Discovery of key growth drivers that can be targeted for therapy is a central goal in cancer research. While high-throughput CRISPR screens have revolutionized our ability to identify gene dependencies in cancer, most large-scale screens are conducted in two-dimensional (2D) culture systems that fail to recapitulate tumor organization and behavior. To uncover architecture-dependent vulnerabilities in breast cancer, we performed parallel CRISPR interference (CRISPRi) screens in 2D and three-dimensional (3D) cultures of MCF7 cells, an estrogen receptor–positive (ER+) breast cancer model representative of a high risk of relapse, luminal subtype. Knockdown of IFNAR2 and TYK2 conferred a growth advantage in 3D cultures, implicating type I interferon signaling as a tumor-intrinsic suppressor of proliferation in 3D spheroids. Transcriptomic and functional analyses demonstrated that type I IFN signaling is endogenously activated in 3D spheroids via RIG-I–mediated sensing of cytosolic double-stranded RNA, leading to TBK1 activation and induction of interferon-stimulated genes (ISGs). This tumor-intrinsic IFN response slowed proliferation in 3D culture, independent of exogenous stimuli or the presence of immune cells. Analysis of bulk, single-cell, and spatial transcriptomic datasets from breast cancer patients revealed that a subset of tumors exhibit elevated IFN signaling in cancer cells, including in immune-depleted tumor cores, consistent with a tumor-intrinsic IFN signature. Our findings uncover an IFN-mediated growth-suppressive program shaped by 3D tumor architecture, and contribute towards a better understanding of the role of tumor-intrinsic IFN activity.

Quantum Medicine and the Immune System

Currently, physics plays a central role in medicine, imaging diagnostics, and in oncology, radiotherapy and hadron therapy, photodynamic therapy or photochemotherapy, oncological hyperthermia, thermal ablation, electrochemotherapy (ETC), and irreversible electroporation (IRE), ending with tumor treating fields (TTF). The immune system consists of a very complex cellular structure which, thanks to soluble elements and strict cell-cell connections, creates interactions between cells but also with cells which are part of other organs and systems. This network is the principal coordinative structure that enables the immune system to maintain its dynamic equilibrium (homeostasis) thanks to signals with activation or inhibitory function and, in the meantime, to adapt its actual response to stimuli coming from the environment. When evaluating the possible connection between an electromagnetic source and the biological effects induced by it, it is very important to take into account all of the different regions which are part of the electromagnetic spectrum. The type of the interaction with a biological substance is determined by the frequency or wavelength of the source, the waveform, the amplitude of the signal, the exposure time, a series of electrical parameters characteristic of the signal, as well as the geomagnetic field and, in general, from the environment in which the electromagnetic field ( EMF) EMF Electromagnetic field (EMF) is applied. Furthermore, the response of human cells to EMFs also depends on their specific characteristics, such as type, metabolic activity, and physiological function. Given that the immune system has a fundamental role in the control of various pathologies and in the growth of tumors, a lot of laboratories have developed an interest in extremely low-frequency electromagnetic field ( ELF-EMF) ELF-EMF Extremely Low Frequency Electro-Magnetic Fields (ELF-EMF) stimulation on different cell types and processes. Furthermore, some other authors have focused on the effects that ELF-EMFs have in terms of expression of specific genes and in terms of signal transduction pathways, although, to date, the experimental data derived from these studies seem to be quite controversial. Therefore, we wanted to carry out a review of the literature aimed at analyzing and clarifying the biological effects and the relative therapeutic significance of ELF-EMF.

Interpretable Breast Cancer Risk Stratification Using Statistical Feature Engineering on Thermal Images

This research solves the “black box” problem of AI implementation in imaging by introducing a transparent, statistically grounded approach to breast cancer risk stratification via infrared thermography without compromising performance. Using the public DMR-IR dataset, statistical feature engineering was applied to training data by extracting first- and second-order statistical features. After ensuring non-normality with a Shapiro-Wilk test, feature significance was established with the Mann-Whitney U test. LASSO regularization selected the five most predictive features: mean, standard deviation, kurtosis, correlation, and energy. To counteract class imbalance, SMOTE was applied, and two machine learning models—logistic regression and random forest (classifier)—were trained on the balanced data and then evaluated on an unseen test dataset. Reporting an AUC of 0.98 over logistic regression’s 0.96 reflects stringent statistical feature engineering and great generalization, creating a strong and interpretable model for breast cancer diagnosis in thermal images, and instills more clinical confidence in AI-based diagnostic systems.

Reasoning Language Model for Personalized Lung Cancer Screening

Accurate risk assessment in lung cancer screening is critical for enabling early cancer detection and minimizing unnecessary invasive procedures. The Lung CT Screening Reporting and Data System (Lung-RADS) has been widely used as the standard framework for patient management and follow-up. Nevertheless, Lung-RADS faces trade-offs between sensitivity and specificity, as it stratifies risk solely based on lung nodule characteristics without incorporating various risk factors. Here we propose a reasoning language model (RLM) to integrate radiology findings with longitudinal medical records for individualized lung cancer risk assessment. Through a systematic study including dataset construction and distillation, supervised fine-tuning, reinforcement learning, and comprehensive evaluation, our model makes significant improvements in risk prediction performance on datasets in the national lung screening trial. Notably, RLM can decompose the risk evaluation task into sub-components, analyze the contributions of diverse risk factors, and synthesize them into a final risk score computed using our data-driven system equation. Our approach improves both predictive accuracy and monitorability through the chain of thought reasoning process, thereby facilitating clinical translation into lung cancer screening.

Series on the Lancet Oncology Commission on Global Cancer Surgery: Introduction—Intent and Content

Background Surgery plays a key role in the multi-disciplinary cancer care pathway. Nearly 80% of patients with solid tumors will require surgical intervention during the course of their disease. Unfortunately, the vast majority of these patients do not have access to safe, timely, high-quality, and affordable cancer surgical care. The first Lancet Oncology Commission on Global Cancer Surgery shone a light on this grave situation and outlined some strategies to address them. The second Lancet Oncology Commission on Global Cancer Surgery (TLO- II) was conceived to continue the work of its predecessor by developing a roadmap of practical solutions to propel improvements in cancer surgical care globally. Methods The Commission was developed by involving approximately 50 cancer care leaders and experts from different parts of the world to ensure diversity of input and global applicability. Results The Commission identified nine solutional domains that are considered essential to deliver safe, timely, high-quality, and affordable cancer surgical care. These nine domains were further refined to develop solutions specific to each of the six World Health Organization regions. Based on the above solutions, we developed eight action items that are intended to propel improvements in cancer surgical care on the global stage. Conclusions The second Lancet Oncology Commission on Global Cancer Surgery builds on the first Commission by developing a pragmatic roadmap of practical solutions that we hope will ensure access to safe, timely, high-quality, and affordable cancer surgical care for everyone regardless of their socioeconomic status or geographic location.

Empowering Kuwaiti adults with cancer: oncology nurses’ perspectives on home-based self-management

Purpose Breast and colorectal cancers are among the most prevalent malignancies in Kuwait. As cancer care shifts toward outpatient and home-based models, this study explored oncology nurses’ perspectives on how Kuwaiti adults with these cancers navigate home-based self-management and identified strategies used to empower patients in this process. Methods A qualitative study using a constructivist grounded theory approach was conducted in 2022 at a specialized cancer care center in Kuwait. Semi-structured interviews were held with seven purposively selected oncology nurses, each with at least 2 years of oncology experience. Data were analyzed through constant comparison and theoretical coding. Results The central theme, “Holistic empowerment and self-management in cancer care amidst systemic challenges,” reflected the nurses’ multifaceted role in supporting patients’ recovery and adaptation. Nurses emphasized that structured health education and family involvement enhanced confidence in managing medications, wound care, and rehabilitation at home. Emotional and psychological support during care transitions, particularly at discharge or palliative stages, strengthened patient motivation and trust. However, resource shortages, language barriers, and socioeconomic disparities constrained empowerment efforts. Collaboration with community organizations provided essential financial and emotional support to fill systemic gaps. Conclusion Oncology nurses play a pivotal role in facilitating self-management among cancer survivors in Kuwait. Strengthening their capacity through targeted training, cultural competence, and policy support can improve survivorship outcomes. These findings underscore the need for culturally competent, system-supported empowerment strategies to enhance survivorship care.

Histomorphology-Guided Prototypical Multi-Instance Learning for Breast Cancer WSI Classification

Histomorphology is crucial in cancer diagnosis. However, existing whole slide image (WSI) classification methods struggle to effectively incorporate histomorphology information, limiting their ability to capture key pathological features. Particularly when the number of instances within a bag is large and their features are complex, it becomes challenging to accurately identify instances decisive for the bag label, making these methods prone to interference from ambiguous instances. To address this limitation, we propose a novel Histomorphology-Guided Prototypical Multi-Instance Learning (HGPMIL) framework that explicitly learns histomorphology-guided prototypical representations by incorporating tumor cellularity, cellular morphology, and tissue architecture. Specifically, our approach consists of three key components: (1) estimating the importance of tumor-related histomorphology information at patch-level based on medical prior knowledge; (2) generating representative prototypes through histomorphology-prototypical clustering; and (3) enabling WSI classification through histomorphology-guided prototypical aggregation. HGPMIL adjusts the decision boundary by incorporating histomorphological importance to reduce instance label uncertainty, thereby reversely optimizing the bag-level boundary. Experimental results demonstrate its effectiveness, achieving high diagnostic accuracy for molecular subtyping, cancer subtyping and survival analysis. The code will be made available at https://github.com/Badgewho/HMDMIL. ;11 pages,8 figures

On-target toxicity limits the efficacy of CDK11 inhibition against cancers with 1p36 deletions

The cyclin-dependent kinase CDK11 is an understudied kinase that has been the subject of conflicting reports regarding its function in cancer. Here, we combine genetic and pharmacological approaches to demonstrate that CDK11 is a critical regulator of cancer cell survival that is required for RNA splicing and the expression of homologous recombination genes. Inhibition of CDK11 disrupts genome stability, promotes the retention of intronic sequences in mature mRNAs, and induces synthetic lethality with PARP inhibitors. Through integrative analysis of functional genomics datasets, we identify heterozygous deletions of chromosome 1p36 - which encompasses CDK11 and its activating cyclin CCNL2 - as a recurrent and predictive biomarker of sensitivity to CDK11 inhibition. To assess the therapeutic potential of CDK11, we develop MEL-495R, a selective and orally bioavailable CDK11 inhibitor. Additionally, we establish a genetically-engineered mouse model that allows us to differentiate between the on-target and off-target effects of CDK11 inhibitors in vivo. Using this platform, we demonstrate that MEL-495R induces widespread on-target toxicity, revealing a narrow therapeutic index. Together, these findings define CDK11 as a core cancer dependency, uncover a chromosomal deletion that sensitizes tumors to CDK11 inhibition, and provide a generalizable strategy for deconvolving drug efficacy and toxicity in vivo for novel oncology targets.

Bladder Cancer Diagnosis with Deep Learning: A Multi-Task Framework and Online Platform

Clinical cystoscopy, the current standard for bladder cancer diagnosis, suffers from significant reliance on physician expertise, leading to variability and subjectivity in diagnostic outcomes. There is an urgent need for objective, accurate, and efficient computational approaches to improve bladder cancer diagnostics. Leveraging recent advancements in deep learning, this study proposes an integrated multi-task deep learning framework specifically designed for bladder cancer diagnosis from cystoscopic images. Our framework includes a robust classification model using EfficientNet-B0 enhanced with Convolutional Block Attention Module (CBAM), an advanced segmentation model based on ResNet34-UNet++ architecture with self-attention mechanisms and attention gating, and molecular subtyping using ConvNeXt-Tiny to classify molecular markers such as HER-2 and Ki-67. Additionally, we introduce a Gradio-based online diagnostic platform integrating all developed models, providing intuitive features including multi-format image uploads, bilingual interfaces, and dynamic threshold adjustments. Extensive experimentation demonstrates the effectiveness of our methods, achieving outstanding accuracy (93.28%), F1-score (82.05%), and AUC (96.41%) for classification tasks, and exceptional segmentation performance indicated by a Dice coefficient of 0.9091. The online platform significantly improved the accuracy, efficiency, and accessibility of clinical bladder cancer diagnostics, enabling practical and user-friendly deployment. The code is publicly available. Our multi-task framework and integrated online tool collectively advance the field of intelligent bladder cancer diagnosis by improving clinical reliability, supporting early tumor detection, and enabling real-time diagnostic feedback. These contributions mark a significant step toward AI-assisted decision-making in urology.

Prevalence of prostate cancer and its grade group stage at diagnosis in patients treated with prostatectomy in rural south western Uganda

Background Prostate cancer is the most frequently diagnosed cancer among men in the world. Uganda and Zimbabwe have been reported to have highest incidence rates of prostate cancer in sub-Saharan Africa. There are no urologists and no prostate cancer diagnostic facilities in rural communities in south western Uganda. Men with lower urinary tract symptoms are treated with prostatectomy by midlevel healthcare workers and general surgeons without prior prostate cancer screening. Histological diagnosis relies on the prostate tissue retrieved during surgery and the results may take several months. Prostate cancer care in southwestern Uganda remains uncoordinated and has not been documented before. This study aimed to establish and document the burden of prostate cancer in rural southwestern Uganda as a basis for further research. Methods This was a retrospective study conducted in hospitals in rural southwestern Uganda. We used hospital records as primary source of data. Histology results of patients treated with prostatectomy during the five-year period (2019–2023) were retrieved and data extracted for analysis. 1013 patients were included in the study. Univariate data analysis was done with STATA version 17.0. The study received ethics clearance for Kabale university REC and Uganda National council of Science and Technology. Results The average age of patients in this study was 70.6 year (range 54–102 years). Prostate cancer was present in 232 (22.9%) patients. Seventeen (7.3%) patients with prostate cancer were below sixty years. Most (75.4%) of the patients with prostate cancer in this study had low to intermediate risk disease. Perineural tumor infiltration was present in 28.9% of prostate cancer patients. Conclusion More than 1 in 5 men (22.9%) with lower urinary tract symptoms treated with prostatectomy in the study period in southwestern Uganda had prostate cancer. Majority of patients (75.4%) had low to intermediate risk disease. These findings highlight the urgent need for systematic improvements in prostate cancer care, including sensitization of both health workers and the general population, establishment of early screening and regional diagnostic and treatment facilities to enhance patient outcomes in resource -limited rural communities in Uganda. Clinical trial number Not applicable.

Transient CB2 receptor activation triggers irreversible luminal differentiation via chromatin remodeling in breast cancer

Cellular plasticity enables cancer cells to escape therapy by adopting stem-like or alternate lineage states. Here, we identify a mechanism by which cannabinoid receptor 2 (CB2R) activation promotes irreversible lineage commitment in breast cancer. Using patient-derived and murine organoids, we show that brief, low-dose exposure to CB2R agonists—either phytogenic or synthetic—induces a basal-to-luminal transition, accompanied by reduced self-renewal, invasiveness, and tumor-initiating potential. These changes are retained under conditions that normally promote dedifferentiation, including fibroblast co-culture, immune pressure, and mechanical shear stress. Mechanistically, CB2R engagement initiates a transient chromatin remodeling program, marked by early expression of pluripotency-associated genes followed by silencing and differentiation commitment. This epigenetically stabilized state renders tumor cells more responsive to tamoxifen and limits the emergence of resistant clones. Our findings uncover a previously unrecognized role for CB2R in modulating cancer cell identity and suggest new opportunities to constrain tumor plasticity by directing differentiation through a drug-responsive pathway.

Development of ultrasound molecular contrast agents by site-specific conjugation of gas vesicles to antibodies

Ultrasound technology is a powerful tool for medical imaging and diagnosis especially when used in conjunction with a contrast agent to target proteins of interest. However, current ultrasound contrast agents are lacking the combination of specificity, stability, and size optimization. In this study we introduce and confirm the feasibility of using nanometer sized gas vesicles (GV) chemically click conjugated to antibodies (mAb) to make the first site-specific mAb-GV conjugates as a durable cancer cell targeting ultrasound molecular contrast agents. Protein expression of human epidermal growth factor receptor 2 (HER2) and programmed death-ligand 1 (PD-L1) were tested along with the antibody targeting efficiency using cancer cell lines and primary cells isolated from tumor bearing mice. The mAb conjugation was optimized to a site-specific method using the mAb glycans and tested with the addition of a clinically used mAb to target trophoblast cell surface antigen 2 (Trop-2). The developed contrast agents utilize the stability of GVs and the specificity of antibodies to label cancer biomarkers in various types of tumors for ultrasound imaging.

Scalable and Loosely-Coupled Multimodal Deep Learning for Breast Cancer Subtyping

Healthcare applications are inherently multimodal, benefiting greatly from the integration of diverse data sources. However, the modalities available in clinical settings can vary across different locations and patients. A key area that stands to gain from multimodal integration is breast cancer molecular subtyping, an important clinical task that can facilitate personalized treatment and improve patient prognosis. In this work, we propose a scalable and loosely-coupled multimodal framework that seamlessly integrates data from various modalities, including copy number variation (CNV), clinical records, and histopathology images, to enhance breast cancer subtyping. While our primary focus is on breast cancer, our framework is designed to easily accommodate additional modalities, offering the flexibility to scale up or down with minimal overhead without requiring re-training of existing modalities, making it applicable to other types of cancers as well. We introduce a dual-based representation for whole slide images (WSIs), combining traditional image-based and graph-based WSI representations. This novel dual approach results in significant performance improvements. Moreover, we present a new multimodal fusion strategy, demonstrating its ability to enhance performance across a range of multimodal conditions. Our comprehensive results show that integrating our dual-based WSI representation with CNV and clinical health records, along with our pipeline and fusion strategy, outperforms state-of-the-art methods in breast cancer subtyping.

A study on Deep Convolutional Neural Networks, Transfer Learning and Ensemble Model for Breast Cancer Detection

In deep learning, transfer learning and ensemble models have shown promise in improving computer-aided disease diagnosis. However, applying the transfer learning and ensemble model is still relatively limited. Moreover, the ensemble model's development is ad-hoc, overlooks redundant layers, and suffers from imbalanced datasets and inadequate augmentation. Lastly, significant Deep Convolutional Neural Networks (D-CNNs) have been introduced to detect and classify breast cancer. Still, very few comparative studies were conducted to investigate the accuracy and efficiency of existing CNN architectures. Realising the gaps, this study compares the performance of D-CNN, which includes the original CNN, transfer learning, and an ensemble model, in detecting breast cancer. The comparison study of this paper consists of comparison using six CNN-based deep learning architectures (SE-ResNet152, MobileNetV2, VGG19, ResNet18, InceptionV3, and DenseNet-121), a transfer learning, and an ensemble model on breast cancer detection. Among the comparison of these models, the ensemble model provides the highest detection and classification accuracy of 99.94% for breast cancer detection and classification. However, this study also provides a negative result in the case of transfer learning, as the transfer learning did not increase the accuracy of the original SE-ResNet152, MobileNetV2, VGG19, ResNet18, InceptionV3, and DenseNet-121 model. The high accuracy in detecting and categorising breast cancer detection using CNN suggests that the CNN model is promising in breast cancer disease detection. This research is significant in biomedical engineering, computer-aided disease diagnosis, and ML-based disease detection.

Prevalence and determinants of sleep problems in cancer survivors compared to a normative population: a PROFILES registry study

Purpose To (1) identify the prevalence of sleep problems in cancer survivors across cancer types and survivorship durations compared to a normative population and (2) determine demographic, clinical, lifestyle, and psychosocial determinants. Method Cancer survivors diagnosed between 2008 and 2015 ( N  = 6736) and an age- and sex-matched normative cohort ( n  = 415) completed the single sleep item of the EORTC QLQ-C30: Have you had trouble sleeping? Participants who responded with “quite a bit”/ “very much” were categorized as poor sleepers. A hierarchical multinomial logistic regression was used to identify determinants of sleep problems. Result The prevalence of sleep problems was higher in cancer survivors (17%) compared to the normative population (11%) ( p  < 0.001), varied across cancer types (10–26%) and did not vary based on survivorship duration. The full model showed that survivors who were female (adjusted odds ratio (AOR) 2.26), overweight (AOR 1.50), had one (AOR 1.25) and ≥ 2 comorbidities (AOR 2.15), were former (AOR 1.30) and current (AOR 1.53) smokers and former alcohol drinkers (AOR 1.73), had a higher level of fatigue (AOR 1.05), anxiety (AOR 1.14), depression (AOR 1.11), and cognitive illness perceptions (AOR 1.02), had a higher odds for sleep problems. Higher education compared to lower education (AOR 0.67), having a partner (AOR 0.69), and obesity compared to normal BMI (AOR 0.86) were protective to sleep problems as well as high physical activity before adjusting for psychological factors (AOR 0.91). Conclusion Modifiable determinants of sleep problems include physical activity, fatigue, anxiety, depression, and illness perception. Implications for Cancer Survivors Sleep problems after cancer deserve clinical attention. They may be improved by addressing modifiable lifestyle factors: increasing physical activity, stop smoking, and reducing alcohol consumption. As fatigue, depression, and illness perception seem related to sleep problems, lifestyle improvements may also improve these outcomes.

Epidemiology and socioeconomic correlates of colorectal cancer in Asia in 2020 and its projection to 2040

Asia bears a disproportionate and rapidly rising burden of colorectal cancer (CRC). However, the incidence and mortality trends vary significantly between Asian countries, mainly due to the diversity of socioeconomic factors and the implementation of screening programs. This study aimed to report the contemporary distribution, socioeconomic correlates, and projections for future trends of CRC across Asia. The Global Cancer Observatory (GLOBOCAN) for the year 2020 was used to obtain data on prevalence, incidence, and mortality rates of CRC. We calculated mortality-to-incidence ratios (MIRs), age-standardized incidence and mortality rates (ASIR and ASMR), crude rates, numbers, and 5-year prevalent cases and rates by age, sex, and subregions of Asia. We assessed the correlation between indicators and human development index (HDI) and the ratio of current health expenditure (CHE) to gross domestic product (GDP) using Pearson’s correlation coefficient. Estimated incidence or mortality rates between 2025 and 2040 were calculated by multiplying age-specific rates for 2020 by the estimated population between 2025 and 2040. In Asia, the 5-year prevalence rate, ASIR, and ASMR of CRC were 55.60, 17.30, and 8.40 per 100,000, respectively. The highest crude incidence and mortality rates were in the 70 + age group. Males had higher ASIRs than females (20.80 vs. 14.00 per 100,000) in Asia. MIRs for men and women were 0.49 globally and 0.50 and 0.51 in Asia, respectively. A positive significant correlation was observed between HDI and both the ASIR and ASMR. A strong negative correlation was observed between HDI and MIR. The number of incident and mortality cases are estimated to increase by 71.10% and 85.10% in 2040, respectively. CRC is a significant public health concern in Asia, with substantially high incidence and mortality rates in East Asia and lower quality of care and survival in less developed regions of the continent. Resource allocation prioritizing population-based screenings alongside capacity building around specialized care centers is crucial across the Asian countries.

SE Journals in 2036: Looking Back at the Future We Need to Have

In 2025, SE publishing faces an existential crisis of scalability. As our communities swell globally and integrate fast-moving methodologies like LLMs, traditional peer-review practices are collapsing under the strain. The "bureaucratic anomaly" of monolithic review has become mathematically unsustainable, creating a stochastic "lottery" that punishes novelty and exhausts researchers. This paper, written from the perspective of 2036, documents potential solutions. Here, the editors of ASE, EMSE, IST, JSS, TOSEM and TSE dream a collective dream of a brighter future. In summary first we stopped fighting (The Journal Alliance). Then we fixed the process (The Lottery / Unbundling / Fixing the Benchmark Graveyard). And then we fixed the culture (Cathedrals/Bazaars).

DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference

Speculative decoding is an effective and lossless approach for accelerating LLM inference. However, existing widely adopted model-based draft designs, such as EAGLE3, improve accuracy at the cost of multi-step autoregressive inference, resulting in high drafting latency and ultimately rendering the drafting stage itself a performance bottleneck. Inspired by diffusion-based large language models (dLLMs), we propose DART, which leverages parallel generation to reduce drafting latency. DART predicts logits for multiple future masked positions in parallel within a single forward pass based on hidden states of the target model, thereby eliminating autoregressive rollouts in the draft model while preserving a lightweight design. Based on these parallel logit predictions, we further introduce an efficient tree pruning algorithm that constructs high-quality draft token trees with N-gram-enforced semantic continuity. DART substantially reduces draft-stage overhead while preserving high draft accuracy, leading to significantly improved end-to-end decoding speed. Experimental results demonstrate that DART achieves a 2.03x--3.44x wall-clock time speedup across multiple datasets, surpassing EAGLE3 by 30% on average and offering a practical speculative decoding framework. Code is released at https://github.com/fvliang/DART.

CORD: Bridging the Audio-Text Reasoning Gap via Weighted On-policy Cross-modal Distillation

Large Audio Language Models (LALMs) have garnered significant research interest. Despite being built upon text-based large language models (LLMs), LALMs frequently exhibit a degradation in knowledge and reasoning capabilities. We hypothesize that this limitation stems from the failure of current training paradigms to effectively bridge the acoustic-semantic gap within the feature representation space. To address this challenge, we propose CORD, a unified alignment framework that performs online cross-modal self-distillation. Specifically, it aligns audio-conditioned reasoning with its text-conditioned counterpart within a unified model. Leveraging the text modality as an internal teacher, CORD performs multi-granularity alignment throughout the audio rollout process. At the token level, it employs on-policy reverse KL divergence with importance-aware weighting to prioritize early and semantically critical tokens. At the sequence level, CORD introduces a judge-based global reward to optimize complete reasoning trajectories via Group Relative Policy Optimization (GRPO). Empirical results across multiple benchmarks demonstrate that CORD consistently enhances audio-conditioned reasoning and substantially bridges the audio-text performance gap with only 80k synthetic training samples, validating the efficacy and data efficiency of our on-policy, multi-level cross-modal alignment approach. ;13 pages, 4 figures

DMV-AVP: Distributed Multi-Vehicle Autonomous Valet Parking Using Autoware

This paper presents DMV-AVP, a distributed simulation of Multi-Vehicle Autonomous Valet Parking (AVP). The system was implemented as an application of the Distributed Multi-Autonomous Vehicle Architecture (DMAVA) for synchronized multi-host execution. Most existing simulation approaches rely on centralized or non-distributed designs that constrain scalability and limit fully autonomous control. This work introduces two modules built on top of DMAVA: 1) the Multi-Vehicle AVP Coordination Framework, composed of AVP Managers and a per-vehicle AVP Node, is responsible for global parking state tracking, vehicle queuing, parking spot reservation, lifecycle coordination, and conflict resolution across multiple vehicles, and 2) the Unity-Integrated YOLOv5 Parking Spot Detection Module, that provides real-time, vision-based perception within AWSIM Labs. Both modules integrate seamlessly with DMAVA and extend it specifically for multi-vehicle AVP operation, supported by a Zenoh communication layer that ensures high data accuracy and controllability across hosts. Experiments conducted on two- and three-host configurations demonstrate consistent coordination, conflict-free parking behavior, and scalable performance across distributed Autoware instances. The results confirm that the proposed DMV-AVP supports cooperative AVP simulation and establishes a foundation for future real-world and hardware-in-the-loop validation. Demo videos and source code are available at: https://github.com/zubxxr/multi-vehicle-avp ;6 pages, 3 figures, 3 tables. Submitted to IEEE IV 2026, Demo videos and source code available

Seeing to Think? How Source Transparency Design Shapes Interactive Information Seeking and Evaluation in Conversational AI

Conversational AI systems increasingly function as primary interfaces for information seeking, yet how they present sources to support information evaluation remains under-explored. This paper investigates how source transparency design shapes interactive information seeking, trust, and critical engagement. We conducted a controlled between-subjects experiment (N=372) comparing four source presentation interfaces - Collapsible, Hover Card, Footer, and Aligned Sidebar - varying in visibility and accessibility. Using fine-grained behavioral analysis and automated critical thinking assessment, we found that interface design fundamentally alters exploration strategies and evidence integration. While the Hover Card interface facilitated seamless, on-demand verification during the task, the Aligned Sidebar uniquely mitigated the negative effects of information overload: as citation density increased, Sidebar users demonstrated significantly higher critical thinking and synthesis scores compared to other conditions. Our results highlight a trade-off between designs that support workflow fluency and those that enforce reflective verification, offering practical implications for designing adaptive and responsible conversational AI that fosters critical engagement with AI generated content.

Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents

Tool-calling agents are increasingly deployed in real-world customer-facing workflows. Yet most studies on tool-calling agents focus on idealized settings with general, fixed, and well-specified tasks. In real-world applications, user requests are often (1) ambiguous, (2) changing over time, or (3) infeasible due to policy constraints, and training and evaluation data that cover these diverse, complex interaction patterns remain under-represented. To bridge the gap, we present Trajectory2Task, a verifiable data generation pipeline for studying tool use at scale under three realistic user scenarios: ambiguous intent, changing intent, and infeasible intents. The pipeline first conducts multi-turn exploration to produce valid tool-call trajectories. It then converts these trajectories into user-facing tasks with controlled intent adaptations. This process yields verifiable task that support closed-loop evaluation and training. We benchmark seven state-of-the-art LLMs on the generated complex user scenario tasks and observe frequent failures. Finally, using successful trajectories obtained from task rollouts, we fine-tune lightweight LLMs and find consistent improvements across all three conditions, along with better generalization to unseen tool-use domains, indicating stronger tool-calling ability.

PAS-Mamba: Phase-Amplitude-Spatial State Space Model for MRI Reconstruction

Joint feature modeling in both the spatial and frequency domains has become a mainstream approach in MRI reconstruction. However, existing methods generally treat the frequency domain as a whole, neglecting the differences in the information carried by its internal components. According to Fourier transform theory, phase and amplitude represent different types of information in the image. Our spectrum swapping experiments show that magnitude mainly reflects pixel-level intensity, while phase predominantly governs image structure. To prevent interference between phase and magnitude feature learning caused by unified frequency-domain modeling, we propose the Phase-Amplitude-Spatial State Space Model (PAS-Mamba) for MRI Reconstruction, a framework that decouples phase and magnitude modeling in the frequency domain and combines it with image-domain features for better reconstruction. In the image domain, LocalMamba preserves spatial locality to sharpen fine anatomical details. In frequency domain, we disentangle amplitude and phase into two specialized branches to avoid representational coupling. To respect the concentric geometry of frequency information, we propose Circular Frequency Domain Scanning (CFDS) to serialize features from low to high frequencies. Finally, a Dual-Domain Complementary Fusion Module (DDCFM) adaptively fuses amplitude phase representations and enables bidirectional exchange between frequency and image domains, delivering superior reconstruction. Extensive experiments on the IXI and fastMRI knee datasets show that PAS-Mamba consistently outperforms state of the art reconstruction methods.

UCPO: Uncertainty-Aware Policy Optimization

The key to building trustworthy large language models (LLMs) lies in endowing them with inherent uncertainty expression capabilities, thereby mitigating overconfident errors in high-stakes applications. However, existing RL paradigms such as GRPO often suffer from Advantage Bias due to binary decision spaces and static uncertainty rewards, inducing either excessive conservatism or overconfidence. To tackle this challenge, this paper unveils the root causes of reward hacking and overconfidence in current RL paradigms incorporating uncertainty-based rewards, based on which we propose the UnCertainty-Aware Policy Optimization (UCPO) framework. UCPO employs Ternary Advantage Decoupling to separate and independently normalize deterministic and uncertain rollouts, thereby eliminating advantage bias. Furthermore, a Dynamic Uncertainty Reward Adjustment mechanism adapts uncertainty weights in real-time according to model evolution and instance difficulty. Experimental results in mathematical reasoning and general tasks demonstrate that UCPO effectively resolves the reward imbalance, significantly improving the reliability of the model beyond their knowledge boundaries. ;Accepted by ICML 2026

CodeContests-O: Powering LLMs via Feedback-Driven Iterative Test Case Generation

The rise of reasoning models necessitates large-scale verifiable data, for which programming tasks serve as an ideal source. However, while competitive programming platforms provide abundant problems and solutions, high-quality test cases for verification remain scarce. Existing approaches attempt to synthesize test cases using Large Language Models (LLMs), but rely solely on the model's intrinsic generation capabilities without external feedback, frequently resulting in insufficiently diverse cases. To address this limitation, we propose a $\textbf{Feedback-Driven Iterative Framework}$ for comprehensive test case construction. Specifically, our method leverages the LLM to generate initial test cases, executes them against known correct and incorrect solutions, and utilizes the failed results as feedback to guide the LLM in refining the test cases toward high fidelity and discriminability. We then apply this method to the CodeContests dataset to construct an optimized high-quality derivative, $\textbf{CodeContests-O}$. Evaluating against the entire pool of solutions ($1.1 \times 10^7$ in total), our dataset achieves an average True Positive Rate (TPR) of $89.37\%$ and True Negative Rate (TNR) of $90.89\%$, significantly outperforming the CodeContests and CodeContests+ by margins of $4.32\%$ and $9.37\%$, respectively. Furthermore, fine-tuning the Qwen2.5-7B model on CodeContests-O results in a $9.52\%$ improvement on LiveCodeBench (Pass@1). Experiments demonstrate the effectiveness of our framework and the quality of CodeContests-O. To support reproducibility and facilitate future research, we release the $\href{https://github.com/cai-jianfeng/CodeContests-O}{code}$ and $\href{https://huggingface.co/datasets/caijanfeng/CodeContests-O}{dataset}$.

360Anything: Geometry-Free Lifting of Images and Videos to 360°

Lifting perspective images and videos to 360° panoramas enables immersive 3D world generation. Existing approaches often rely on explicit geometric alignment between the perspective and the equirectangular projection (ERP) space. Yet, this requires known camera metadata, obscuring the application to in-the-wild data where such calibration is typically absent or noisy. We propose 360Anything, a geometry-free framework built upon pre-trained diffusion transformers. By treating the perspective input and the panorama target simply as token sequences, 360Anything learns the perspective-to-equirectangular mapping in a purely data-driven way, eliminating the need for camera information. Our approach achieves state-of-the-art performance on both image and video perspective-to-360° generation, outperforming prior works that use ground-truth camera information. We also trace the root cause of the seam artifacts at ERP boundaries to zero-padding in the VAE encoder, and introduce Circular Latent Encoding to facilitate seamless generation. Finally, we show competitive results in zero-shot camera FoV and orientation estimation benchmarks, demonstrating 360Anything's deep geometric understanding and broader utility in computer vision tasks. Additional results are available at https://360anything.github.io/. ;ECCV 2026. Project page: https://360anything.github.io/

Q-Hawkeye: Reliable Visual Policy Optimization for Image Quality Assessment

Image Quality Assessment (IQA) predicts perceptual quality scores consistent with human judgments. Recent RL-based IQA methods built on MLLMs focus on generating visual quality descriptions and scores, ignoring two key reliability limitations: (i) although the model's prediction stability varies significantly across training samples, existing GRPO-based methods apply uniform advantage weighting, thereby amplifying noisy signals from unstable samples in gradient updates; (ii) most works emphasize text-grounded reasoning over images while overlooking the model's visual perception ability of image content. In this paper, we propose Q-Hawkeye, an RL-based reliable visual policy optimization framework that redesigns the learning signal through unified Uncertainty-Aware Dynamic Optimization and Perception-Aware Optimization. Q-Hawkeye estimates predictive uncertainty using the variance of predicted scores across multiple rollouts and leverages this uncertainty to reweight each sample's update strength, stabilizing policy optimization. To strengthen perceptual reliability, we construct paired inputs of degraded images and their original images and introduce an Implicit Perception Loss that constrains the model to ground its quality judgments in genuine visual evidence. Extensive experiments demonstrate that Q-Hawkeye outperforms state-of-the-art methods and generalizes better across multiple datasets. Our dataset and code are available at https://github.com/AMAP-ML/Q-Hawkeye.

A Mechanistic View on Video Generation as World Models: State and Dynamics

Large-scale video generation models have demonstrated emergent physical coherence, positioning them as potential world models. However, a gap remains between contemporary "stateless" video architectures and classic state-centric world model theories. This work bridges this gap by proposing a novel taxonomy centered on two pillars: State Construction and Dynamics Modeling. We categorize state construction into implicit paradigms (context management) and explicit paradigms (latent compression), while dynamics modeling is analyzed through knowledge integration and architectural reformulation. Furthermore, we advocate for a transition in evaluation from visual fidelity to functional benchmarks, testing physical persistence and causal reasoning. We conclude by identifying two critical frontiers: enhancing persistence via data-driven memory and compressed fidelity, and advancing causality through latent factor decoupling and reasoning-prior integration. By addressing these challenges, the field can evolve from generating visually plausible videos to building robust, general-purpose world simulators.

Learning to Ideate for Machine Learning Engineering Agents

Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dual-agent framework that separates ideation from implementation. In our system, an implementation agent can request strategic help from a dedicated Ideator. We show this approach is effective in two ways. First, in a training-free setup, our framework significantly outperforms implementation-only agent baselines on MLE-Bench. Second, we demonstrate that the Ideator can be trained with reinforcement learning (RL) to generate more effective ideas. With only 1K training samples from 10 MLE tasks, our RL-trained Qwen3-8B Ideator achieves an 11.5% relative improvement compared to its untrained counterpart and surpasses Claude Sonnet 3.5. These results highlights a promising path toward training strategic AI systems for scientific discovery. ;EACL 2026 main conference

FIT to Forget: Robust Continual Unlearning for Large Language Models

While large language models (LLMs) exhibit remarkable capabilities, they increasingly face demands to unlearn memorized privacy-sensitive, copyrighted, or harmful content. Existing unlearning methods primarily focus on \emph{single-shot} scenarios, whereas real-world deletion requests arrive \emph{continually}. Naïvely applying these methods to sequential requests leads to severe utility degradation and catastrophic forgetting. To address this, we propose \fit, a robust continual unlearning framework to process high-volume sequential deletion streams while resisting both catastrophic forgetting and post-unlearning recovery. \fit stabilizes sequential updates through three synergistic mechanisms: redundancy \underline{F}iltering, \underline{I}mportance-aware adaptive algorithm selection, and \underline{T}argeted layer attribution. Furthermore, to facilitate rigorous evaluation, we introduce \textbf{PCH}, a unified benchmark encompassing \textbf{P}ersonal, \textbf{C}opyrighted, and \textbf{H}armful content, alongside two symmetric metrics, Forget Degree (F.D.) and Retain Utility (R.U.), to systematically quantify forgetting-utility trade-offs. Extensive experiments across five LLMs (up to 14B parameters) demonstrate that \fit consistently achieves state-of-the-art unlearning efficacy and utility preservation. Notably, even after hundreds of sequential requests, \fit preserves strong downstream (\eg, GSM8K, MMLU) performance and exhibits superior resilience against relearning and quantization recovery attacks. ;26 Pages

Enhancing Control Policy Smoothness by Aligning Actions with Predictions from Preceding States

Deep reinforcement learning has proven to be a powerful approach to solving control tasks, but its characteristic high-frequency oscillations make it difficult to apply in real-world environments. While prior methods have addressed action oscillations via architectural or loss-based methods, the latter typically depend on heuristic or synthetic definitions of state similarity to promote action consistency, which often fail to accurately reflect the underlying system dynamics. In this paper, we propose a novel loss-based method by introducing a transition-induced similar state. The transition-induced similar state is defined as the distribution of next states transitioned from the previous state. Since it utilizes only environmental feedback and actually collected data, it better captures system dynamics. Building upon this foundation, we introduce Action Smoothing by Aligning Actions with Predictions from Preceding States (ASAP), an action smoothing method that effectively mitigates action oscillations. ASAP enforces action smoothness by aligning the actions with those taken in transition-induced similar states and by penalizing second-order differences to suppress high-frequency oscillations. Experiments in Gymnasium and Isaac-Lab environments demonstrate that ASAP yields smoother control and improved policy performance over existing methods. ;Accepted at AAAI-26. 7 pages (excluding references), 3 figures

A low-order hybrid method for the variable-density incompressible Navier-Stokes equations

In this work we introduce and analyse a new low-order method for the variable-density incompressible Navier-Stokes equations. The main novelty of the proposed method lies in the support of general meshes, possibly including polygonal or polyhedral elements as well as non-matching interfaces. We carry out a complete analysis, showing stability, existence and uniqueness of a discrete solution, and convergence of the latter to a suitably defined weak solution of the continuous problem. Numerical tests validate the theoretical results.

Reconstruction-Anchored Diffusion Model for Text-to-Motion Generation

Diffusion models have seen widespread adoption for text-driven human motion generation and related tasks due to their impressive generative capabilities and flexibility. However, current motion diffusion models face two major limitations: a representational gap caused by pre-trained text encoders that lack motion-specific information, and error propagation during the iterative denoising process. This paper introduces Reconstruction-Anchored Diffusion Model (RAM) to address these challenges. First, RAM leverages a motion latent space as intermediate supervision for text-to-motion generation. To this end, RAM co-trains a motion reconstruction branch with two key objective functions: self-regularization to enhance the discrimination of the motion space and motion-centric latent alignment to enable accurate mapping from text to the motion latent space. Second, we propose Reconstructive Error Guidance (REG), a testing-stage guidance mechanism that exploits the motion diffusion model's inherent self-correction ability to mitigate error propagation. At each denoising step, REG uses the motion reconstruction branch to reconstruct the previous estimate, reproducing the prior error patterns. By amplifying the residual between the current prediction and the reconstructed estimate, REG highlights the improvements in the current prediction. Extensive experiments demonstrate that RAM achieves significant improvements and state-of-the-art performance. Our code will be released. ;ECCV 2026

Probing the Future of Meta-Analysis: Eliciting Design Principles via an Agentic Research IDE

Meta-analyses and systematic reviews demand rigorous abductive reasoning to build, test, and refine hypotheses across vast, heterogeneous literature. While NLP advancements have automated parts of this pipeline, existing tools often detach researchers from the cognitive loop or function merely as retrieval engines, leading to loss of intellectual ownership and frequent context switching. We present Research IDE, a prototype reimagining authoring environments through the "Research as Code" metaphor. Research IDE embeds a multi-agent backend into the writing flow, enabling in-situ verification via "hypothesis breakpoints." A one-week field deployment with 8 domain experts, followed by a reflective workshop, as a Research through Design (RtD) probe, reveals that users strongly preferred this verification workflow, actively leveraged prior knowledge for confirmation, and reported that breakpoints sparked insights. Drawing from participant feedback and suggestions, we derive design implications for future AI-assisted research tools that fully preserve researcher autonomy and intellectual ownership while harnessing computational scale.

Time-to-Injury Forecasting in Elite Female Football: A DeepHit Survival Approach

Injury occurrence in football poses significant challenges for athletes and teams, carrying personal, competitive, and financial consequences. While machine learning has been applied to injury prediction before, existing approaches often rely on static pre-season data and binary outcomes, limiting their real-world utility. This study investigates the feasibility of using a DeepHit neural network to forecast time-to-injury from longitudinal athlete monitoring data, while providing interpretable predictions. The analysis utilised the publicly available SoccerMon dataset, containing two seasons of training, match, and wellness records from elite female footballers. Data was pre-processed through cleaning, feature engineering, and the application of three imputation strategies. Baseline models (Random Forest, XGBoost, Logistic Regression) were optimised via grid search for benchmarking, while the DeepHit model, implemented with a multilayer perceptron backbone, was evaluated using chronological and leave-one-player-out (LOPO) validation. DeepHit achieved a concordance index of 0.762, outperforming baseline models and delivering individualised, time-varying risk estimates. Shapley Additive Explanations (SHAP) identified clinically relevant predictors consistent with established risk factors, enhancing interpretability. Overall, this study provides a novel proof of concept: survival modelling with DeepHit shows strong potential to advance injury forecasting in football, offering accurate, explainable, and actionable insights for injury prevention across competitive levels.

Predicting Networks Before They Happen: Experimentation on a Real-Time V2X Digital Twin

Emerging safety-critical Vehicle-to-Everything (V2X) applications require networks to proactively adapt to rapid environmental changes rather than merely reacting to them. While Network Digital Twins (NDTs) offer a pathway to such predictive capabilities, existing solutions typically struggle to reconcile high-fidelity physical modeling with strict real-time constraints. This paper presents a novel, end-to-end real-time V2X Digital Twin framework that integrates live mobility tracking with deterministic channel simulation. By coupling the Tokyo Mobility Digital Twin-which provides live sensing and trajectory forecasting-with VaN3Twin-a full-stack simulator with ray tracing-we enable the prediction of network performance before physical events occur. We validate this approach through an experimental proof-of-concept deployed in Tokyo, Japan, featuring connected vehicles operating on 60 GHz links. Our results demonstrate the system's ability to predict Received Signal Strength (RSSI) with a maximum average error of 1.01 dB and reliably forecast Line-of-Sight (LoS) transitions within a maximum average end-to-end system latency of 250 ms, depending on the ray tracing level of detail. Furthermore, we quantify the fundamental trade-offs between digital model fidelity, computational latency, and trajectory prediction horizons, proving that high-fidelity and predictive digital twins are feasible in real-world urban environments. ;Accepted to 103rd Vehicular Technology Conference: VTC2026-Spring

Achievable Burning Densities of Growing Grids

Graph burning is a discrete-time process on graphs where vertices are sequentially activated and burning vertices cause their neighbours to burn over time. In this work, we focus on a dynamic setting in which the graph grows over time, and at each step we burn vertices in the growing grid $G_n = [-f(n),f(n)]^2$. We investigate the set of achievable burning densities for functions of the form $f(n)=\lceil cn^α\rceil$, where $α\ge 1$ and $c>0$. We show that for $α=1$, the set of achievable densities is $[1/(2c^2),1]$, for $1<α<3/2$, every density in $[0,1]$ is achievable, and for $α=3/2$, the set of achievable densities is $[0,(1+\sqrt{6}c)^{-2}]$.

EuleroDec: A Complex-Valued RVQ-VAE for Efficient and Robust Audio Coding

Audio codecs power discrete music generative modelling, music streaming and immersive media by shrinking PCM audio to bandwidth-friendly bit-rates. Recent works have gravitated towards processing in the spectral domain; however, spectrogram-domains typically struggle with phase modeling which is naturally complex-valued. Most frequency-domain neural codecs either disregard phase information or encode it as two separate real-valued channels, limiting spatial fidelity. This entails the need to introduce adversarial discriminators at the expense of convergence speed and training stability to compensate for the inadequate representation power of the audio signal. In this work we introduce an end-to-end complex-valued RVQ-VAE audio codec that preserves magnitude-phase coupling across the entire analysis-quantization-synthesis pipeline and removes adversarial discriminators and diffusion post-filters. Without GANs or diffusion we match or surpass much longer-trained baselines in-domain and reach SOTA out-of-domain performance. Compared to standard baselines that train for hundreds of thousands of steps, our model reducing training budget by an order of magnitude is markedly more compute-efficient while preserving high perceptual quality. ;Accepted at ICASSP 2026

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.

An Efficient Batch Solver for the Singular Value Decomposition on GPUs

The singular value decomposition (SVD) is a powerful tool in modern numerical linear algebra, which underpins computational methods such as principal component analysis (PCA), low-rank approximations, and randomized algorithms. Many practical scenarios require solving numerous small SVD problems, a regime generally referred to as "batch SVD". Existing programming models can handle this efficiently on parallel CPU architectures, but high-performance solutions for GPUs remain immature. A GPU-oriented batch SVD solver is introduced. This solver exploits the one-sided Jacobi algorithm to exploit fine-grained parallelism, and a number of algorithmic and design optimizations achieve unmatched performance. Starting from a baseline solver, a sequence of optimizations is applied to obtain incremental performance gains. Numerical experiments show that the new solver is robust across problems with different numerical properties, matrix shapes, and arithmetic precisions. Performance benchmarks on both NVIDIA and AMD systems show significant performance speedups over vendor solutions as well as existing open-source solvers.

Beyond Prompting: Efficient and Robust Contextual Biasing for Speech LLMs via Logit-Space Integration (LOGIC)

The rapid emergence of new entities -- driven by cultural shifts, evolving trends, and personalized user data -- poses a significant challenge for existing Speech Large Language Models (Speech LLMs). While these models excel at general conversational tasks, their static training knowledge limits their ability to recognize domain-specific terms such as contact names, playlists, or technical jargon. Existing solutions primarily rely on prompting, which suffers from poor scalability: as the entity list grows, prompting encounters context window limitations, increased inference latency, and the "lost-in-the-middle" phenomenon. An alternative approach, Generative Error Correction (GEC), attempts to rewrite transcripts via post-processing but frequently suffers from "over-correction", introducing hallucinations of entities that were never spoken. In this work, we introduce LOGIC (Logit-Space Integration for Contextual Biasing), an efficient and robust framework that operates directly in the decoding layer. Unlike prompting, LOGIC decouples context injection from input processing, ensuring constant-time complexity relative to prompt length. Extensive experiments using the Phi-4-MM model across 11 multilingual locales demonstrate that LOGIC achieves an average 9% relative reduction in Entity WER with a negligible 0.30% increase in False Alarm Rate. ;This paper is withdrawn temporarily to ensure full compliance with internal institutional publication approval processes

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.

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

Post-acute COVID-19 outcomes including participant-reported long COVID: amubarvimab/romlusevimab versus placebo in the ACTIV-2 trial

BACKGROUND: It is unknown if early COVID-19 monoclonal antibody (mAb) therapy can reduce risk of Long COVID. The mAbs amubarvimab/romlusevimab were previously demonstrated to reduce risk of hospitalization/death by 79%. This study assessed the impact of amubarvimab/romlusevimab on late outcomes, including Long COVID. METHODS: Non-hospitalized high-risk adults within 10 days of COVID-19 symptom onset enrolled in a randomized, double-blind, placebo-controlled phase 2/3 trial of amubarvimab/romlusevimab for COVID-19 treatment. Late symptoms, assessed using a participant-completed symptom diary, were a pre-specified exploratory endpoint. The primary outcome for this analysis was the composite of Long COVID by participant self-report (presence of COVID-19 symptoms as recorded in the diary at week 36) or hospitalization or death by week 36. Inverse probability weighting (IPW) was used to address incomplete outcome ascertainment, giving weighted risk ratios (wRR) comparing amubarvimab/romlusevimab to placebo. FINDINGS: Participants received amubarvimab/romlusevimab (n = 390) or placebo (n = 390) between January and July 2021. Median age was 49 years, 52% were female, 18% Black/African American, 49% Hispanic/Latino, and 9% COVID-19-vaccinated at entry. At week 36, 103 (13%) had incomplete outcome ascertainment, and 66 (17%) on amubarvimab/romlusevimab and 92 (24%) on placebo met the primary outcome (wRR = 0.70, 95% confidence interval (CI) 0.53–0.93). The difference was driven by fewer hospitalizations/deaths with amubarvimab/romlusevimab (4%) than placebo (13%). Among 652 participants with available diary responses, 53 (16%) on amubarvimab/romlusevimab and 44 (14%) on placebo reported presence of Long COVID. INTERPRETATION: Amubarvimab/romlusevimab treatment, while highly effective in preventing hospitalizations/deaths, did not reduce risk of Long COVID. Additional interventions are needed to prevent Long COVID. FUNDING: 10.13039/100000060National Institute of Allergy and Infectious Diseases of the 10.13039/100000002National Institutes of Health. Amubarvimab and romlusevimab supplied by Brii Biosciences.

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)

Pediatric Long COVID Subphenotypes: An EHR-based study from the RECOVER program

Pediatric Long COVID has been associated with a wide variety of symptoms, conditions, and organ systems, but distinct clinical presentations, or subphenotypes, are still being elucidated. In this exploratory analysis, we identified a cohort of pediatric (age <21) patients with evidence of Long COVID and no pre-existing complex chronic conditions using electronic health record data from 38 institutions and used an unsupervised machine learning-based approach to identify subphenotypes. Our method, an extension of the Phe2Vec algorithm, uses tens of thousands of clinical concepts from multiple domains to represent patients’ clinical histories to then identify groups of patients with similar presentations. The results indicate that cardiorespiratory presentations are most common (present in 54% of patients) followed by subphenotypes marked (in decreasing order of frequency) by musculoskeletal pain, neuropsychiatric conditions, gastrointestinal symptoms, headache, and fatigue.

The heightened impact of COVID-19 on emotional wellbeing and delays in care in patients with cutaneous T-cell lymphoma: a cross-sectional study

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.

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.

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.

"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.

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.

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.

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).

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.

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.

Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework

Long COVID continues to challenge public health by affecting a considerable number 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 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 achieved an average confidence of 0.7788, with the minimum and maximum confidence being 0.1566 and 0.9928, respectively. This model showcases the ability of advanced language models to categorize research papers without any training data or predefined classification labels, thus enabling a more rapid and scalable assessment of existing literature. This paper also highlights the multifaceted nature of Long COVID research by demonstrating how advanced computational techniques applied to social media conversations can reveal deeper insights into the experiences, symptoms, and narratives of individuals affected by Long COVID.

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

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.

Evaluating Amazon Effects and the Limited Impact of COVID-19 With Purchases Crowdsourced from US Consumers

We leverage a recently published dataset of Amazon purchase histories, crowdsourced from thousands of US consumers, to study how online purchasing behaviors have changed over time, how changes vary across demographic groups, the impact of the COVID-19 pandemic, and relationships between online and offline retail. This work provides a case study in how consumer-level purchases data can reveal purchasing behaviors and trends beyond those available from aggregate metrics. For example, in addition to analyzing spending behavior, we develop new metrics to quantify changes in consumers' online purchase frequency and the diversity of products purchased, to better reflect the growing ubiquity and dominance of online retail. Between 2018 and 2022 these consumer-level metrics grew on average by more than 85%, peaking in 2021. We find a steady upward trend in individuals' online purchasing prior to COVID-19, with a significant increase in the first year of COVID, but without a lasting effect. Purchasing behaviors in 2022 were no greater than the result of the pre-pandemic trend. We also find changes in purchasing significantly differ by demographics, with different responses to the pandemic. We further use the consumer-level data to show substitution effects between online and offline retail in sectors where Amazon heavily invested: books, shoes, and grocery. Prior to COVID we find year-to-year changes in the number of consumers making online purchases for books and shoes negatively correlated with changes in employment at local bookstores and shoe stores. During COVID we find online grocery purchasing negatively correlated with in-store grocery visits. This work demonstrates how crowdsourced, open purchases data can enable economic insights that may otherwise only be available to private firms.

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.

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.

An Environmental Health Early Warning System: When Resilient Climates and Windblown Dusts Risk Public Health

Arid regions, the source of most airborne mineral dusts, comprise a third of the Earth’s land surface, where around two billion people are exposed daily to the fine particles raised by wind. Crossing political borders and traveling on air currents around the world, these particles affect the health of their local communities as well as distant populations—putting all at risk for cardiovascular and respiratory illnesses that are overlain with emerging health issues, including potential clinical misdiagnoses of tuberculosis, influenza, and SARS COVID that present similar initial symptoms. Risks of exposure are affected by local and regional weather characteristics and climatic conditions that are in a state of flux. Advances in science and technology promise to reduce the health problems, but windblown dusts and what travels with them can change the fundamental problem and appropriate response. This chapter uses examples of meningitis, asthma, and Valley fever to illustrate how risks may be lowered through an (environmental) dust-health early warning system. A half century of dedicated measurements of particulate air quality and of environmental science enhanced by Earth-orbiting satellites reveal the truth of airborne dust extent, and much of its variability in time and space. These truths have been essential in advancing numerical, dynamic models of the atmosphere which mimic and predict weather systems that loft and transport the airborne dusts that medical sciences and epidemiology prove harmful. The union of many scientific disciplines and services makes possible improved public health around the world.

“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.

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.

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.

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.

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.

Human Immunodeficiency Virus I: History, Epidemiology, Transmission, and Pathogenesis

While research has shown that human immunodeficiency virus (HIV) may have been present in humans as early as the start of the twentieth century in the Democratic Republic of the Congo (formerly Zaire), the diagnosis remained out of the public eye for over half a century. In the 1980s, all that changed. When acquired immunodeficiency syndrome (AIDS) was first described in 1981, acquisition of the disease was considered a death sentence. HIV had not yet been discovered and antiretroviral (ARV) medications were not available. Initial treatments were limited to treating concomitant opportunistic infections and their complications. Moreover, the medical and public hysteria over how the disease could be spread leads to stigma among those affected. More than 39 million people worldwide are infected, with the largest number of infected individuals living in resource-poor areas of sub-Saharan Africa and South and Southeast Asia. Additionally, it is estimated that approximately 75% of people living with HIV currently have access to treatment. In the United States, an estimated 1,200,000 people were living with HIV in 2022, which includes an estimate of the number of people with HIV who do not yet know of their diagnosis. In 2022, the last full year for which statistics are currently available, 37,981 new cases of HIV were diagnosed in the United States. HIV is a retrovirus that is transmitted generally through direct blood exposure or through contact with contaminated body fluids, such as with sexual exposure. Infection with HIV leads to significant immunosuppression as the virus infects CD4+ T cells and causes their progressive depletion.

Community variability in TB-related stigma in South Africa: an ecologic analysis from the MISSED TB Outcomes Study

INTRODUCTION: Tuberculosis (TB) stigma is a critical barrier to timely diagnosis and treatment. Although stigma originates within communities, few studies have quantified community-level TB stigma or its variability across geographic contexts. This study describes methods for capturing community-level TB stigma and examines stigma variability across 93 urban, peri-urban, and rural communities in Buffalo City Metropolitan Health District, South Africa. METHODS: As part of the MISSED TB Outcomes Study, heads of household (HoHs) were surveyed in a geographically clustered random sample of households across demarcated study communities. Validated scales were used to measure perceived community-level TB stigma, HIV stigma, and TB/HIV knowledge. Demographic data, including self-reported household TB and HIV history, were also captured. Community-level data, including TB and HIV stigma, were generated by aggregating individual responses within each study community. Associations between TB stigma and other community-level variables were analyzed using robust linear regression. RESULTS: Surveys were completed by 3,869 households across 93 communities. Median community TB stigma scores varied significantly by community location, with rural communities reporting the lowest stigma and peri-urban communities the highest. TB stigma was positively associated with HIV stigma across all community types, with the strongest associations in urban and rural communities. No associations were observed between TB stigma and TB prevalence, TB knowledge, or household demographics after adjusting for community location. CONCLUSIONS: TB stigma varied meaningfully across communities and was influenced by urbanicity and HIV stigma. These findings suggest that stigma-reduction interventions must be tailored to local contexts and consider community-level determinants beyond individual knowledge or TB burden. The identified variability in TB stigma will inform future multilevel analyses of the TB care cascade in South Africa.

Prevalence of and risk factors for suicidal ideation among perinatal women with HIV infection: a systematic review, meta-analysis, and meta-regression

Background The prevalence of suicidal ideation (SI) among HIV-positive pregnant women is a complex issue influenced by multiple risk factors. By addressing these risk factors and focusing on vulnerable regions, healthcare providers and policymakers can strive to alleviate the burden of SI in this population. The objective of this systematic review, meta-analysis, and meta-regression was to estimate the prevalence and identify risk factors for SI among HIV-positive pregnant women. Methods The review systematically searched PubMed, Scopus and Web of Science to identify relevant studies published until December 2024. A meta-analysis was conducted to summarize the prevalence and risk factors for SI among pregnant women with HIV infection. Sensitivity and meta-regression analysis were performed to explore the potential sources of heterogeneity in the distribution and determinants of suicidal behaviors within this at-risk group. Results The review identified 18 studies involving 5,242 participants. The overall prevalence of SI was 20.5%; 95% CI: 14.6 – 28.0% in pregnant women living with HIV, 19.8%; 95% CI: 12.6 – 29.6% in perinatal, and 14.9%; 95% CI: 7.8 – 26.5% in prenatal. The prevalence of SI was 17.1%, 20.3%, and 34.5% for the periods 2020–2024, 2015–2019, and 2000–2014, respectively. By gross domestic product (GDP), the prevalence was 16.9% in low, 23.0% in moderate, and 24.1% in high GDP countries, with the highest prevalence in the USA (24.2%). Among various risk factors, partner violence (OR = 1.44; 95% CI: 1.05–1.98), and higher education (OR = 0.80; 95% CI: 0.64–0.99) were identified as significant risk factors and protective factors, respectively. Meta-regression analysis indicated that GDP, partner violence, year, age, and depression were potential sources of heterogeneity, respectively. Conclusion The results indicated a high prevalence of SI among perinatal women living with HIV. The educational level, GDP, partner violence, and year were significant risk factors and potential sources of heterogeneity. It is crucial to incorporate specific questions about suicidal ideation into routine prenatal care for this population, even when depressive symptoms are not apparent. These findings underscore the need for a multifaceted approach to addressing suicidal ideation in HIV-positive prenatal women.

Efficacy and acceptability of liraglutide for obesity in people with HIV: results of an open-label clinical trial in South Africa

BACKGROUND: In the Liraglutide for Obesity in HIV (LIROH) trial, we evaluated the efficacy and acceptability of liraglutide plus lifestyle counselling on weight and cardiometabolic health markers, along with the effect of discontinuation of therapy, among people with HIV (PWH) and a body mass index (BMI) ≥ 30 kg/m(2) in rural South Africa. METHODS: From May – September 2024, we enrolled 40 PWH in KwaZulu-Natal in a Phase IV, open-label clinical trial of liraglutide 3.0 mg daily for 12 weeks. Key cardiometabolic health outcomes included changes in weight, waist circumference, and hemoglobin A1c (HbA1c). We also assessed depression, quality of life (QoL) and sleep quality. We also assessed acceptability of the intervention based on enrollment fraction and retention at 12 weeks and 24 weeks. RESULTS: We screened 52 participants, of whom 44 (85%) were eligible and 40 (91%) consented to enroll. Thirty-eight (95%) completed the study, with 2 (5%) discontinuing treatment due to relocation. Participants experienced substantial reductions in weight and waist circumference, and mean HbA1c (Weight: −2.9% [(−1.0 – 4.5%]; waist: −5.6 cm [−8.2 to −2.9]; HbA1c: −0.3% [−0.4 to −0.2]). Moreover, depressive symptoms, sleep quality, and psychological QoL all improved significantly with treatment. Weight and waist circumference rebounded significantly off treatment. The changes in depression, sleep quality and QoL were negligible post-treatment. CONCLUSIONS: This trial is among the first studies of GLP-1 RAs in Southern Africa and demonstrates that this class for obesity treatment among PWH in this region has efficacy to improve metabolic health and that the study intervention was acceptable in this population.

Including the Household: Individual, Community and Household Factors Affecting Antiretroviral Therapy Adherence After ART Initiation in Cape Town, South Africa

Antiretroviral therapy (ART) adherence is crucial for health outcomes of people living with HIV (PLHIV), influenced by a complex interplay of individual, community, and household factors. This article focuses on the influence of household factors, as well as individual and community factors, on ART adherence among PLHIV in Cape Town who have recently initiated ART. Baseline data for a cluster-randomized controlled trial were collected from 316 PLHIV in 12 districts in Cape Town between 6th May 2021 and 22nd May 2022. Zero-inflated Poisson models, with cluster-adjusted standard errors, were used to analyse the association between individual, household, and community factors and ART adherence measures. At the household-level, household support was associated with both better self-rated adherence (exp(β) = 0.81, z =  − 4.68, p < 0.001) and fewer days when pills were missed (exp(β) = 0.65, z =  − 2.92, p = 0.003). Psychological violence (exp(β) = 1.37, z = 1.97, p = 0.05) and higher household asset scores (exp(β) = 1.29, z =  − 2.83, p = 0.05) were weakly associated with poorer ART adherence. At the individual-level, male gender (exp(β) = 1.37, z = 3.95, p < 0.001) and reinitiating ART (exp(β) = 1.35, z = 3.64, p < 0.001) were associated with worse self-rated ART adherence; higher education levels (exp(β) = 0.30 times, z =  − 3.75, p < 0.001) and better HIV knowledge (exp(β) = 0.28, z =  − 2.83, p = 0.005) were associated with fewer days where pills were missed. At the community-level, community stigma was associated with worse self-rated ART adherence (exp(β) = 1.24, z = 3.01, p = 0.003). When designing interventions to improve ART adherence, household, individual and community factors should all be considered, particularly in addressing gender-based disparities, reducing stigma, tackling violence, and enhancing household support. Clinical Trial Number: Pan African Clinical Trial Registry, PACTR201906476052236. Registered on 24 June 2019. La adherencia a la terapia antirretroviral (TAR) es crucial para los resultados de salud de las personas que viven con el VIH (PLHIV), influenciada por una compleja interacción de factores individuales, comunitarios y del hogar. Este artículo se centra en la influencia de los factores del hogar, individuales y comunitarios en la adherencia al TAR entre personas que iniciaron recientemente el TAR en Ciudad del Cabo. Se recopilaron datos de referencia para un ensayo de control aleatorio por grupos de 316 PLHIV en 12 distritos de Ciudad del Cabo entre el 6 de mayo de 2021 y el 22 de mayo de 2022. Se utilizaron modelos de Poisson inflados a cero, con errores estándar ajustados por conglomerado para estudiar la asociación entre factores individuales, del hogar o comunitarios con dos medidas de adhesión al TAR: por un lado la auto declaración de adhesión, y por otro la cantidad de días en que se olvidó de tomar la medicina en los últimos 4 días. A nivel del hogar, el apoyo del hogar se asoció con una mejor adherencia auto declarada (exp(β) = 0.81, z =  − 4.68, p < 0.001) y menos días en los que se omitió la medicina (exp(β) = 0.65, z =  − 2.92, p = 0.003). La violencia psicológica (exp(β) = 1.37, z = 1.97, p = 0.05) y las puntuaciones más altas de activos del hogar (exp(β) = 1.29, z =  − 2.83, p = 0.05) se asociaron con una peor adherencia al TAR. A nivel individual, el sexo masculino (exp(β) = 1.37, z = 3.95, p < 0.001) y el reinicio del TAR (exp(β) = 1.35, z = 3.64, p < 0.001) se asociaron con una peor adherencia al TAR autodeclarada; niveles de educación más altos (exp(β) = 0.30 times, z =  − 3.75, p < 0.001) y un mejor conocimiento sobre el VIH (exp(β) = 0.28, z =  − 2.83, p = 0.005) se asociaron con menos días en los que se omitió la medicina. A nivel comunitario, el estigma comunitario se asoció con una peor autodelaración de adhesión del TAR (exp(β) = 1.24, z = 3.01, p = 0.003). Para mejorar la adherencia al TAR, se deben tener en cuenta los factores del hogar, así como los individuales y comunitarios, particularmente al abordar las disparidades de género, reducir el estigma, abordar la violencia y mejorar el apoyo del hogar.

Analysis of risky sexual behaviors among male college students who were sexually active in Sichuan, China: a cross-sectional survey

Background Males have accounted for a significant share of new HIV infections among young people in the recent years. This study aimed to identify the factors associated with risky sexual behaviors, including early sexual debut, multiple sexual partnership and condomless sex, among sexually active male college students and provide implications for tailored health interventions. Methods The cross-sectional study was conducted from December 2020 to December 2021 in 16 colleges that were located in Sichuan Province, one of the high-risk areas in China. Overall 1640 male college students who reported sexually experienced were analyzed in this study. Multivariable logistic regression analysis was applied to determine factors associated with early sexual debut, multiple sexual partnership and condomless sex. Results The average age of included male students was 19.95 ± 1.56. Of them, 27.74% initiated sexual behavior early, 48.60% reported multiple sexual partnership, and 16.52% did not use condoms at the latest sexual intercourse. Students who were younger (age ≤ 19, AOR = 7.60, 95%CI: 4.84–11.93; age20-21, AOR = 3.26, 95%CI: 2.04–5.21) and self-identified as sexual minorities (AOR = 2.38, 95%CI: 1.69–3.36) were more likely to have early sexual debut. The odds of having multiple sexual partners were higher among those who were ethnic minorities (AOR = 1.79, 95%CI: 1.33–2.41) and accepted extramarital sex (AOR = 1.33, 95%CI: 1.03–1.71). The likelihood of engaging in condomless sex at the latest sexual intercourse was lower among those who had sufficient knowledgeable about HIV (AOR = 0.63, 95%CI: 0.44–0.89), were very confident in condom use efficacy (AOR = 0.26, 95%CI: 0.16–0.43) and confident (AOR = 0.48, 95%CI: 0.34–0.69). Early sexual debut was positively associated with multiple sexual partnership (AOR = 3.64, 95%CI: 2.82–4.71) and condomless sex at the latest intercourse (AOR = 1.53, 95%CI: 1.07–2.20), respectively. Conclusion Early sexual debut, multiple sexual partnership and condomless sex were of considerable concern among male college students. Comprehensive sex education curricula were advised by developing customized information on HIV prevention, sexuality and empowering students with assertiveness and negotiation skills with regard to condom use during and before college.

Characteristics of molecular epidemiology and transmitted drug resistance among newly diagnosed HIV-1 infections in Lishui, China from 2020 to 2023

Background Transmitted drug resistance (TDR) is becoming an obstacle to the success of antiretroviral therapy (ART) as the HIV epidemic continues to spread. This study aimed to investigate the characteristics of TDR and the molecular epidemiology of ART-naive HIV-1 infections in Lishui. Methods A total of 481 plasma samples were collected from ART-naive HIV-1 infections in Lishui between 2020 and 2023. The sequences acquired from infections were used to analyze the characteristics of genotype, TDR, and molecular transmission network. Results This study discovered that the three most prevalent subtypes among the 455 sequences successfully obtained from infections in Lishui were CRF08_BC (35.8%), CRF07_BC (26.4%), and CRF01_AE (25.9%). The overall prevalence of TDR was 12.1%, and the K103N (2.4%) was the most frequent mutation. Multivariate analysis showed that CRF08_BC (OR = 5.401, P  < 0.001) and CD4^+ cell concentration of 200–499 cells/µL (OR = 1.684, P  = 0.030) were associated with a higher risk of entering the molecular transmission network and clustering, whereas the current address in other cities (OR = 0.328, P  = 0.004), junior middle school (OR = 0.472, P  = 0.006), and junior college or above (OR = 0.387, P  = 0.045) were associated with a lower risk of clustering. Conclusions This study revealed that the prevalence of TDR was at an intermediate level of drug resistance, and high levels of resistance were predominantly concentrated in efavirenz (EFV) and nevirapine (NVP) among the NNRTIs. Middle-aged and older infections represented a significant proportion of the molecular transmission network. This suggests that HIV surveillance and targeted prevention and treatment interventions are essential to reduce the risk of HIV transmission.

Clinical manifestations and immune markers of non-HIV-related CMV retinitis

Background Since the HIV epidemic in the 1980s, CMV retinitis has been mainly reported in this context. CMV retinitis in persons living with HIV is usually observed when CD4 + cells are below 50 cells/mm3. This study aims to describe the immune markers of non-HIV-related CMV retinitis as well as to describe its clinical manifestations and outcomes. Methods Retrospective chart review of consecutive patients with CMV retinitis not related to HIV seen at the uveitis clinic of Jules Gonin Eye Hospital between 2000 and 2023. We reported the clinical manifestations and outcomes of the patients. We additionally assessed immune markers during CMV retinitis (leukocyte, lymphocyte, CD4 + cell and CD8 + cell counts as well as immunoglobulin levels). Results Fifteen patients (22 eyes) were included. Underlying disease was hematologic malignancy in 9 patients, solid organ transplant in 3 patients, rheumatic disease in 2 patients and thymoma in one patient. The median time between the onset of underlying disease and the diagnosis of retinitis was 4.8 years. Lymphopenia was observed in 8/15 patients (mild = 3, moderate = 4, severe = 1), and low CD4 counts were observed in 9/12 patients, with less than 100 cells/mm3 in 4 patients. Hypogammaglobulinemia was detected in 7/11 patients. Retinitis was bilateral in 7/15 patients, and severe visual loss was frequent (5/19 eyes). Disease recurrence was seen in 7/13 patients at a median time of 6 months after initial diagnosis. No differences in immune markers were observed in patients with vs. without recurrence. Conclusion CMV retinitis is a rare disorder that can affect patients suffering any kind of immunodeficiency. It is associated with a high visual morbidity despite adequate treatment. CD4 + cell counts are usually higher than those in HIV patients, but B-cell dysfunction is common.

Sex hormone-binding globulin, testosterone and type 2 diabetes risk in middle-aged African women: exploring the impact of HIV and menopause

OBJECTIVES: Sex hormone-binding globulin (SHBG) and testosterone are differentially associated with type 2 diabetes (T2D) risk. We investigated whether these associations differ by HIV and menopausal status in Black South African women living with (WLWH) and without HIV (WLWOH). DESIGN: Cross-sectional observational. METHODS: Eighty one premenopausal (57 WLWOH, 24 WLWH) and 280 postmenopausal (236 WLWOH, 44 WLWH) women from the Middle-Aged Soweto Cohort (MASC) completed the following measures: circulating SHBG and sex hormones, body composition (dual energy x-ray absorptiometry), oral glucose tolerance test to estimate insulin sensitivity (Matsuda index), secretion (insulinogenic index, IGI) and clearance, and beta-cell function (disposition index, DI). Dysglycaemia was defined as either impaired fasting or postprandial glucose or T2D. RESULTS: SHBG was higher and total and free testosterone were lower in postmenopausal WLWH than WLWOH (all p < 0.023). Irrespective of HIV serostatus, SHBG was positively associated with Matsuda index, insulin clearance and DI and inversely with HOMA-IR (all p<0.011). The association between SHBG and Matsuda index was stronger in premenopausal than postmenopausal women (p=0.043 for interaction). Free testosterone (and not total testosterone) was only negatively associated with basal insulin clearance (p=0.021), and positively associated with HOMA-IR in premenopausal and not post-menopausal women (p=0.015 for interaction). CONCLUSIONS: We show for the first time that midlife African WLWH have higher SHBG and lower total and free testosterone than WLWOH, which corresponded to their higher beta-cell function, suggesting a putative protective effect of SHBG on T2D risk in WLWH. SIGNIFICANCE STATEMENT: This study in midlife Black African women suggest that higher sex hormone binding protein (SHBG) and lower free testosterone in women living with HIV (WLWH) may be associated with reduced risk of type 2 diabetes (T2D) compared to women living without HIV. Further, this study provides a putative mechanism underlying the lower prevalence of T2D in WLWH and obesity compared to women living with obesity but without HIV. However, longitudinal studies are required to understand the clinical implications of these findings.

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.

In vitro selection of cyclized, glycosylated peptide antigens that tightly bind HIV high mannose patch antibodies

In vitro selection is typically limited to discovery of peptides, proteins and nucleic acids. Given the importance of carbohydrate-protein interactions in diverse areas of biology including cell adhesion/recognition, immunoregulation and host-pathogen interactions, directed-evolution-based methods for discovery of potent glycoligands are greatly needed. We have previously reported a method for in vitro selection of glycopeptides that combines mRNA display, alkynyl amino acid incorporation, and CuAAC “click” glycosylation. Herein, we describe extensions of this method that incorporate chemical cyclization, removal of N-terminal glycosylation sites and next-generation sequencing; as an approach to HIV immunogen design, we have then used this method to develop mimics of the High Mannose Patch (HMP), which is the region on HIV envelope protein gp120 most commonly targeted by HIV broadly neutralizing antibodies (bnAbs). We prepared libraries of 10 (12-14) glycopeptides about 50 amino acids in length, containing variable numbers of high mannose (Man (9) GlcNAc (2) ) glycans and cyclization at varied sites. We performed selections to obtain binders of HIV bnAbs PGT128, PGT122, and gl-PGT121, a germline precursor of PGT122, and prepared numerous glycopeptide hits by chemical synthesis. Selected glycopeptides in some cases bound very tightly to their target HIV bnAb, e.g., with a K (D) as low as 0.5 nM for PGT128. These glycopeptides are of interest as immunogens and tools for HIV vaccine design.

Isolating the effects of HIV infection and HIV exposure on epigenetic profiles in infants using historical data from the Mothers and Infants Cohort Study

BACKGROUND: Epigenetics offers insight into the mechanisms by which early life HIV infection and HIV exposure in utero affects offspring health. However, due to the widespread use of antiretroviral therapy (ART) during pregnancy/infancy, contemporary studies are unable to disentangle effects of HIV from ART exposure on epigenetic profiles. METHODS: Using historical specimens collected before widespread use of ART (1985–1991), we compared DNA methylation (DNAm) profiles among infants with perinatally-acquired HIV (PHIV), HIV-exposed but uninfected (HEU), and HIV-unexposed uninfected (HUU). DNAm in peripheral blood mononuclear cells collected at 3 and 12 months of age (36 PHIV, 33 HEU, and 33 HUU) was profiled using the Illumina Infinium MethylationEPIC BeadChip. We tested for differentially methylated (DM) CpG sites between groups at 3 and 12 months, adjusting for sex, race/ethnicity, and cell type proportions. Biological pathway enrichment analyses were conducted. FINDINGS: Comparing PHIV to HEU, there were 2 DM sites at 3 months and 11 at 12 months. Comparing PHIV to HUU, there was 1 DM CpG site at 3 months and 6 at 12 months. Immune-related pathways, including interferon-mediated signalling pathways were enriched. HIV exposure was not associated with any variation in DNA methylation, as no differences were detected between HEU vs. HUU at 3 or 12 months. INTERPRETATION: HIV infection (in the absence of ART during pregnancy/infancy) was associated with DNA methylation changes at 3 and 12 months of life in infants. Differential methylation in PHIV is related to immune processes and HIV exposure in the absence of infection does not contribute to differential methylation. FUNDING: This study was supported by funding from the 10.13039/100000002National Institutes of Health (R21HD104558 to SS, K01DA053157 to SS, P30ES019776 to CJM, and P30ES005022 to ESB.

Distinct immune responses to HIV and CMV in Hofbauer cells across gestation highlight evolving placental immune dynamics

Placental immune responses to Human Immunodeficiency Virus (HIV) and Human Cytomegalovirus (CMV) vary across gestational stages and may influence postnatal outcomes. This study investigates the innate immunity of Hofbauer cells from placentae obtained at early/mid-gestation (18–21.6 weeks) and term (>37 weeks). RNA sequencing and cytokine profiling reveal that early/mid-gestation HCs exhibit heightened differential gene expression responses compared to term HCs, indicating a distinct transcriptional activity in early pregnancy. Significant overlap in gene expression profiles of early/mid-gestation cells in response to CMV and HIV suggest similar innate immune responses, while term cells exhibit distinct patterns, reflecting the temporal evolution of placental immunity. Integration with Human Protein Atlas database reveals more placental-specific differentially expressed genes in early/mid-gestation HCs exposed to HIV and CMV compared to term cells. Functional analysis reveals downregulation of pathways related to oxygen stress, estrogen response, and KRAS signaling pathway in early/mid-gestation HCs, with HIV uniquely upregulating reactive oxygen species and CMV uniquely disrupting WNT β-Catenin signaling. In term HCs, CMV exposure upregulates antiviral interferon (IFN) signaling and inflammatory pathways. Co-expression analysis highlights distinct molecular pathway enrichments across gestation, particularly with upregulation of IFN signaling and disruption of lipid metabolism in term CMV-exposed HCs. Cytokine profiling shows enhanced expression of GM-CSF, IFN-γ, and Th2-associated cytokines in early/mid-gestation HCs, indicating heightened immune responsiveness. These findings reveal the dynamic nature of placental immunity and underscore the need for targeted interventions to address unique immune and metabolic disruptions caused by viral infections at distinct stages of pregnancy to improve fetal and infant health outcomes.

Patterns of HIV-1 Viral Load Suppression and Drug Resistance During the Dolutegravir Transition: A Population-based Longitudinal Study

BACKGROUND: Data on the population-scale impact of dolutegravir (DTG)-based HIV regimens in sub-Saharan Africa are extremely limited. We used data from a surveillance cohort in southern Uganda to assess viral suppression and antiretroviral (ART) resistance over 10-years alongside DTG scale-up. METHODS: Consenting participants in the population-based Rakai Community Cohort Study between August 2011 and March 2023 aged 15–49 completed questionnaires and provided samples for HIV testing, viral load quantification, and viral deep-sequencing. We collected data on DTG utilization at HIV care clinics. We estimated the prevalence of HIV suppression and ART resistance using robust Poisson regression. Bayesian logistic regression quantified associations between resistance and individual-level suppression across surveys. RESULTS: Among 8781 people with HIV (PWH), suppression increased from 57.1% (2014, 95% confidence interval [CI], 55.4%–58.8%) to 90.3% (2022, 95% CI, 89.2%–91.4%). By 2020 84.4% (95% CI, 83.7%–85.2%) and 64.6% (95% CI, 63.9%–65.3%) of men and women on ART were on DTG. Among treatment-experienced viremic PWH, any intermediate/high resistance decreased from 51.1% (95% CI, 40.7%–64.2%, 2014) to 27.9% (95% CI, 21.3%–36.5%, 2022). Two of 258 (0.8%) 2022 participants harbored intermediate/high-level DTG resistance (inQ148R, inE138K, and inG140A). inS153Y (2-fold INSTI resistance) was observed in 23/306 (7.5%) of viremic individuals, with evidence of transmission. By 2022, NNRTI/NRTI resistance was not associated with a reduction in individual-level suppression (risk ratios: 1.15, 95% HPD: 0.93–1.39; 1.14, 0.86–1.42). CONCLUSIONS: Viral suppression increased during the DTG transition with minimal emerging intermediate/high-level resistance. Falling resistance among treatment-experienced PWH underscores the role of ART adherence in reducing viremia. The emergence of inS153Y justifies continued surveillance.

Population analysis and immunologic landscape of melanoma in people living with HIV

People living with HIV (PLWH) diagnosed with melanoma have consistently exhibited worse clinical outcomes than HIV-negative individuals (PLw/oH) with the same cancer, even in the era of antiretroviral therapy (ART). To investigate the underlying factors contributing to these disparities, we analyzed electronic health records from 922 PLWH and 334,972 PLw/oH with melanoma. PLWH were diagnosed with melanoma at a younger age and had a higher representation of Hispanic and Black individuals. Notably, PLWH had a markedly increased risk of brain metastases. Additionally, despite similar treatment durations, PLWH experienced significant delays in initiating immune checkpoint therapy (ICI) and exhibited worse survival outcomes at both five- and ten-years post-treatment with ICI. To explore potential biological determinants of these disparities, we conducted spatial immune transcriptomics on melanoma tumors (n=11). This analysis revealed a more immunosuppressive tumor landscape in PLWH, characterized by upregulated immune checkpoints (e.g., PD1, LAG3, CTLA4) and diminished antigen presentation (e.g., HLA-DRB, B2M ), with distinct spatial distributions in the tumors versus the tumor microenvironments. Downstream validation via multiplex immunofluorescence (n=15 PLWH, n=14 PLw/oH) confirmed an exhausted CD8 (+) T cell compartment, marked by enrichment of PD1 (int) LAG3 (-) and PD1 (int) LAG3 (+) subpopulations, along with a significant accumulation of immunosuppressive myeloid-derived suppressor cells (CD11b (+) HLA-DR (-) CD33 (+) ) in PLWH. These distinct immune profiles suggest chronic HIV infection fosters a permissive tumor microenvironment that might undermine effective immune responses and contribute to poor clinical outcomes for PLWH with melanoma. Targeting the actionable immune pathways identified in this study could inform tailored therapeutic strategies to mitigate these disparities.

A genome-wide association study of methamphetamine use among people with HIV

Background Amphetamine-like stimulants are the most used psychostimulants in the world; methamphetamine use is the most prevalent in people with HIV. Prolonged methamphetamine use can cause lasting damage to the heart, gut, and brain, as well as auditory hallucinations and paranoid thinking. However, relatively little is known about methamphetamine use and its genetic contributors. Methods Using genetic information from the Centers for AIDS Research Network of Integrated Clinical Systems (CNICS) cohort, we conducted a multi-ancestry genome-wide association study (GWAS) of methamphetamine use among people with HIV ( n  = 1,196 reported ever use, n  = 4,750 reported never use). Results No single nucleotide polymorphism was statistically associated with methamphetamine use at the genome-wide level ( p  < 5 * 10^–8) in our study. Further, we did not replicate previously suggested genetic variants from other studies (all p  > 0.05 in our analysis). Discussion Our study suggests that there is no single strong genetic contributor to lifetime use of methamphetamine in people with HIV enrolled in CNICS. Larger studies with more refined outcome assessment are warranted to further understand the contribution of genetics to methamphetamine use and use disorder. Investigation into social and environmental contributors to methamphetamine use are similarly necessary.

Perspectives of People With HIV on Implementing Long-acting Cabotegravir Plus Rilpivirine in Clinics and Community Settings in the United Kingdom: Results From the Antisexist, Antiracist, Antiageist Implementing Long-acting Novel Antiretrovirals Study

INTRODUCTION: The equity-focused Implementing Long-Acting Novel Antiretrovirals study evaluated feasibility, acceptability, appropriateness of delivering on-label 2-monthly cabotegravir and rilpivirine (CAB + RPV) injections for human immunodeficiency virus (HIV)-1 therapy in clinics and community settings. METHODS: The study, which mandated inclusive recruitment, was conducted May–December 2022 at 6 UK sites. Injections were delivered in clinic (month [M] 1–6) and in clinic or community setting according to patient choice (M6–12). Surveys were completed at baseline, M4, and M12 using validated measures for feasibility (FIM), acceptability (AIM), and appropriateness (IAM). Primary endpoint: proportion of participants agreeing that the injection and community setting were feasible (FIM ≥4) at M12. Fourteen participants completed interviews at baseline and M12. RESULTS: Community settings offered by sites included: home visits (n = 3), HIV support organizations (n = 2), and community clinic (n = 1). Of 114 participants, 54% were female, 70% racially minoritized, and 40% aged ≥50 years. A total of 27/114 chose to receive injections in community settings. FIM/AIM/IAM scores at M12 were high for the injection (79.0–87.4%) and lower for the community setting (44.2–47.4%) overall. Subgroup analyses indicated differences in scores by gender and ethnicity. Among those who attended the community, FIM/AIM/IAM scores for the community setting at M12 were high (73.1–80.8%). Concerns about stigma, inconvenience, and losing access to trusted clinicians negatively influenced perceptions of receiving injections at community settings, amongst other factors. CONCLUSIONS: CAB + RPV injections were considered highly feasible, acceptable, and appropriate; however, few chose community delivery. Those that chose community delivery found it highly acceptable and feasible. Further exploration of CAB + RPV delivery in alternative community sites not offered (eg, primary care, pharmacies) is warranted. CLINICALTRIALS.GOV: NCT05294159

Virus-induced vesicular acidification enhances HIV immune evasion

To inhibit endocytic entry of some viruses, cells promote acidification of endosomes by expressing the short isoform of human nuclear receptor 7 (NCOA7) which increases activity of vacuolar ATPase (V-ATPase). While we found that HIV-1 infection of primary T cells led to acidification of endosomes, NCOA7 levels were only marginally affected. Contrastingly, levels of the 50 kDa form of the sodium/hydrogen exchanger 6 (NHE6) were greatly reduced. NHE6 overexpression and low concentrations of the V-ATPase inhibitor, concanamycin A, selectively reversed endosomal acidification. Endosomal neutralization by these interventions also reduced Nef-dependent MHC-I downmodulation by our wildtype HIV reporter virus. NHE6 overexpression disrupted MHC-I downmodulation by reducing recruitment of Nef to Rab11 (+) compartments and inhibiting interactions between Nef, β-COP, and ARF-1. In addition, we found that the HIV Vif protein was required for downmodulation of the 50 kDa form of NHE6 and for endosomal acidification but was dispensable for Nef-dependent MHC-I downmodulation.

THC Reverses SIV-Induced Senescence in Astrocytes: Possible Compensatory Mechanism Against HIV Associated Brain Injury?

Despite effective combination antiretroviral therapy (cART), chronic neuroinflammation and glial dysfunction continues to be an important yet understudied issue with people living with HIV (PLWH). The endocannabinoid system is increasingly recognized as a potential therapeutic target for modulating neuroimmune environments, given its role in regulating synaptic plasticity, immune responses, and neuroinflammatory cascades. However, the extent to which cannabinoids influence HIV-associated neuroinflammation remains unclear. This study investigates the impact of Δ9-tetrahydrocannabinol (THC) on astrocyte growth characteristics, viability, and senescence-associated cytokine release following exposure to HIV Tat protein using primary mixed glial cultures derived from rhesus macaques. Real-time impedance-based cellular integrity assessments were conducted using the xCELLigence system, while morphological analyses and cytokine quantification were performed using phase-contrast microscopy and multiplex immunoassays. Treatment of macaques with THC protected the astrocytes from virus-induced senescence. Further, THC facilitated a rapid recovery from Tat-induced decline in astrocyte adhesion, suggesting a compensatory effect. THC promoted glial process elongation and morphological complexity, indicative of a shift toward a neuroprotective phenotype. Furthermore, THC significantly reduced inflammatory cytokine secretion, including TNF-α, IL-6, and IL-1β, in an apparently dose-dependent manner. These findings suggest that THC may modulate neuroinflammation in PLWH by promoting astrocytic survival, suppressing inflammatory cytokine secretion, and enhancing neurotrophic signaling. However, prolonged exposure to high-dose THC may negatively impact glial survival. The results underscore the complexity of cannabinoid signaling in the CNS and highlight the potential of cannabinoid-based interventions to mitigate HIV-associated neuroinflammation.

Bayesian Inference for a Time-Fractional HIV Model with Nonlinear Diffusion

This study investigates an inverse problem associated with a time-fractional HIV infection model incorporating nonlinear diffusion. The model describes the dynamics of uninfected target cells, infected cells, and free virus particles, where the diffusion terms are nonlinear density functions. The primary objective is to recover the unknown diffusion functions by utilizing final-time measurement data. Due to the inherent ill-posedness of the inverse problem and the presence of measurement noise, we employ a Bayesian inference framework to obtain stable and reliable estimates while quantifying uncertainty. To solve the inverse problem efficiently, we develop an Iterative Regularizing Ensemble Kalman Method (IREKM), which enables the simultaneous estimation of multiple diffusion terms without requiring gradient information. Numerical experiments validate the effectiveness of the proposed method in reconstructing the unknown diffusion terms under different noise levels, demonstrating its robustness and accuracy. These findings contribute to a deeper understanding of HIV infection dynamics and provide a computational approach for parameter estimation in fractional diffusion models.

Use of multiple pharmacies by people living with HIV

Using multiple pharmacies has been linked to negative outcomes including increased inappropriate drug use, toxicity, and mortality, yet up to half of all patients in the US use multiple pharmacies. A pilot survey was administered to measure pharmacy use in people living with HIV (PWH) and examine the associated attitudes and outcomes. In a midwestern HIV clinic, a convenience sample was surveyed based on using single (SPU) versus multiple pharmacies (MPU). One hundred forty-two subjects participated (69 SPU; 73 MPU): 75% male, 59% white, and > 70% being ≥ 40 years old. There was a trend towards increased recall of self-reported CD4 and viral load status for SPUs vs MPUs [OR 2.26 (95%, 0.91–5.64)]. SPU participants indicated acquiring all meds in one place [Likert Scale weighted average (LS WA) = 4.45], proximity (LS WA = 3.64), the identification of adverse effects (LS WA = 4.17), and concerns about missing potential drug-drug interactions (LS WA = 4.06) as reasons for preferring a single pharmacy. The desire to keep HIV status private (LS WA = 3.65) was noted as a significant reason for using a separate pharmacy specifically for acquiring antiretrovirals in MPU participants. Results indicate patients often have justifiable reasons for using single versus multiple pharmacies.

Exposure to COVID-19 Pandemic-Related Stressors and Their Association With Distress, Psychological Growth and Drug Use in People With HIV in Nouvelle Aquitaine, France (ANRS CO3 AQUIVIH-NA Cohort-QuAliV-QuAliCOV Study)

International audience; We investigated people living with HIV (PLWH)'s exposure to COVID-19 pandemic stressors and their association with distress, psychological growth, and substance use. PLWH in the ANRS CO3 AQUIVIH-NA cohort's QuAliV study (Nouvelle Aquitaine, France) completed an adapted CAIR Lab Pandemic Impact Questionnaire (C-PIQ) and reported substance use between 9/2021 to 3/2022. We described cumulative stressor exposure (score 0-16) and explored variation by PLWH characteristics (demographic, HIV-related, risk factors, psychosocial). Associations with distress (score 0-23), psychological growth (score 0-20), and substance use were assessed using regression models. Participants reported exposure to a median of 2 (IQR: 1-4) stressors. Stressor exposure was higher in working-age (&lt;60) and psychosocially vulnerable PLWH. Exposure to an additional stressor correlated with a 0.7-point increase in distress scores (95% C.I. 0.5-1.0, p&lt;0.001), a 0.04-point increase (95% C.I. 0.01-0.07, p=0.002) in psychological growth scores in working-age PLWH. In older PLWH, additional stressor correlated with a 0.8-point (95% C.I. 0.4-1.2, p&lt;0.001) increase in distress and a 0.1-point increase (95% C.I. 0.06-0.2, p=0.001) in growth scores. Each additional stressor was associated with 1.2 (95% C.I. 1.0-1.4, p=0.02) higher adjusted odds of cannabis use in working-age PLWH, and 1.2 (95% C.I. 1.0-1.4, p=0.004) higher adjusted odds of drug use. Exposure to stressors was linked to increased distress, cannabis and drug use but also growth. Providers should not only be aware of risk (of severe COVID-19) but also be mindful of the social and psychological challenges PLWH face as these may affect their retention in care, especially during challenging times.

Chemogenetic Activation of Medial Prefrontal Cortex Projections to the Nucleus Accumbens Shell Suppresses Cocaine-Primed Reinstatement in EcoHIV Infected Mice

HIV is highly comorbid with cocaine use disorder (CUD). Relapse is a major challenge in the treatment of CUD, and people living with HIV (PLWH) exhibit shorter time to relapse. One driver of relapse may be re-exposure to cocaine, which can be modeled in rodents using cocaine-primed reinstatement. This process involves neuroadaptations within the medial prefrontal cortex (mPFC) and nucleus accumbens (NAc) shell, regions that mediate cocaine reward learning and relapse-related behavior. HIV infection interacts with cocaine to alter corticostriatal circuits, which may further dysregulate cocaine seeking. To investigate the impact of HIV infection on cocaine reward learning and reinstatement and the role of mPFC-NAc circuits, we utilized the EcoHIV mouse model, a chimeric form of HIV-1 which can infect wild-type mice. Our findings demonstrate that EcoHIV infection enhances cocaine-primed reinstatement. We also observed increased cocaine-induced expression of the cellular activation marker cFos in the NAshell in EcoHIV-infected mice. Given the role of the mPFC-NAshell circuit in cocaine-seeking behaviors, we further demonstrated that chemogenetic activation of this circuit could reverse the behavioral deficits induced by EcoHIV. We propose that HIV infection contributes to neuroadaptations in the mPFC-NAshell circuit, and enhancing its activity may inhibit relapse-related behavior. These findings indicate that key neuronal circuits underlying cocaine reinstatement are similarly implicated in HIV infection and suggest potential strategies for managing relapse in PLWH.

High-throughput droplet-based analysis of influenza A virus genetic reassortment by single-virus RNA sequencing

International audience; The segmented RNA genome of influenza A viruses (IAVs) enables viral evolution through genetic reassortment after multiple IAVs coinfect the same cell, leading to viruses harboring combinations of eight genomic segments from distinct parental viruses. Existing data indicate that reassortant genotypes are not equiprobable; however, the low throughput of available virology techniques does not allow quantitative analysis. Here, we have developed a high-throughput single-cell droplet microfluidic system allowing encapsulation of IAV-infected cells, each cell being infected by a single progeny virion resulting from a coinfection process. Customized barcoded primers for targeted viral RNA sequencing enabled the analysis of 18,422 viral genotypes resulting from coinfection with two circulating human H1N1pdm09 and H3N2 IAVs. Results were highly reproducible, confirmed that genetic reassortment is far from random, and allowed accurate quantification of reassortants including rare events. In total, 159 out of the 254 possible reassortant genotypes were observed but with widely varied prevalence (from 0.038 to 8.45%). In cells where eight segments were detected, all 112 possible pairwise combinations of segments were observed. The inclusion of data from single cells where less than eight segments were detected allowed analysis of pairwise cosegregation between segments with very high confidence. Direct coupling analysis accurately predicted the fraction of pairwise segments and full genotypes. Overall, our results indicate that a large proportion of reassortant genotypes can emerge upon coinfection and be detected over a wide range of frequencies, highlighting the power of our tool for systematic and exhaustive monitoring of the reassortment potential of IAVs.

Clinical and Economic Inpatient Burden of Influenza and Influenza-Like Illness in Germany 2019–2025: Analysis of Nationwide Hospital Data

Introduction Data on the influenza burden in Germany during and after the global COVID-19 pandemic are limited. This study presents nationwide trends in the clinical and economic inpatient burden of influenza and influenza-like illness (ILI) in the German population from 2019 to 2025. Methods We conducted a descriptive, retrospective analysis of nationwide hospital data from the German Institute for Hospital Reimbursement covering six consecutive influenza seasons (2019–2020 to 2024–2025). Influenza/ILI hospitalizations were identified using the International Classification of Diseases version 10 (ICD-10) codes J09–J11 recorded as primary diagnosis. Outcomes included hospitalization counts, incidence rates, mean length of stay, ICU admissions, in-hospital mortality, and estimated costs, and were reported for three age groups (0–17 years, 18–59 years, ≥ 60 years). Regional variation across federal states was assessed using directly age-standardized rates. Results We identified 240,646 influenza/ILI hospitalizations, with the highest burden observed in the 2024–2025 season (87,745 cases; incidence: 105.0/100,000 population). Less than 5000 cases were observed in both the 2020–2021 and 2021–2022 seasons. Across all seasons, adults aged ≥ 60 years accounted for 48% of hospitalizations and 92% of 10,054 in-hospital deaths. In this age group, in-hospital mortality ranged between 5.8% and 12.3% per season. Mean hospitalization costs per case increased during the study period and were highest in older adults, reaching €5430 in 2023–2024 and €5421 in 2024–2025. Total inpatient costs of influenza/ILI were highest in 2024–2025 and estimated at €390 million. Regional hospitalization rates varied considerably, but patterns were inconsistent across seasons. Conclusions Influenza/ILI causes a substantial and re-emergent inpatient burden in Germany, disproportionately affecting older adults. These findings highlight the need for sustained prevention efforts to reduce the burden of influenza/ILI in Germany. Further research is needed to understand the substantial regional disparities between federal states.

Predicting Influenza A Viral Host Using PSSM and Word Embeddings

The rapid mutation of the influenza virus threatens public health. Reassortment among viruses with different hosts can lead to a fatal pandemic. However, it is difficult to detect the original host of the virus during or after an outbreak as influenza viruses can circulate between different species. Therefore, early and rapid detection of the viral host would help reduce the further spread of the virus. We use various machine learning models with features derived from the position-specific scoring matrix (PSSM) and features learned from word embedding and word encoding to infer the origin host of viruses. The results show that the performance of the PSSM-based model reaches the MCC around 95%, and the F1 around 96%. The MCC obtained using the model with word embedding is around 96%, and the F1 is around 97%. ;Comment: Accepted for publication at CIBCB 2021. V1: accepted version + minor correction to table 1; V2: corrected a minor typo; V3: update the formula of error rate; V4: replacing 'nested cv' with 'nested k-fold cv' for better clarity

Analysis of viral pneumonia and risk factors associated with severity of influenza virus infection in hospitalized patients from 2012 to 2016

BACKGROUND: Influenza viruses cause pneumonia in approximately one-third of cases, and pneumonia is an important cause of death. The aim was to identify risk factors associated with severity and those that could predict the development of pneumonia. METHODS: This retrospective, observational study included all adult patients with confirmed influenza virus infection admitted to Son Espases University Hospital during four influenza seasons in Spain (October to May) from to 2012–2016. RESULTS: Overall, 666 patients with laboratory-confirmed influenza were included, 93 (14%) of which were severe; 73 (10.9%) were admitted to Intensive Care Unit (ICU), 39 (5.8%) died, and 185 (27.7%) developed pneumonia. Compared to less severe cases, patients with severe disease: were less vaccinated (40% vs. 28%, p = 0.021); presented with more confusion (26.9% vs. 6.8%), were more hypoxemic (Horowitz index (PaO(2)/FiO(2)) 261 vs. 280), had higher C-reactive protein (CRP) (12.3 vs. 4.0), had more coinfections (26.8% vs. 6.3%) and had more pleural effusion (14% vs. 2.6%) (last six all p < 0.001). Risk factors significantly associated with severity were pneumonia [OR (95% CI) = 4.14 (2.4–7.16)], history of heart disease (1.84, 1.03–3.28), and confusion at admission (4.99, 2.55–9.74). Influenza vaccination was protective (0.53, 0.28–0.98). Compared to those without pneumonia, the pneumonia group had higher CRP (11.3 vs. 4.0, p < 0.001), lower oxygen saturation (92% vs. 94%, p < 0.001), were more hypoxic (PaO(2)/FiO(2) 266 vs. 281, p < 0.001), and incurred more mechanical ventilation, septic shock, admission to the ICU, and deaths (all four p < 0.001). Higher CRP and lower oxygen saturation were independent variables for predicting the development of pneumonia. CONCLUSIONS: Pneumonia, history of heart disease, confusion and no influenza vaccination were independent variables to present complications in patients admitted with influenza infection.

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.

An economic evaluation of two PCR-based respiratory panel assays for patients admitted to hospital with community-acquired pneumonia (CAP) in the UK, France and Spain

Background On admission to hospital, patients with community-acquired pneumonia (CAP), undergo extensive diagnostic testing. Two high-throughput laboratory-based PCR panels which return a result in 5.5 hours (h) have been developed to test for pathogens commonly associated with upper (Respiratory 1 Panel) and lower (Respiratory 3 Panel) respiratory tract infections (GeneFirst, Oxford). These could replace multiple diagnostic tests currently used. Methods An online survey, completed by senior clinicians in the UK, France and Spain, was used to collect data on the diagnostic testing of immunocompetent and immunocompromised adults admitted to hospital with CAP, including the cost of diagnostics. Data were used to inform a cost-comparison model. For each country, the average cost of diagnostic testing per patient was calculated separately for immunocompetent and immunocompromised patients. The model compared three testing strategies with standard of care (SoC). In the Panel 1 strategy, the Respiratory 1 Panel was used for patients that would otherwise have tests which could be replaced by Respiratory 1 Panel, equivalent strategies for Respiratory 3 Panel and for both panels combined were assessed. Results In total, 48 surveys were completed (UK = 17; France = 15; Spain = 16). Compared with SoC, the Panel 1 + 3 strategy was most favourable, resulting in cost savings for immunocompetent and immunocompromised patients respectively, of €22.09 (£18.50) and €26.12 (£21.88) in the UK, €99.60 and €108.77 in France and €27.07 and €51.87 in Spain. Conclusion In all three countries, the use of these respiratory panels could reduce the average cost of diagnostics used for patients admitted to hospital with CAP.

Epidemiological characteristics of imported respiratory infectious diseases in China, 2014‒2018

Background With the progress of globalization, international mobility increases, greatly facilitating cross-border transmission of respiratory infectious diseases (RIDs). This study aimed to analyze the epidemiological characteristics and factors influencing imported RIDs, with the goal of providing evidence to support adoption of high-tech, intelligent methods to early find imported RIDs and prevent their spread in China. Methods We obtained data of imported RIDs cases from 2014 to 2018 from the Inbound Sentinel Network of Customs and the National Notifiable Diseases Reporting System in China. We analyzed spatial, temporal, and population distribution characteristics of the imported RIDs. We developed an index to describe seasonality. Pearson correlation coefficients were used to examine associations between independent variables and imported cases. Data analyses and visualizations were conducted with R software. Results From a total of 1 409 265 253 inbound travelers, 31 732 (2.25/100 000) imported RIDs cases were reported. RIDs cases were imported from 142 countries and five continents. The incidence of imported RIDs was nearly 5 times higher in 2018 (2.81/100 000) than in 2014 (0.58/100 000). Among foreigners, incidence rates were higher among males (5.32/100 000), 0–14-year-olds (15.15/100 000), and cases originating in Oceania (11.10/100 000). The vast majority (90.3%) of imported RIDs were influenza, with seasonality consistent with annual seasonality of influenza. The spatial distribution of imported RIDs was different between Chinese citizens and foreigners. Increases in inbound travel volume and the number of influenza cases in source countries were associated with the number of imported RIDs. Conclusions Our study documented importation of RIDs into China from 142 countries. Inbound travel poses a significant risks bringing important RIDs to China. It is urgent to strengthen surveillance at customs of inbound travelers and establish an intelligent surveillance and early warning system to prevent importation of RIDs to China for preventing further spread within China. Graphical Abstract

Lipid nanoparticle composition for adjuvant formulation modulates disease after influenza virus infection in QIV vaccinated mice

Adjuvants can enhance vaccine effectiveness of currently licensed influenza vaccines. We tested influenza vaccination in a mouse model with two adjuvants: Sendai virus derived defective interfering (SDI) RNA, a RIG-I agonist, and an amphiphilic imidazoquinoline (IMDQ-PEG-Chol), TLR7/8 adjuvant. The negatively charged SDI RNA was formulated into lipid nanoparticles (LNPs) facilitating the direct delivery of a RIG-I agonist to the cytosol. We have previously tested SDI and IMDQ-PEG-Chol as standalone and combination adjuvants for influenza and SARS-CoV-2 vaccines. Here we tested two different ionizable lipids, K-Ac7-Dsa and S-Ac7-Dog, for LNP formulations. The adjuvanticity of IMDQ-PEG-Chol with and without empty or SDI-loaded LNPs was validated in a licensed vaccine setting (quadrivalent influenza vaccine or QIV) against H1N1 influenza virus, showing robust induction of antibody titres and T cell responses. Depending on the adjuvant combination and LNP lipid composition (K-Ac7-Dsa or S-Ac7-Dog lipids), humoral and cellular vaccine responses could be tailored towards type 1 or type 2 host responses with specific cytokine profiles that correlated with protection during viral infection. The extent of protection conferred by different vaccine/LNP/adjuvant combinations was examined against challenge with the vaccine-matching strain of H1N1 influenza A virus. Groups that received either LNP formulated with SDI, IMDQ-PEG-Chol or both showed very low levels of viral replication in their lungs at five days post virus infection. LNP ionizable lipid composition as well as loading (empty versus SDI) also skewed host responses to infection, as reflected in the cytokine and chemokine levels in lungs of vaccinated animals upon infection. These studies show the potential of LNPs as adjuvant delivery vehicles for licensed vaccines and illustrate the importance of LNP composition for subsequent host responses to infection, an important point of consideration for vaccine safety.

Pertussis and influenza immunization: perceived attitude and decision of postpartum patients

Background Vaccination of pregnant patients with tetanus toxoid, reduced diphtheria toxoid, and acellular pertussis (Tdap) and influenza vaccine during influenza season can reduce maternal and fetal morbidity and mortality; nevertheless, vaccination rates remain suboptimal in this patient population. To investigate the effect of a brief educational counseling session on maternal Tdap and influenza vaccination and determine factors influencing women’s decision in regards to receiving Tdap and or influenza vaccine during their pregnancy. Methods A face-to-face semi-structured cross-sectional survey was administered to postpartum patients on their anticipated day of discharge (June 11-August 21, 2018). A brief educational counseling session about maternal pertussis and Tdap vaccine was provided to interested patients after which the Tdap vaccine was offered to eligible patients who did not receive it during their pregnancy or upon hospital admission. Medical records were reviewed to determine if surveyed patients were vaccinated prior to discharge. Results Two hundred postpartum patients were surveyed on their day of anticipated discharge. Of those who were surveyed, 103 (51.5%) had received Tdap and 80 (40.0%) had received influenza vaccinations prior to hospitalization. Among immunized patients, the common facilitators were doctor’s recommendation (Tdap: 68, 54.4%; influenza: 3, 6.0%), to protect their baby (Tdap: 57, 45.6%; influenza: 17, 34.0%) and for self-protection (Tdap: 17, 13.6%; Influenza: 17, 34.0%). Of the 119 participants who had not received either Tdap or influenza vaccine prior to the survey, the barriers cited were that the vaccine was not offered by the provider (Tdap: 36, 52.2%; influenza: 29, 27.6%), belief that vaccination was unnecessary (Tdap: 5, 7.2%; influenza: 9, 8.5%), safety concerns for baby (Tdap: 4, 5.8%; influenza: 2, 1.9%). Of 97 patients who were not immunized with Tdap prior to admission but were eligible to receive vaccine, 24 (25%) were vaccinated prior to survey as part of routine hospital-based screening and vaccination program, 29 (38.2%) after our survey. Conclusion Interventions to educate pregnant patients about the benefits of vaccination for their baby, addressing patient safety concerns, and vaccine administration in obstetricians’ offices may significantly improve maternal vaccination rates.

Evaluating reduced effectiveness after repeat influenza vaccination while accounting for confounding by recent infection and within-season waning

BACKGROUND. Studies have reported that prior-season influenza vaccination is associated with higher risk of clinical influenza infection among vaccinees in a given season. Understanding the underlying causes requires consideration of within-season waning and recent infection. METHODS. Using the US Flu Vaccine Effectiveness (VE) Network data over 8 influenza seasons (2011-2012 to 2018-2019), we estimated the effect of prior-season vaccination on the odds of clinical infection in a given season, after accounting for waning vaccine protection using regression methods. We adjusted for potential confounding by recent clinical infection using inverse-probability weighting. We investigated theoretically whether unmeasured subclinical infection in the prior season, which is more likely in the non-repeat vaccinees, could explain the repeat vaccination effect. RESULTS. Repeat vaccinees vaccinated earlier in a season by one week. After accounting for waning VE, repeat vaccinees were still more likely to test positive for influenza A(H3N2) (OR=1.11, 95% CI: 1.02-1.21) but not for influenza B (OR=1.03, 95% CI:0.89-1.18) or A(H1N1) (OR=1.03, 95% CI:0.90-1.19) compared to those vaccinated in the given season only. Recent clinical infection with the homologous (sub)type protected against clinical infection with A(H3N2) or B. Individuals with clinical infection in one season had 1.11 (95% CI: 1.03-1.19) times the odds of switching vaccination status in the following season. Adjusting for recent clinical infections did not strongly influence the estimated effect of prior-season vaccination. Adjusting for subclinical infection could theoretically attenuate this effect. CONCLUSION. Waning protection and recent clinical infection were insufficient to explain observed reduced VE in repeat vaccinees with a test-negative design.

Association of frailty with influenza and hospitalization due to influenza among independent older adults: a longitudinal study of Japan Gerontological Evaluation Study (JAGES)

BACKGROUND: It is unknown that whether frailty is a risk factor of influenza and the hospitalization among older adults, although it has been shown that frailty was associated with poor recovery from the hospitalization among those. We examined the association of frailty with influenza and the hospitalization and the effect by sex among independent older adults. METHODS: We used the longitudinal data from the Japan Gerontological Evaluation Study (JAGES), performed in 2016 and 2019 and conducted in 28 municipalities in Japan. The target population comprised 77,103 persons aged ≥ 65 years who did not need assistance from the public long-term care insurance. Primary outcome measures were influenza and hospitalization due to influenza. Frailty was evaluated with the Kihon check list. We estimated the risk of influenza, the hospitalization, those risks by sex, and the interaction for frailty and sex using Poisson regression adjusting for covariates. RESULTS: Frailty was associated with both influenza and the hospitalization among the older adults compared with nonfrail individuals after adjusting for covariates (influenza, frail: risk ratio {RR}: 1.36, 95% confidence interval {95% CI}: 1.20 − 1.53, and prefrail: RR: 1.16, 95% CI: 1.09 − 1.23; the hospitalization, frail: RR: 3.18, 95% CI: 1.84 − 5.57, and prefrail: RR: 2.13, 95% CI: 1.44 − 3.16). Male was associated with the hospitalization, but not associated with influenza compared to female (the hospitalization: RR: 1.70, 95% CI: 1.15 − 2.52 and influenza: RR: 1.01, 95% CI: 0.95 − 1.08). The interaction for frailty and sex was significant neither in influenza nor in the hospitalization. CONCLUSION: These results suggest that frailty is a risk of influenza and the hospitalization, that risks of the hospitalization are different by sex, but that the sex difference does not cause the effect heterogeneity of frailty on the susceptibility and severity among independent older adults. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-023-03979-y.

Herpesviridae lung reactivation and infection in patients with severe COVID-19 or influenza virus pneumonia: a comparative study

BACKGROUND: Lung reactivations of Herpesviridae, herpes simplex virus (HSV) and cytomegalovirus (CMV) have been reported in COVID-19 patients. Whether or not those viral reactivations are more frequent than in other patients is not known. METHODS: Retrospective monocentric cohort study of 145 patients with severe COVID-19 pneumonia requiring invasive mechanical ventilation and who were tested for HSV and CMV in bronchoalveolar lavage performed during fiberoptic bronchoscopy for ventilator-associated pneumonia suspicion. Rates of HSV and CMV lung reactivations, and HSV bronchopneumonitis were assessed and compared with an historical cohort of 89 patients with severe influenza pneumonia requiring invasive mechanical ventilation. RESULTS: Among the 145 COVID-19 patients included, 50% and 42% had HSV and CMV lung reactivations, respectively, whereas among the 89 influenza patients, 63% and 28% had HSV and CMV lung reactivations, respectively. Cumulative incidence of HSV lung reactivation (taking into account extubation and death as competing events) was higher in influenza than in COVID-19 patients (p = 0.03), whereas the rate of HSV bronchopneumonitis was similar in both groups (31% and 25%, respectively). Cumulative incidence of CMV lung reactivation (taking into account extubation and death as competing events) was similar in COVID-19 and influenza patients (p = 0.07). Outcomes of patients with HSV or CMV lung reactivations were similar to that of patients without, whatever the underlying conditions, i.e., in COVID-19 patients, in influenza patients, or when all patients were grouped. CONCLUSIONS: HSV and CMV lung reactivations are frequent in COVID-19 patients, but not more frequent than in patients with influenza-associated severe pneumonia, despite a higher severity of illness at intensive care unit admission of the latter and a longer duration of mechanical ventilation of the former. Although no impact on outcome of HSV and CMV lung reactivations was detected, the effect of antiviral treatment against these Herpesviridae remains to be determined in these patients. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13613-022-01062-0.

Safety and Immunogenicity of Bivalent RSVpreF Vaccine Coadministered With Seasonal Inactivated Influenza Vaccine in Older Adults

BACKGROUND: Respiratory syncytial virus (RSV) and influenza are both typically seasonal diseases, with winter peaks in temperate climates. Coadministration of an RSV vaccine and influenza vaccine could be a benefit, requiring 1 rather than 2 visits to a healthcare provider for individuals receiving both vaccines. METHODS: The primary immunogenicity objective of this phase 3, 1:1 randomized, double-blind, placebo-controlled study in healthy adults aged ≥65 years in Australia was to demonstrate noninferiority of immune responses with coadministration of the stabilized RSV prefusion F protein–based vaccine (RSVpreF) and seasonal inactivated influenza vaccine (SIIV) versus SIIV or RSVpreF administered alone, using a 1.5-fold noninferiority margin (lower bound 95% confidence interval >.667). Safety and tolerability were evaluated by collecting reactogenicity and adverse event data. RESULTS: Of 1403 participants randomized, 1399 received vaccinations (median age, 70; range, 65‒91 years). Local reactions and systemic events were mostly mild or moderate when RSVpreF was coadministered with SIIV or administered alone. No vaccine-related serious adverse events were reported. Geometric mean ratios were 0.86 for RSV-A and 0.85 for RSV-B neutralizing titers at 1 month after RSVpreF administration and 0.77 to 0.90 for strain-specific hemagglutination inhibition assay titers at 1 month after SIIV. All comparisons achieved the prespecified 1.5-fold noninferiority margin. CONCLUSIONS: The primary study objectives were met, demonstrating noninferiority of RSVpreF and SIIV immune responses when RSVpreF was coadministered with SIIV and that RSVpreF had an acceptable safety and tolerability profile when coadministered with SIIV. The results of this study support coadministration of RSVpreF and SIIV in an older-adult population. CLINICAL TRIALS REGISTRATION: https://clinicaltrials.gov/study/NCT05301322.

Influenza vaccine uptake among children and older adults in China: a secondary analysis of a quasi-experimental study

Background Influenza vaccination is the key to prevent influenza-related disease, especially among high-risk populations. However, influenza vaccine uptake in China is low. This secondary analysis of a quasi-experimental trial aimed to understand factors associated with influenza vaccine uptake among children and older people stratified by funding context. Methods A total of 225 children (aged 0.5-8 years) and 225 older people (aged 60 years or above) were recruited from three clinics (rural, suburban and urban) in Guangdong Province. Participants were allocated into two groups based on funding contexts: a self-paid group (N = 150, 75 children and 75 older adults) in which participants paid full price for their vaccination; and a subsidized group (N = 300, 150 children and 150 older adults) in which varying levels of financial support was provided. Univariate and multivariable logistic regressions were conducted stratified by funding contexts. Results Overall, 75.0% (225/300) of participants in the subsidized group and 36.7% (55/150) in the self-paid group got vaccinated. Older adults had lower vaccination rates than children in both funding groups, while both age groups showed much higher uptake in the subsidized group than in the self-paid group (aOR = 5.96, 95% CI: 3.77–9.42, p = 0.001). In the self-paid group, having prior influenza vaccination history of children (aOR:2.61, 95%CI: 1.06–6.42) or older people (aOR:4.76, 95%CI: 1.08–20.90) was associated with increased influenza vaccine uptake compared to those who had no prior vaccination experiences in the family. While in the subsidized group, participants who got married or lived with partners (aOR = 0.32, 0.10–0.98) had lower vaccination uptake than single ones. Trust in providers’ advice (aOR = 4.95, 95%CI:1.99, 12.43), perceived effectiveness of the vaccine (aOR: 12.18, 95%CI: 5.21–28.50), and experienced influenza-like illnesses in the family in the past year (aOR = 46.52, 4.10, 533.78) were associated with higher vaccine uptake. Conclusions Older people had suboptimal vaccine uptake compared to children in both contexts and need more attention to enhance influenza vaccination. Tailoring interventions to different vaccine funding contexts may help improve influenza vaccination: In self-paid context, motivating people to accept their first ever influenza vaccination may be a promising strategy. In subsidized context, improving public confidence in vaccine effectiveness and providers’ advice would be useful.

MO1043: Influenza Vaccine Awareness and Acceptance Among Individuals with Glomerulonephritis and Renal Vasculitis During the Corononavirus Disease 2019 Pandemic

BACKGROUND AND AIMS: Patients with glomerulonephritis may have an increased risk of influenza infection and morbidity, but vaccine coverage remained low with little data on acceptance in glomerulonephritis. We aimed to assess influenza vaccine awareness among patients with glomerulonephritis and identify determinants of vaccine acceptance. METHOD: Single-center cross-sectional study of patients with glomerulonephritis who completed a survey in the clinic or over the telephone between June and August 2021. Sociodemographic data, health literacy measured by HLS-EU-47 questionnaire, influenza and the coronavirus disease (COVID-19) vaccine awareness and determinants of vaccine acceptance according to the World Health Organization framework. RESULTS: Among 86 patients who completed the survey, influenza vaccine awareness was lower than COVID-19 vaccine awareness (75.6% versus 100%). After adjusting for the survey type, use of English language at home and at healthcare settings, higher income and professional or executive occupation were significantly associated with influenza vaccine awareness, while older age and lower education level were associated with reduced awareness. The healthcare provider was the most frequent information source and > 90% trusted that healthcare providers and the government considered the patients’ best interests and gave correct information. Only half thought their medical condition and medications would affect their vaccine decision while a quarter to half did not understand how the vaccine worked and thought there were better ways to protect against infection. CONCLUSION: Healthcare providers can actively identify and advocate influenza vaccines to the unaware and overcome potential barriers to reduce influenza infections and morbidity in glomerulonephritis.

A prospective, comparative cohort analysis of influenza antibody waning in Michigan and Hong Kong during periods of low influenza circulation

BACKGROUND: Reduced influenza transmission during the COVID-19 pandemic prompted concern about waning of population immunity that could lead to subsequent surges in circulation. We evaluated this by comparing longitudinal influenza antibody titers in Michigan and Hong Kong, two regions with reduced influenza transmission during the COVID-19 pandemic. METHODS: In two prospective cohort studies (HIVE, Michigan; EPI-HK, Hong Kong), we analyzed longitudinal serum samples collected from 2020 through 2023 from participants without documented influenza virus infection or vaccination. Sera were tested using hemagglutination inhibition assays (HAI) against relevant vaccine strains. Geometric mean titers (GMTs) and fold changes were estimated by region and time. Linear mixed-effects models were used to assess temporal trends. RESULTS: We analyzed 173 sera from 57 HIVE participants and 259 sera from 60 EPI-HK participants. Initial GMTs in 2020–21 ranged from 12.3–123.4 in HIVE and 6.3–40.9 in EPI-HK (B/Yamagata–H1N1). Fold changes in GMTs ranged from 1.2–2.6 in HIVE and 0.7–1.0 in EPI-HK. In HIVE models, no significant change in HAI titers over time was detected. In EPI-HK, small but statistically significant monthly declines were observed for select H1N1 (A/Michigan) and H3N2 (A/Hong Kong) strains (e.g., A/Hong Kong: −0.98%, 95% CI: −1.82% to −0.11%). CONCLUSION: Minimal HAI titer waning was observed in both regions. In some cases, antibody levels increased in Michigan, possibly indicating cryptic circulation of strains prior to the 2022/23 influenza season. These findings do not support an “immunity debt” during pandemic restrictions and could help explain the lack of a substantial surge in influenza impact after the COVID-19 pandemic.

Influenza vaccination patterns among at-risk patients during the Covid-19 pandemic—a retrospective cross-sectional study based on claims data

Purpose The Covid-19 pandemic may have encouraged at-risk patients to get vaccinated against influenza for the first time. As previous vaccinations are known predictors for further vaccinations, knowledge about individual vaccination patterns, especially in first time vaccinated patients, is of great interest. The aim of this study was to determine influenza vaccination uptake rate (VUR), individual vaccination patterns and factors associated with vaccination uptake among at-risk patients. Methods The study design was retrospective cross-sectional. Based on claims data, VUR was determined for four influenza seasons (2018/2019—2021/2022). In a cohort subgroup, with data available for all seasons, VUR, vaccination patterns and factors associated with uptake were determined. At-risk patients were people aged ≥ 65 and adult patients with chronic diseases. Results We included n  = 238,461 patients in the cross-sectional analysis. Overall VUR ranged between 21.8% (2018/2019) and 29.1% (2020/2021). Cohort subgroup consisted of n  = 138,526 patients. Within the cohort, 56% were never vaccinated and 11% were vaccinated in all seasons. 14.3% of previously unvaccinated patients were vaccinated for the first time in the first pandemic season (2020/2021 season). The strongest predictor for vaccination was history of vaccinations in all previous seasons (OR 56.20, 95%CI 53.62–58.90, p < 0.001). Conclusion Influenza VUR increased during the Covid-19 pandemic, but only a minority of previously eligible but unvaccinated at-risk patients were vaccinated for the first time in the first pandemic season. Previous vaccinations are predictors for subsequent vaccinations and health care professionals should actively address at-risk patients’ vaccination history in order to recommend vaccination in future seasons.

Determinants of non-vaccination against seasonal influenza during pregnancy

Objective The objective of this study was to identify the determinants of influenza non-vaccination during pregnancy in Canada. Methods Biological mothers of children born between December 2018 and March 2019 were surveyed about vaccinations they had received during pregnancy, reasons for non-vaccination, obstetrical history, and demographics. Simple and multiple logistic regression models were used to measure associations between various sociodemographic factors as well as obstetrical history, and non-vaccination against influenza. We analyzed data from 2361 mothers. Results Factors associated with non-vaccination included being followed during pregnancy by a midwife compared to by an obstetrician-gynecologist (OR 2.02; 95% CI, 1.17‒3.50); having two or more past live births compared to none (OR 1.58; 95% CI, 1.01‒2.49); having an education level below high school diploma compared to a bachelor’s degree or above (OR 2.50; 95% CI, 1.06‒5.90); and having a household income below $60,000 (OR 2.46; 95% CI, 1.42‒4.24) or between $60,000 and $99,999 (OR 2.77; 95% CI, 1.70‒4.52) compared to a household income of $140,000 or more. The province or territory of prenatal care proved to be an important factor in non-vaccination, with statistically significant odds ratios for certain provinces: OR 7.50 (95% CI, 1.40‒40.26) for Ontario, 8.23 (95% CI, 1.53‒44.23) for Newfoundland and Labrador, and 11.39 (95% CI, 2.14‒60.60) for Quebec, as compared to the territories. Conclusion Despite universal access to influenza vaccines in Canada during pregnancy, regional variations and socioeconomic disparities in non-vaccination are still observable. Objectif Identifier les déterminants de la non-vaccination contre la grippe pendant la grossesse au Canada. Méthodes Notre étude porte sur 2 361 mères biologiques d’enfants nés entre décembre 2018 et mars 2019 qui ont été interrogées sur les vaccins reçus pendant leur grossesse, les raisons de non-vaccination, leurs antécédents obstétricaux, et leurs caractéristiques démographiques. Des modèles de régression logistique simple et multiple ont été utilisés pour mesurer les associations entre divers facteurs sociodémographiques, les antécédents obstétricaux, et la non-vaccination contre l’influenza. Résultats Les facteurs associés à la non-vaccination comprennent le suivi de grossesse par une sage-femme par rapport à un obstétricien-gynécologue (RC 2,02; IC 95% : 1,17‒3,50); avoir eu deux naissances vivantes ou plus par rapport à aucune (RC 1,58; IC 95% : 1,01‒2,49); avoir une scolarité inférieure au diplôme d’études secondaires par rapport à un baccalauréat ou plus (RC 2,50; IC 95% : 1,06‒5,90); et avoir un revenu du ménage inférieur à 60 000 $ (RC 2,46; IC 95% : 1,42‒4,24) ou entre 60 000 $ et 99 999 $ (RC 2,77; IC 95% : 1,70‒4,52) par rapport à un revenu ménager de 140 000 $ ou plus. La province ou le territoire de soins prénataux s’est avéré un facteur important de la non-vaccination avec des rapports de cote statistiquement significatifs pour certaines provinces : RC 7,50 (IC 95% : 1,40‒40,26) pour l’Ontario, 8,23 (IC 95% : 1,53‒44,23) pour Terre-Neuve-et-Labrador, et 11,39 (IC 95% : 2,14‒60,60) pour le Québec, comparativement aux territoires. Conclusion Malgré l’accès universel aux vaccins antigrippaux au Canada durant la grossesse, des variations régionales et des disparités socioéconomiques en non-vaccination persistent.

Polymer formulated self-amplifying RNA vaccine is partially protective against influenza virus infection in ferrets

COVID-19 has demonstrated the power of RNA vaccines as part of a pandemic response toolkit. Another virus with pandemic potential is influenza. Further development of RNA vaccines in advance of a future influenza pandemic will save time and lives. As RNA vaccines require formulation to enter cells and induce antigen expression, the aim of this study was to investigate the impact of a recently developed bioreducible cationic polymer, pABOL for the delivery of a self-amplifying RNA (saRNA) vaccine for seasonal influenza virus in mice and ferrets. Mice and ferrets were immunized with pABOL formulated saRNA vaccines expressing either haemagglutinin (HA) from H1N1 or H3N2 influenza virus in a prime boost regime. Antibody responses, both binding and functional were measured in serum after immunization. Animals were then challenged with a matched influenza virus either directly by intranasal inoculation or in a contact transmission model. While highly immunogenic in mice, pABOL-formulated saRNA led to variable responses in ferrets. Animals that responded to the vaccine with higher levels of influenza virus-specific neutralizing antibodies were more protected against influenza virus infection. pABOL-formulated saRNA is immunogenic in ferrets, but further optimization of RNA vaccine formulation and constructs is required to increase the quality and quantity of the antibody response to the vaccine.

Enhanced Safety Surveillance of GSK’s Inactivated Quadrivalent Seasonal Influenza Vaccine in Belgium, Germany, and Spain During the 2022/2023 Influenza Season

Background Seasonal influenza is prevented through annual vaccination, especially in children and older adults. These vaccines are annually updated based on World Health Organization recommendations and require continuous safety monitoring. Objective We assessed the frequency and severity of adverse events within 7 days of administering GSK’s inactivated quadrivalent seasonal influenza vaccine (IIV4) in Belgium, Germany, and Spain during the 2022/2023 influenza season. Methods In this enhanced safety surveillance study, adults who received GSK’s IIV4 and parents/guardians/legally acceptable representatives of vaccinated children (aged 6 months–17 years) were invited to complete adverse drug reaction cards reporting adverse events within 7 days post-vaccination. Results In total, 1332 participants (53.6% female) received at least one dose of GSK’s IIV4, including 43 children who received two doses. Overall, 97.8% of adverse drug reaction cards were completed and returned in the study. All participants in Belgium were adults, while 54.7% and 7.4% in Spain and Germany, respectively, were pediatric participants aged 6 months–17 years. After Dose 1, across all age groups, 49.8% of participants reported at least one adverse event. The most common adverse events (cumulative frequency >5%) following Dose 1 were injection-site pain (37.6%), fatigue (15.0%), headache (13.2%), injection-site swelling (9.3%), myalgia (7.6%), and injection-site erythema (7.4%). Across all countries, adverse events were most common in adults aged 18–65 years (59.7%), followed by those aged 3–17 years (47.0%), >65 years (35.7%), and 6–35 months (23.5%). After Dose 2, 18.6% of participants reported at least one adverse event, with general disorders and administration site conditions again being the most frequent. Conclusions Across all age and risk groups for serious disease, no serious adverse events related to GSK’s IIV4 were reported within 7 days post-vaccination. This study supports and confirms the acceptable safety profile of GSK’s IIV4 across all recommended age groups. Clinical Trial Registration ClinicalTrials.gov number: not applicable. Graphical Abstract

Evolutionary and temporal dynamics of emerging influenza D virus in Europe (2009-22)

International audience; Influenza D virus (IDV) is an emerging influenza virus that was isolated for the first time in 2011 in the USA from swine with respiratory illness. Since then, IDV has been detected worldwide in different animal species, and it was also reported in humans. Molecular epidemiological studies revealed the circulation of two major clades, named D/OK and D/660. Additional divergent clades have been described but have been limited to specific geographic areas (i.e. Japan and California). In Europe, IDV was detected for the first time in France in 2012 and subsequently also in Italy, Luxembourg, Ireland, the UK, Switzerland, and Denmark. To understand the time of introduction and the evolutionary dynamics of IDV on the continent, molecular screening of bovine and swine clinical samples was carried out in different European countries, and phylogenetic analyses were performed on all available and newly generated sequences. Until recently, D/OK was the only clade detected in this area. Starting from 2019, an increase in D/660 clade detections was observed, accompanied by an increase in the overall viral genetic diversity and genetic reassortments. The time to the most recent common ancestor (tMRCA) of all existing IDV sequences was estimated as 1995-16 years before its discovery, indicating that the virus could have started its global spread in this time frame. Despite the D/OK and D/660 clades having a similar mean tMRCA (2007), the mean tMRCA for European D/OK sequences was estimated as January 2013 compared to July 2014 for European D/660 sequences. This indicated that the two clades were likely introduced on the European continent at different time points, as confirmed by virological screening findings. The mean nucleotide substitution rate of the hemagglutinin-esterase-fusion (HEF) glycoprotein segment was estimated as 1.403 × 10 −3 substitutions/site/year, which is significantly higher than the one of the HEF of human influenza C virus (P < 0.0001). IDV genetic drift, the introduction of new clades on the continent, and multiple reassortment patterns shape the increasing viral diversity observed in the last years. Its elevated substitution rate, diffusion in various animal species, and the growing evidence pointing towards zoonotic potential justify continuous surveillance of this emerging influenza virus.

Burden of influenza A (H1N1)pdm09 infection among tuberculosis patients: a prospective cohort study

Background Influenza and tuberculosis both cause significant morbidity and mortality worldwide. Therefore, this study aimed to estimate the burden of influenza A (H1N1)pdm09 virus infection among human tuberculosis patients and the general population. Methods A prospective cohort study was conducted among a cohort group (TB positive patients) as exposed and a comparison group (general population) as non-exposed. A total of 304 participants were recruited in both groups and followed for a period of 12 weeks. Of the 304 concurrently enrolled individuals, 152 were TB-positive patients (cohort group) and 152 were from the general population (comparison group).To calculate the sample size, the power of study was kept at 80% for detecting a difference at 5% alpha level assuming the 25% prevalence of respiratory viruses in cohort group compared to 12.5% in general population. An oropharyngeal swab was taken from a participant with symptoms of influenza-like illness (ILI). Samples were tested by conventional reverse transcription polymerase chain reaction (RT-PCR) for the detection of influenza A (H1N1)pdm09. All statistical analyses were conducted using R software. Results A total of 95 participants developed influenza-like illness (ILI) symptoms. Among these, 64 tested positive for influenza A(H1N1)pdm09, of which 39 were from the exposed group and 25 were from the non-exposed group. During the 12-week period of follow-up, the influenza A (H1N1)pdm09 incidence rate was 20 per 1000 people. The risk of testing positive for influenza A (H1N1)pdm09 was 1.66 times higher in the exposed group compared to the non-exposed group. The cumulative incidence indicated that 25% of the TB cohort and 16% of the comparison group were at risk of getting influenza A (H1N1)pdm09 during the 12 weeks of follow-up. Conclusion Participants from the TB cohort had a higher incidence of influenza A (H1N1)pdm09 than the general population suggesting that they should be prioritized for influenza vaccination.

Classification of Influenza Hemagglutinin Protein Sequences using Convolutional Neural Networks

The Influenza virus can be considered as one of the most severe viruses that can infect multiple species with often fatal consequences to the hosts. The Hemagglutinin (HA) gene of the virus can be a target for antiviral drug development realised through accurate identification of its sub-types and possible the targeted hosts. This paper focuses on accurately predicting if an Influenza type A virus can infect specific hosts, and more specifically, Human, Avian and Swine hosts, using only the protein sequence of the HA gene. In more detail, we propose encoding the protein sequences into numerical signals using the Hydrophobicity Index and subsequently utilising a Convolutional Neural Network-based predictive model. The Influenza HA protein sequences used in the proposed work are obtained from the Influenza Research Database (IRD). Specifically, complete and unique HA protein sequences were used for avian, human and swine hosts. The data obtained for this work was 17999 human-host proteins, 17667 avian-host proteins and 9278 swine-host proteins. Given this set of collected proteins, the proposed method yields as much as 10% higher accuracy for an individual class (namely, Avian) and 5% higher overall accuracy than in an earlier study. It is also observed that the accuracy for each class in this work is more balanced than what was presented in this earlier study. As the results show, the proposed model can distinguish HA protein sequences with high accuracy whenever the virus under investigation can infect Human, Avian or Swine hosts.

Co-expression network analysis identifies potential candidate hub genes in severe influenza patients needing invasive mechanical ventilation

Background Influenza is a contagious disease that affects people of all ages and is linked to considerable mortality during epidemics and occasional outbreaks. Moreover, effective immunological biomarkers are needed for elucidating aetiology and preventing and treating severe influenza. Herein, we aimed to evaluate the key genes linked with the disease severity in influenza patients needing invasive mechanical ventilation (IMV). Three gene microarray data sets (GSE101702, GSE21802, and GSE111368) from blood samples of influenza patients were made available by the Gene Expression Omnibus (GEO) database. The GSE101702 and GSE21802 data sets were combined to create the training set. Hub indicators for IMV patients with severe influenza were determined using differential expression analysis and Weighted correlation network analysis (WGCNA) from the training set. The receiver operating characteristic curve (ROC) was also used to evaluate the hub genes from the test set's diagnostic accuracy. Different immune cells' infiltration levels in the expression profile and their correlation with hub gene markers were examined using single-sample gene set enrichment analysis (ssGSEA). Results In the present study, we evaluated a total of 447 differential genes. WGCNA identified eight co-expression modules, with the red module having the strongest correlation with IMV patients. Differential genes were combined to obtain 3 hub genes (HLA-DPA1, HLA-DRB3, and CECR1). The identified genes were investigated as potential indicators for patients with severe influenza who required IMV using the least absolute shrinkage and selection operator (LASSO) approach. The ROC showed the diagnostic value of the three hub genes in determining the severity of influenza. Using ssGSEA, it has been revealed that the expression of key genes was negatively correlated with neutrophil activation and positively associated with adaptive cellular immune response. Conclusion We evaluated three novel hub genes that could be linked to the immunopathological mechanism of severe influenza patients who require IMV treatment and could be used as potential biomarkers for severe influenza prevention and treatment.

Improving outpatient care in adult inflammatory bowel disease: effect of implementation of a reminder checklist in the electronic health records (IBD-ERS)—a pilot study

Studies have shown that patients with inflammatory bowel disease (IBD) do not receive age appropriate preventive care services at the same rate as the general population. Providers extract information on preventive measures compliance by chart review, discussion with patients or deferment to primary care providers to ensure and document compliance. The aim of this pilot study was to evaluate the effectiveness of our standardised template which was incorporated in the electronic health records in order to provide the highest quality of clinical care and improve efficiency. We compared the outcomes before and after implementation of the template. In our preimplementation phase, we performed retrospective single-centre chart review of all patients diagnosed with IBD and treated with an immune modulator therapy between years January 2015–December 2016 and December 2019–July 2020. Preventive care measures included influenza and pneumonia, smoking cessation, checking thiopurine methyltransferase (TPMT) enzyme activity prior to starting thiopurines, screening for hepatitis B status, and tuberculosis (TB) testing prior to starting anti-TNF therapy. A total of 200 patients were included. Prior to the template implementation, manual extraction of data showed about 43% and 31% of the patients with IBD received influenza vaccination in 2015 and 2016, respectively. There were 40.9% who received pneumococcal vaccination, 57.5% with TPMT activity prior to thiopurine use, 60% had hepatitis B testing and only 12.5% had documented TB test. Post intervention, there was a significant increase in vaccination rates with 93.1% and 87.6% received influenza and pneumococcal vaccination, respectively (p<0.0005). About 94.7% had TPMT activity, 96.8% had hepatitis B and 98.9% had TB test completed (p<0.0005). The average time (minutes) to obtain information for each patient decreased from 12.27 to 4.62. Our study demonstrated a significant improvement in documented immunisation rates and quality of preventive care after implementation of standardised template.

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).

Sustained gut dysbiosis and intestinal inflammation show correlation with weight gain in person with chronic HIV infection on antiretroviral therapy

Background Person with human immunodeficiency virus type-1 (PWH) are prone to chronic inflammation due to residual viral production, even with antiretroviral therapy (ART), which increases the risk of age-related diseases. There is also limited information on changes in the intestinal environment of PWH during ART. In this longitudinal study, we investigated changes in the gut microbiota, persistence of chronic inflammation, interactions between the gut environment and inflammation, and metabolic changes in PWH using long-term ART. Results We analyzed changes in clinical parameters and gut microbiota in 46 PWH over a mean period of 4 years to understand the influence of gut dysbiosis on inflammation. Overall, changes in the gut microbiota included a decrease in some bacteria, mainly involved in short-chain fatty acid (SCFA) production, and an increase in certain opportunistic bacteria. Throughout the study period, an increase in bacterial-specific metabolic activity was observed in the intestinal environment. Continued decline in certain bacteria belonging to the Clostridia class and metabolic changes in gut bacteria involved in glucose metabolism. Additionally, patients with a low abundance of Parabacteroides exhibited low bacterial alpha diversity and a significant increase in body mass index (BMI) during the study period. Monocyte chemoattractant protein 1, a marker of macrophage activation in the plasma, continued to increase from baseline (first stool collection timepoint) to follow-up (second stool collection timepoint), demonstrating a mild correlation with BMI. Elevated BMI was mild to moderately correlated with elevated levels of plasma interleukin 16 and chemokine ligand 13, both of which may play a role in intestinal inflammation and bacterial translocation within the gut microbiota. The rate of BMI increase correlated with the rate of decrease in certain SCFA-producing bacteria, such as Anaerostipes and Coprococcus 3 . Conclusion Our data suggest that despite effective ART, PWH with chronic inflammation exhibit persistent dysbiosis associated with gut inflammation, resulting in a transition to an intestinal environment with metabolic consequences. Moreover, the loss of certain bacteria such as Parabacteroides in PWH correlates with weight gain and may contribute to the development of metabolic diseases.

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.

Teaching about dermatophytosis in the era of antifungal resistance: design, implementation, and evaluation of a multidisciplinary workshop in Honduras

Background The global increase in fungal infections and the emergence of antifungal-resistant species, particularly Trichophyton indotineae , have exposed critical gaps in medical mycology training and diagnostic capacity, especially in low- and middle-income countries. Despite their high prevalence, dermatophytoses are underrepresented in health sciences curricula and continuing professional education, contributing to misdiagnosis, delayed treatment, and inappropriate antifungal use. Methods We designed, implemented, and evaluated a multidisciplinary educational workshop entitled “Medical Mycology: Dermatophytoses, New Challenges in the Era of Antifungal Resistance”, conducted in Honduras in September 2025. The 24-hour in-person intervention combined 16 h of theoretical instruction with 8 h of hands-on laboratory training and targeted healthcare professionals and laboratory personnel nationwide. Educational impact was assessed using pre- and post-intervention questionnaires evaluating knowledge on dermatophyte taxonomy, identification methods, epidemiology, and antifungal resistance. Additionally, a structured survey was applied to characterize participants’ institutional diagnostic capacities. Descriptive analyses were performed to compare pre- and post-workshop responses. Results Among the 36 participants who completed both assessments, post-workshop results demonstrated substantial learning gains across all evaluated domains. The largest improvements were observed in recognition of recently revised dermatophyte genera and in understanding the transmission, clinical relevance, and terbinafine resistance mechanisms of T. indotineae . Correct response rates increased to near 100% for most items. The diagnostic capacity survey revealed that diagnostic practices in participating institutions are predominantly based on phenotypic methods, characterized by reliance on direct microscopy and culture, the exclusive use of morphological identification, and the absence of molecular methods, antifungal susceptibility testing, and MALDI-TOF mass spectrometry. The workshop also facilitated the development of standardized didactic materials and the establishment of a preliminary national network for future molecular surveillance. Conclusions This experience demonstrates that a structured, evidence-based educational intervention can significantly strengthen knowledge and practical skills in medical mycology. Such initiatives represent a scalable model to improve dermatophyte diagnosis and antifungal resistance awareness in resource-limited settings.

“But how true that is, I do not know”: the influence of written sources on the medicinal use of fungi across the western borderlands of the former Soviet Union

Fungi have been used for medicinal purposes for many centuries. This study, based on 35 historical written sources and 581 in-depth semi-structured interviews from eight countries in the western borderlands of the former Soviet Union, investigates the medicinal use of fungi by local communities. We compared the taxa and uses obtained from fieldwork and historical sources with works that advocated fungi use within Soviet herbals, representing the centralised medical system. During fieldwork, we identified eight locally used fungi and one lichen. The highest numbers of medicinal uses were documented in Russia, Estonia and Ukraine. Studies published before the Soviet era listed 21 fungal taxa and one lichen species used in the study region. However, only six of these taxa were mentioned as used by people in our field studies ( Amanita muscaria , Boletus edulis , Lycoperdon , Morchella , Phallus impudicus and Cetraria islandica ). Notably, these same six taxa were consistently endorsed in Soviet herbals. Of the remaining three taxa recorded in the fieldwork, none were mentioned in historical written sources. However, they were promoted either in Soviet herbals ( Inonotus obliquus , Kombucha ) or later popular publications ( Cantharellus cibarius ). This highlights the significant influence of written sources on the use of fungi for medicinal purposes within the studied local communities.

Fungi Matter: Aphanoascella galapagosensis Associated with Carapace Lesions in Free-Living Galapagos Tortoises

Galapagos giant tortoises are among the most iconic reptile species on earth; however, an increase in anthropogenic activities has created new challenges for their health and well-being. The presence of whitish lesions on the carapace of Galapagos tortoises ( Chelonoidis spp.) was previously described, potentially due to fungal growths, but its etiology remained unexplored. Aiming to close this gap, we analyzed carapace scrapes from six different species of free-living giant tortoises of Santa Cruz, Isabela, San Cristobal, and Española islands. In total, we tested 145 fresh and frozen carapace scrapes from 145 individuals with carapace whitish lesions (W-L, n  = 80) and without them (W-O, n  = 65), using panfungal endpoint PCRs for the ITS and D1-D2 regions. Aphanoascella galapagosensis was detected in W-L samples from all tortoise species and in none of the W-O samples. Four A. galapagosensis nucleotide sequence types (ST) obtained by using the D1-D2 protocol were identified in these tortoises; ST1 was detected on Santa Cruz, Isabela, and Española Islands whereas ST2 and ST3 were only detected on Isabela, and ST4 on San Cristobal. Neodevriesia spp. and Elsinoe spp. were the most common microorganisms found in W-O samples. These results suggest that A. galapagosensis is the etiological agent of whitish lesions in tortoise carapace contributing to baseline data on carapace fungi in giant Galapagos tortoises. Further research is needed to assess the prevalence and potential pathogenicity of A. galapagosensis and its impact for the conservation of these endangered species.

Antibiotic feeding changes the bacterial community of Chilo suppressalis and thereby affects its pesticide tolerance

Background Owing to the widespread use of chemical pesticides to control agricultural pests, pesticide tolerance has become a serious problem. In recent years, it has been found that symbiotic bacteria are related to pesticides tolerance. To investigate the potential role of microorganisms in the pesticide tolerance of Chilo suppressalis , this study was conducted. Results The insect was fed with tetracycline and cefixime as the treatment group (TET and CFM, respectively), and did not add antibiotics in the control groups (CK). The 16S rDNA sequencing results showed that antibiotics reduced the diversity of C. suppressalis symbiotic microorganisms but did not affect their growth and development. In bioassays of the three C. suppressalis groups (TET, CFM, and CK), a 72 h LC_50 fitting curve was calculated to determine whether long-term antibiotic feeding leads to a decrease in pesticide resistance. The CK group of C. suppressalis was used to determine the direct effect of antibiotics on pesticide tolerance using a mixture of antibiotics and pesticides. Indirect evidence suggests that antibiotics themselves did not affect the pesticide tolerance of C. suppressalis . The results confirmed that feeding C. suppressalis cefixime led to a decrease in the expression of potential tolerance genes to chlorantraniliprole. Conclusions This study reveals the impact of antibiotic induced changes in symbiotic microorganisms on the pesticide tolerance of C. suppressalis , laying the foundation for studying the interaction between C. suppressalis and microorganisms, and also providing new ideas for the prevention and control of C. suppressalis and the creation of new pesticides.

The transcriptional regulator Fur modulates the expression of uge, a gene essential for the core lipopolysaccharide biosynthesis in Klebsiella pneumoniae

Background Klebsiella pneumoniae is a Gram-negative pathogen that has become a threat to public health worldwide due to the emergence of hypervirulent and multidrug-resistant strains. Cell-surface components, such as polysaccharide capsules, fimbriae, and lipopolysaccharides (LPS), are among the major virulence factors for K. pneumoniae . One of the genes involved in LPS biosynthesis is the uge gene, which encodes the uridine diphosphate galacturonate 4-epimerase enzyme. Although essential for the LPS formation in K. pneumoniae , little is known about the mechanisms that regulate the expression of uge . Ferric uptake regulator (Fur) is an iron-responsive transcription factor that modulates the expression of capsular and fimbrial genes, but its role in LPS expression has not yet been identified. This work aimed to investigate the role of the Fur regulator in the expression of the K. pneumoniae uge gene and to determine whether the production of LPS by K. pneumoniae is modulated by the iron levels available to the bacterium. Results Using bioinformatic analyses, a Fur-binding site was identified on the promoter region of the uge gene; this binding site was validated experimentally through Fur Titration Assay (FURTA) and DNA Electrophoretic Mobility Shift Assay (EMSA) techniques. RT-qPCR analyses were used to evaluate the expression of uge according to the iron levels available to the bacterium. The iron-rich condition led to a down-regulation of uge , while the iron-restricted condition resulted in up-regulation. In addition, LPS was extracted and quantified on K. pneumoniae cells subjected to iron-replete and iron-limited conditions. The iron-limited condition increased the amount of LPS produced by K. pneumoniae . Finally, the expression levels of uge and the amount of the LPS were evaluated on a K. pneumoniae strain mutant for the fur gene. Compared to the wild-type, the strain with the fur gene knocked out presented a lower LPS amount and an unchanged expression of uge , regardless of the iron levels. Conclusions Here, we show that iron deprivation led the K. pneumoniae cells to produce higher amount of LPS and that the Fur regulator modulates the expression of uge , a gene essential for LPS biosynthesis. Thus, our results indicate that iron availability modulates the LPS biosynthesis in K. pneumoniae through a Fur-dependent mechanism.

Advancing Patient Advocacy in Mycology: Cultivating Collaboration in Education, Research, and Policy

In the healthcare landscape, diseases such as cancer and HIV/AIDS have benefited from the patient's perspective. For fungal diseases, the patient voice remains absent in critical areas such as policy formulation, funding decisions, and research priorities. Patients affected by fungal disease, along with their caregivers and advocacy groups, possess invaluable insights into the challenges and unmet needs they face. By elevating their voices and experiences, policymakers, funding agencies, and researchers can gain a more comprehensive understanding of the multifaceted nature of fungal disease and the urgency of addressing them. This paper addresses the pressing need for a coordinated effort to elevate the patient voice in advocating for improved policies, increased funding, and enhanced research initiatives regarding fungal disease.

Dermatophytic Dynamics: A Holistic Approach to Superficial Mycosis Research

Human fungus infections are a serious public health issue that needs a prompt and correct diagnosis to cure effectively. Human fungal infections have historically been treated with in-person consultations and exams by trained laboratory mycologists. Lately, deep learning models-based computer-aided diagnosis systems have become more potent instruments, especially for late-stage mycological diagnoses. This research presents experimental results using a deep learning technique to classify different species of fungi. The work is noteworthy for using a vision transformer, demonstrating its effectiveness in classifying fungi. This work contributes to the ongoing advancement of diagnostic techniques and provides a feasible route for artificial intelligence integration in mycology. The results show a remarkable 97.3% accuracy rate, underscoring the potential of deep learning models—especially vision transformers—to enhance the efficacy and accuracy of fungal infection diagnosis. This study contributes to the growing body of research on artificial intelligence and medical mycology. The vision transformer’s efficacy in classifying fungi confirms the transformative potential of deep learning in diagnostic methods.

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.

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.

Introduction to Fungi in Wastewater Treatment and Fundamentals of Mycology and Wastewater Chemistry

The rapid development of the industrial sector has contributed significantly to the global economy and the well-being of individuals, while it has consequences in terms of waste generation, consumption of resources, and energy usage, which require the development of ad hoc strategies and countermeasures to contain their impacts. Covered are microbial bioremediation, bioleaching, bioaugmentation, biostimulation, mycoremediation, and phytoremediation. Among these biological remediation approaches, mycoremediation is beneficial since it can successfully break down a wide variety of contaminants and its efficacy is not restricted to specific concentrations of pollutants. Mycoremediation is a cost-effective and environmentally benign technology that uses fungal biomass to remove hazardous chemicals from polluted water. Mushrooms are fungal fruiting structures that grow from a fibrous mass known as the mycelium. Macro fungi have demonstrated a unique capacity to break down a variety of dangerous and lignin-containing wastes produced by a variety of anthropogenic and natural activities. This chapter establishes approaches that could serve as a prospective framework for addressing the worldwide issue of wastewater.

Recent Advance: Fluorescein Stain

Fluorescein stain can be used efficiently and cost effective way for examining the raw ophthalmic specimens visualizing tissue details under microscopic examinations. Author(s) developed this staining method in various raw ophthalmic specimens to detect micro-organisms including parasites, corneal and conjunctival pathologies, lid, orbital pathologies and in enucleated eyeballs. The staining process is very simple and can be performed quickly spending minimum time. The staining also gave important informations about the retinal pigment epithelium, retina, intraocular cancer seedings etc. The novelty of the stain had wider applications in detecting fungus, viral inclusion bodies, parasites, immunological and tumor cells covering anatomy, pathology, onco-pathology, microbiology, parasitology, virology, immunology, entomology and so on.

Strain-dependent induction of primary bile acid 7-dehydroxylation by cholic acid

Background Bile acids (BAs) are steroid-derived molecules with important roles in digestion, the maintenance of host metabolism, and immunomodulation. Primary BAs are synthesized by the host, while secondary BAs are produced by the gut microbiome through transformation of the former. The regulation of microbial production of secondary BAs is not well understood, particularly the production of 7-dehydroxylated BAs, which are the most potent agonists for host BA receptors. The 7-dehydroxylation of cholic acid (CA) is well established and is linked to the expression of a bile acid-inducible ( bai ) operon responsible for this process. However, little to no 7-dehydroxylation has been reported for other host-derived BAs (e.g., chenodeoxycholic acid, CDCA or ursodeoxycholic acid, UDCA). Results Here, we demonstrate that the 7-dehydroxylation of CDCA and UDCA by the human isolate Clostridium scindens is induced when CA is present, suggesting that CA-dependent transcriptional regulation is required for substantial 7-dehydroxylation of these primary BAs. This is supported by the finding that UDCA alone does not promote expression of bai genes. CDCA upregulates expression of the bai genes but the expression is greater when CA is present. In contrast, the murine isolate Extibacter muris exhibits a distinct response; CA did not induce significant 7-dehydroxylation of primary BAs, whereas BA 7-dehydroxylation was promoted upon addition of germ-free mouse cecal content in vitro . However, E. muris was found to 7-dehydroxylate in vivo . Conclusions The distinct expression responses amongst strains indicate that bai genes are regulated differently. CA promoted bai operon gene expression and the 7-dehydroxylating activity in C. scindens strains. Conversely, the in vitro activity of E. muris was promoted only after the addition of cecal content and the isolate did not alter bai gene expression in response to CA. The accessory gene baiJ was only upregulated in the C. scindens ATCC 35704 strain, implying mechanistic differences amongst isolates. Interestingly, the human-derived C. scindens strains were also capable of 7-dehydroxylating murine bile acids (muricholic acids) to a limited extent. This study shows novel 7-dehydroxylation activity in vitro resulting from the presence of CA and suggests distinct bai gene expression across bacterial species.

Fungus in the Fur: An Overview of Fungal Infections in Cats, Dogs, and Exotic Small Mammals

Fungal infections occur in a wide variety of mammals including cats, dogs, and exotic small mammals. These infections are generally categorized as superficial/cutaneous, subcutaneous, and systemic. While most reported cases involve cats and dogs, fungal infections have also been documented in various exotic small mammal species. Although microbiological diagnostic approaches are similar across patient species, clinical signs and treatment strategies can vary significantly. Managing these infections in veterinary medicine presents unique challenges, particularly in exotic small mammals, due to species-specific differences in pathophysiology, treatment options, and husbandry considerations. In this chapter, we discuss (1) superficial/cutaneous, (2) subcutaneous, (3) systemic fungal infections in cats, dogs, and exotic small mammals, and (4) the challenges with managing these veterinary fungal infections.

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.

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.

Vaginal microbiome differences between patients with adenomyosis with different menstrual cycles and healthy controls

Background Adenomyosis is a commonly observed benign gynecological disease that affects the quality of life and social psychology of women of childbearing age. However, because of the unknown etiology and incidence of adenomyosis, its pathophysiological mechanism remains unclear; further, because no noninvasive, accurate, and individualized diagnostic methods are available, treatment and efficacy evaluations are limited. Notably, the interaction between the changes in the microecological environment of the female reproductive tract and human immunity, endocrine, and other links leads to the occurrence and development of diseases. In addition, the vaginal microbiome differs in different menstrual cycles; therefore, assessing the differences between the microbiomes of patients with adenomyosis and healthy individuals in different menstrual cycles will improve the understanding of the disease and provide references for the search for noninvasive diagnosis and individualized precision treatment of adenomyosis. This study aimed to explored the data of individuals in different menstrual cycles. Results Differences in the vaginal microbiome between patients with adenomyosis and healthy individuals were observed. At phylum level, the relative abundance of Firmicutes in the adenomyosis group was higher than that in the control group, which contributed the most to the species difference between the two groups. At the genus level, Lactobacillus was the most dominant in both groups, Alpha-diversity analysis showed significant differences in the adenomyosis and control group during luteal phase (Shannon index, p  = 0.0087; Simpson index, p  = 0.0056). Beta-diversity index was significantly different between the two groups ( p  = 0.018). However, based on Weighted Unifrac analysis, significant differences were only observed throughout the luteal phase ( p  =  0.0146 ). Within the adenomyosis group, differences between women with different menstrual cycles were also observed. Finally, 50 possible biomarkers including were screened and predicted based on the random forest analyse. Conclusions The vaginal microbiome of patients with adenomyosis and healthy individuals differed during menstrual periods, especially during the luteal phase. These findings facilitate the search for specific biological markers within a limited range and provide a more accurate, objective, and individualized diagnostic and therapeutic evaluation method for patients with adenomyosis, compared to what is currently available.

Veterinary Mycology: Challenges and Applications

Veterinary mycology is an important field of veterinary science that focuses on the identification, diagnosis, and treatment of infectious and zoonotic fungal diseases that pose risks to both animal and public health. The emergence of new and resistant fungal pathogens is influenced by human-animal-environmental interactions. In Southeast Asia, the environmental conditions with warm, humid climate, dense biodiversity, urbanization, and habitat disruption can influence the growth and transmission of these novel fungal pathogens. This chapter highlights the importance of fungal pathogens, zoonotic transmission, and the impact of environmental factors and climate change in the transmission and evolution of fungal diseases. It describes the growing concerns and challenges of antifungal resistance and the increasing cases of fungal diseases, especially those with zoonotic potential. In response to these challenges, this chapter reviews recent and current applications of advanced diagnostic techniques used for the timely identification of fungal pathogens, improving the accuracy and timeliness of disease reporting, treatment, and control. Development of antifungal drugs is also addressed in this chapter with more emphasis on the importance of a collaborative response, that is, a One Health approach involving veterinary, medical, and environmental health to foster more effective surveillance systems and intervention strategies. Moreover, this chapter explores the future outlook of interdisciplinary collaboration when it comes to fungal disease monitoring, antifungal drug development, and sustainable research funding to mitigate the growing threat of fungal diseases and strengthen animal, human, and environmental health.

Probiotic potential of lactic acid bacteria isolated from Ethiopian traditional fermented Cheka beverage

Introduction : Lactic acid bacteria (LAB) are a cluster of microbes distributed in a variety of environments and have potential probiotic activity to improve human well-being. This study was aimed at assessing the probiotic potential of LAB isolated from Cheka , an Ethiopian traditionally fermented beverage. Method Pure isolates obtained from 16 Cheka samples from Konso ( n  = 8) and Derashe ( n  = 8) were characterized morphologically, biochemically, and physiologically by considering basic criteria to identify the LAB. The probiotics properties of the LAB were evaluated in vitro at low pH values (2.0 and 3.0), and two bile salt concentrations (0.3 and 0.5%) for 3 and 6 h. The 16 S rRNA gene sequencing was done using an ABI 3730xl sequencer, and the gene sequences were aligned. Results Of the 27 pure isolates, 11 isolates were proven to be LAB with non-motile, negative for catalase, and non-spore former characteristics. Based on cultural characteristics and sugar fermentation ability, the 11 isolates were assembled into the genera Lactobacillus (55%), Lactococcus (18%), Pediococcus (18%), and Leuconostoc (9%). At pH 3.0 and a bile salt concentration of 0.3%, isolate ChK-11 showed a better survival rate (97 and 94%) than other isolates [ChK-7 (93 and 80%) > ChD-5 (84 and 76%) > ChD-8 (46 and 36%) > ChK-4 (41 and 34%)] for 6 and 3 h, respectively. According to 16 S rRNA sequencing results, isolates ChK-11 and ChK-7 were found to be Weissella paramesenteroides and Leuconostoc pseudomesenteroides with sequence similarity of 99 and 91%, respectively. Conclusions In the present study, probiotic LAB ( Weissella paramesenteroides and Leuconostoc pseudomesenteroides ) was successfully isolated and sequenced from Cheka samples. The findings of this in vitro study indicated that fermented beverages like Cheka are a source of the LAB with probiotic functional properties. Overall, Weissella paramesenteroides and Leuconostoc pseudomesenteroides isolates, which showed promising probiotic properties under in vitro conditions, can be used for starter culture development for the Cheka fermentation process.

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.

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 .

Fungi in Education: Reports from the Philippines

Fungi remain one of the most diverse organisms on the planet. Thus, the study of fungi, the science of mycology, is as essential as the other fields of biology. In the Philippines, teaching various aspects of fungal biology follows the teacher-centric lecture approach coupled with a practical and technical skills-based laboratory component. In this chapter, we present three higher education institutions in the country as case scenarios, each of which has its own unique implementation of teaching fungi. These case scenarios provide a context on how mycology is taught in the country and offer insights into innovative and inclusive learning activities. We also provided an example of technology-based learning activity in teaching fungi and called for more innovative and inclusive learning activities.

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.

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.

Sex Matters: Association with Superager Classification and Risk Factors

Superagers are 80 to 89-year-olds with average or better cognition and memory equivalent to individuals 20 to 30 years younger. As sex and modifiable lifestyle/health factors influence cognitive aging and dementia risk, we examined their impact on superager status. Data from participants (n = 469; 67% female) aged 80–89 years old were analyzed from an online database that included demographic and dementia risk factors, and performance on tasks assessing working memory, cognitive inhibition, associative memory, and set shifting. Cross-sectional comparisons were made between superagers and those with typical-for-age cognitive abilities (typical-agers) to examine relationships between sex, superager status, and dementia risk factors. Females performed better than age-matched males on the associative memory task in the 50–69 years old group used for normative comparisons, and in the 80–89 years old group (ps < .001). More females than males were classified as superagers using non-sex-stratified normative comparisons (p = .009), and in sex-stratified normative comparisons (p = .022). Total weighted dementia risk reduced odds of superager status (OR = 0.199, 95% CI [0.046, 0.829]). Other lifestyle dementia risk factors were unrelated to superager status or could not be tested due to low endorsement. The findings support observations that superaging is more common in females, even when controlling for sex differences in memory performance. Future studies of superagers should account for sex differences. Results support being ambitious about dementia prevention, as having fewer modifiable dementia risk factors may be positively associated with superager status.

A Systematic Review to Explore a Neuropsychological Profile that Predates Anorexia Nervosa

OBJECTIVE: Research demonstrates reduced cognitive flexibility and weak central coherence during acute illness and following recovery from anorexia nervosa (AN). This systematic review investigated if these impairments are present in first-degree relatives of individuals with AN, representing a possible neuropsychological risk profile. METHODS: A systematic review of electronic databases was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search ended on July 14, 2023. Established search terms and inclusion criteria identified relevant research. Risk of bias was assessed using the Critical Appraisal Skills Program. The review was registered with Prospero international prospective register of systematic reviews (No. CRD42023401268). Study selection, descriptive data, critical appraisal, and risk of bias are presented in tables and figures. RESULTS: The search yielded 10 studies. The included studies conducted neuropsychological assessments of discordant AN relatives and lifetime longitudinal study participants. Most studies found cognitive flexibility and central coherence to be significantly reduced in participants with AN and their relatives compared with controls. One study found decision making to be significantly impaired in AN participants and relatives. Effect sizes were moderate to large. DISCUSSION: Reduced cognitive flexibility and weak central coherence appear to be endophenotypes of AN. Further research is required with relatives concordant for AN to establish whether these biomarkers co-segregate with AN within families. These findings suggest a possibility of developing screeners to identify individuals at risk of AN allowing for early intervention.

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.

Neural Circuitries and Alcohol Use Disorder: Cutting Corners in the Cycle

An implicit tenet of the alcohol use disorder (AUD) research field is that knowledge of how alcohol interacts with the brain is critical to the development of an understanding of vulnerability to AUD and treatment approaches. Gaining this understanding requires the mapping of brain function critical to specific components of this heterogeneous disorder. Early approaches in humans and animal models focused on the determination of specific brain regions sensitive to alcohol action and their participation in AUD-relevant behaviors. Broadly speaking, this research has focused on three domains, Binge/Intoxication, Negative Affect/Withdrawal, and Preoccupation/Anticipation, with a number of regions identified as participating in each. With the generational advances in technologies that the field of neuroscience has undergone over the last two decades, this focus has shifted to a circuit-based analysis. A wealth of new data has sharpened the field’s focus on the specific roles of the interconnectivity of multiple brain regions in AUD and AUD-relevant behaviors, as well as demonstrating that the three major domains described above have much fuzzier edges than originally thought. In this chapter, we very briefly review brain regions previously implicated in aspects of AUD-relevant behavior from animal model research. Next, we move to a more in-depth overview of circuit-based approaches, and the utilization of these approaches in current AUD research.

Psychotherapies and Impact on Borderline Personality Disorder and Ensuing Biological Markers: A New Narrative

Borderline personality disorder is characterized by challenges in interpersonal relationships and self-image, impulsivity, and affective instability. Putative neurobiological mechanisms have deepened understanding of the pathophysiology of the disorder, yet challenges remain in the provision of effective treatment. Psychotherapy is the first-line treatment for BPD and though a range of evidence-based modalities are supported, rates of nonresponse are approximately 50%. One approach to improving treatment outcomes concerns research at the cross-disciplinary intersection of neurobiology and psychotherapy. A comprehensive overview of psychotherapy studies that have incorporated biological markers will be provided in this chapter, including neuroimaging, genetics, and neuroendocrine studies. Most studies have investigated task-related brain activation, mainly using affective paradigms that have implicated primarily frontotemporal and limbic regions. High between-study heterogeneity and an overall small number of studies, particularly genetics and neuroendocrine studies, preclude firm conclusions. Recent developments in the field and recommendations for future progress will be discussed.

Ketamine in Translational Neuroscience: A Brief Introduction

Systems neuroscience employs a network and topological perspective to elucidate the paradigm “brain.” The attribution of perception, cognition, consciousness, and behavior to findings obtained from multimodal physiological research, neuroimaging, and clinical neuromonitoring represents a significant aspect of the field within translational neuroscience. From a clinical perspective, there is great hope that advances in the treatment of severe neurofunctional disorders will be driven by the examination of the “big data” now available. Examples of clinical applications include the study of severe brain injury, coma, and (disorders of) consciousness. This brief introduction will delineate the fundamental principles of systems neuroscience, computational models, bioinformatics, and artificial intelligence as they pertain to the clinical use and investigation of ketamine. The meaning of this research for innovative therapeutic approaches and the importance of “real-world brain and body biology” within the strengths and weaknesses of translational systems neuroscience will be outlined.

Norepinephrine Neurons in the Nucleus of the Solitary Tract Suppress Luteinizing Hormone Secretion in Female Mice

Stress impairs fertility, at least in part, via inhibition of gonadotropin secretion. Luteinizing hormone (LH) is an important gonadotropin that is released in a pulsatile pattern in males and in females throughout the majority of the ovarian cycle. Several models of stress, including acute metabolic stress, suppress LH pulses via inhibition of neurons in the arcuate nucleus of the hypothalamus that coexpress kisspeptin, neurokinin B, and dynorphin (termed KNDy cells) which form the pulse generator. The mechanism for inhibition of KNDy neurons during stress, however, remains a significant outstanding question. Here, we investigated a population of catecholamine neurons in the nucleus of the solitary tract (NTS), marked by expression of the enzyme dopamine beta-hydroxylase (DBH), in female mice. First, we found that a subpopulation of DBH neurons in the NTS is activated (express c-Fos) during metabolic stress. Then, using chemogenetics, we determined that activation of these cells is sufficient to suppress LH pulses, augment corticosterone secretion, and induce sickness-like behavior. In subsequent studies, we identified evidence for suppression of KNDy cells (rather than downstream signaling pathways) and determined that the suppression of LH pulses was not dependent on the acute rise in glucocorticoids. Together these data support the hypothesis that DBH cells in the NTS are important for regulation of neuroendocrine and behavioral responses to stress.

Non-instructed Motor Skill Learning in Monkeys: Insights from Deep Reinforcement Learning Models

We employ Reinforcement Learning (RL) models to unravel the mechanisms behind the learning behavior of two macaque monkeys engaged in a free-moving multi-target reaching task. The study was conducted using computer simulations reflecting the animal’s learning conditions, and compared with the actual arm movements recorded on two macaque monkeys on a Kinarm apparatus. Our paper thus provides important insights for the design of motor control learning systems, combining end-effector control design with the learning of motor chunks associations. Our research is of interest for the modeling and understanding of natural motor learning systems, but also heads toward the design of more “brain-inspired” adaptive robotic manipulators.

The Emergence of a Complex Representation of Touch Through Interaction with a Robot

In this paper, we present a novel robot model of touch, and its representation in an artificial cortex, that aims to capture some of the complexity of human touch. In particular, our approach integrates artificial mechanoception and nociception in an adaptive sensory field (the robot’s “sensory body”), allowing for a more comprehensive simulation of tactile sensations. The robot’s sensory field is then processed by a biologically plausible neural network in a way akin to sensory processing in the somatosensory and anterior cingulate cortex. Findings from our experimental results show our model’s ability to integrate complex data from infrared sensors, leading to the emergence of a spatial sensory body representation in our neural network, with potentially significant implications for robot perception and interaction.

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.

Introduction to Neurophysiology and the Use of Psychoactive Substances

This chapter offers a detailed examination of the neurophysiological mechanisms underlying neural activity, emphasizing the roles of neurotransmitters, neuroplasticity, and the effects of psychoactive substances. It starts with a discussion of fundamental neuronal functions, including action potentials and synaptic transmission. The chapter then explores long-term potentiation (LTP) and long-term depression (LTD) as critical mechanisms of synaptic plasticity. It further investigates the involvement of neurotransmission systems in various brain circuits related to inhibitory control, decision-making, and reward processing. The impact of psychoactive substances on these systems is analyzed, highlighting changes in neurotransmitter balance and subsequent neuroplastic adaptations. Particular attention is given to the mesocorticolimbic pathway and its modulation by drugs of abuse, as well as the roles of glutamate and GABAergic systems. The chapter concludes by examining the effects of substance use on brain function and behavior, particularly how these alterations persist during withdrawal and influence overall neuroadaptation.

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.

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.

Research Methodologies for Studying the Neuroscience of Psychoactive Substance Use

Recent advancements in neuroimaging and genetic techniques have significantly transformed our understanding of the neurobiological mechanisms underlying substance use disorders (SUDs). Optogenetic and chemogenetic approaches, in conjunction with traditional neuroimaging methods, have offered unprecedented insights into the brain’s response to psychoactive substances. This chapter reviews the current state of knowledge concerning these techniques and their applications in addiction research, highlighting their contributions to elucidating drug-seeking behaviors, neural circuitry, and potential therapeutic strategies.

Sensorimotor Control

This chapter offers an overview of the neurobiology of human movement. The functions of specific areas and sites within the nervous system and how their integrative action ensures various aspects of movement will be described. A sequential approach will be adopted, commencing with the muscle and spinal cord, progressing through the brain stem and descending motor tracts, the motor cortex, and finally the basal ganglia and the cerebellum. While this may suggest serial processing, it is essential to recognize that the processing underlying human movements relies on parallel and integrated activities within and between the networks. The chapter concludes with a synthesis of the different elements and a description of how three critical movement behaviors are generated: reaching and grasping, balance and posture, and locomotion. Throughout the text, the implications of basic neurobiology in the context of neurorehabilitation will be emphasized.

Advanced AI Models for Emotion Recognition by Functional MRI: Fully Connected Neural Network

Functional magnetic resonance imaging (fMRI) allows for the temporal observation of human brain activity, providing insights into the activation patterns of various brain regions under different emotional states. Given the complexity of brain data, influenced by numerous factors, accurately predicting emotions using fMRI data presents a significant challenge. This study aims to develop an advanced artificial intelligence model for emotion recognition based on a comprehensive set of fMRI datasets. The dataset includes fMRI data from 20 participants engaged in emotion-inducing tasks, such as watching video clips, with emotional responses categorized into three classes and nine levels. The fMRI signals were segmented into 25-s intervals, capturing neural activity across 246 brain regions. To enhance signal quality and reduce noise, we employed a nonlinear least squares method, where each 25-s signal segment was modeled using a polynomial function. The polynomial parameters were optimized to minimize the sum of squared differences between observed and predicted values. Initially, a fully connected neural network (FCNN) model was trained to identify the brain regions whose activity patterns are most strongly correlated with emotional states. These identified regions were then selected for further training. Subsequently, the signals from the selected brain regions are concatenated for training multiple Fully Convolutional Neural Network (FCNN) models. An ensemble voting system is then employed to predict the unknown dataset, with the most frequently occurring prediction result being used as the final output. Our emotion recognition system achieved an accuracy of approximately 52–58% for class predictions and around 30% for level predictions. The model’s performance is further evidenced by a submission score of 0.5704. These findings underscore the strong association between brain fMRI data and emotions, demonstrating the potential of FCNNs in decoding emotional states from brain fMRI data. Despite challenges such as data heterogeneity and individual variability, the model’s evaluation on the ICBHI dataset indicates significant predictive capabilities, contributing to our understanding of the neural mechanisms of emotions.

Decreased Beta Power and OFC–STN Phase Synchronization during Reactive Stopping in Freely Behaving Rats

During natural behavior, an action often needs to be suddenly stopped in response to an unexpected sensory input—referred to as reactive stopping. Reactive stopping has been mostly investigated in humans, which led to hypotheses about the involvement of different brain structures, in particular the hyperdirect pathway. Here, we directly investigate the contribution and interaction of two key regions of the hyperdirect pathway, the orbitofrontal cortex (OFC) and subthalamic nucleus (STN), using dual-area, multielectrode recordings in male rats performing a stop-signal task. In this task, rats have to initiate movement to a go-signal, and occasionally stop their movement to the go-signal side after a stop-signal, presented at various stop-signal delays. Both the OFC and STN show near-simultaneous field potential reductions in the beta frequency range (12–30 Hz) compared with the period preceding the go-signal and the movement period. These transient reductions (∼200 ms) only happen during reactive stopping, which is when the stop-signal was received after action initiation, and are well timed after stop-signal onset and before the estimated time of stopping. Phase synchronization analysis also showed a transient attenuation of synchronization between the OFC and STN in the beta range during reactive stopping. The present results provide the first direct quantification of local neural oscillatory activity in the OFC and STN and interareal synchronization specifically timed during reactive stopping.

EEG Theta/Beta Ratio Variability in Relation to Attachment Style in the Context of Secretary Problem

This study examines the Theta/Beta Ratio (TBR) in EEG measurements to infer the cognitive load during decision-making across different attachment styles. Participants’ EEGs were analyzed within the secretary problem framework while they were at rest and making decisions. The secure attachment group demonstrated higher TBR difference medians, suggesting a lower cognitive load. Conversely, the fearfully avoidant group exhibited significantly lower medians, indicating a heightened cognitive load. The anxiously attached group’s TBR varied widely, suggesting inconsistent cognitive engagement, while the avoidant group’s levels were moderate. These results imply that attachment style determines the cognitive load experienced during decision-making, with fearfully avoidant individuals bearing the highest load. This research elucidates the neurobiological links between attachment styles and cognitive processing.

Brain-Based Learning: Support Critical Thinking with Brain-Based Learning Strategies

Brain-based learning aligns educational strategies with the brain’s natural processes for absorbing, retaining, and applying knowledge. The purpose of this chapter is to explain the neurophysiology of learning and provide practical strategies that work with, rather than against, the brain’s natural learning mechanisms. When nursing students understand why certain approaches are more effective—like spaced repetition, chunking information, and active reflection—they are able to embrace these methods in their learning. The relationship between brain-based learning and critical thinking is explored through practical examples of how nursing students can apply these concepts in classroom, clinical, and simulation settings. Case studies demonstrate how understanding concepts like neurotransmitter function, memory formation, and the role of sleep in learning helps students optimize their study habits and learning techniques. Interactive learning activities and chapter synthesis exercises help students apply and integrate key concepts.

The Neural and Genetic Basis of Romantic Love

In recent decades, human and animal studies have made strides in advancing knowledge about the neural and genetic basis of attachment—the selective preference for specific individuals, which is characterized by proximity-seeking, care, and the investment of time and resources. Pair-bonding is one instantiation of attachment, but it is unique for its role in reproduction (with genetic implications) and biparental care of offspring. Unique to humans, and sometimes coinciding with pair-bonding, is the complex phenomenon of romantic love, a basic human motivation involving an intense desire to be united with the beloved physically, emotionally, and mentally. Increasingly, scientists have examined the biological basis of romantic love, providing a deeper understanding of the mechanisms underlying attachment, pair-bonding, and its nature. In this chapter, we provide an overview of animal models detailing the biological mechanisms of pair-bonding. Next, we review research on the neural and genetic basis of pair-bonding and romantic love in humans. Finally, we comment on future directions of this work and elucidate how the neural and genetic processes discussed herein influence the expression and function of this basic human drive that we call love.

Arachidonic Acid Mobilization and Peroxidation Promote Microglial Dysfunction in Aβ Pathology

Aberrant increase of arachidonic acid (ARA) has long been implicated in the pathology of Alzheimer's disease (AD), while the underlying causal mechanism remains unclear. In this study, we revealed a link between ARA mobilization and microglial dysfunction in Aβ pathology. Lipidomic analysis of primary microglia from App(NL-GF) mice showed a marked increase in free ARA and lysophospholipids (LPLs) along with a decrease in ARA-containing phospholipids, suggesting increased ARA release from phospholipids (PLs). To manipulate ARA-containing PLs in microglia, we genetically deleted lysophosphatidylcholine acyltransferase 3 (Lpcat3), the main enzyme catalyzing the incorporation of ARA into PLs. Loss of microglial Lpcat3 reduced the levels of ARA-containing PLs, free ARA and LPLs, leading to a compensatory increase in monounsaturated fatty acid (MUFA)-containing PLs in both male and female App(NL-GF) mice. Notably, the reduction of ARA in microglia significantly ameliorated oxidative stress and inflammatory responses while enhancing the phagocytosis of Aβ plaques and promoting the compaction of Aβ deposits. Mechanistically, scRNA seq suggested that LPCAT3 deficiency facilitates phagocytosis by facilitating de novo lipid synthesis while protecting microglia from oxidative damage. Collectively, our study reveals a novel mechanistic link between ARA mobilization and microglial dysfunction in AD. Lowering brain ARA levels through pharmacological or dietary interventions may be a potential therapeutic strategy to slow down AD progression.

Mesoscale Calcium (Ca^2+) Imaging in Freely Behaving Mice

Mesoscale imaging of calcium dynamics has emerged as a powerful technique to study the coordination of activity of multiple cortical regions simultaneously during awake behavior. While much of the work in the field has focused on studying cortical dynamics during head-fixed preparations, we have recently developed the mini-mScope—a head mounted widefield imaging microscope capable of imaging large swathes of the dorsal cortex of freely behaving mice. In this chapter, we guide the reader with methodological details to build and use the mini-mScope for widefield Ca^2+ imaging in freely behaving mice performing complex behaviors such as spatial navigation.

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.

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.

Development of a Screening Tool for Assessing Sexual Difficulties Among Patients with Parkinson's Disease: The PD-SDS.

International audience; People with Parkinson's disease (PwPD) exhibit various sexual difficulties (SDs) that may be due to motor and/or nonmotor symptoms or the use of antiparkinsonian medication. SDs are often underreported by PwPD and underexplored by physicians. This study aimed to explore the SDs experienced by PwPD and create a scale for assessing them. A corpus of items was generated from semistructured interviews to represent the experience of PwPD as closely as possible. The number of items was reduced according to the psychometric properties, and the scale's structure was subsequently examined. The final phase consisted of measuring the scale's validity and reliability. After assessment of the original corpus of 59 items by PwPD and clinicians, a 25-item version was obtained. The analysis of item properties led to the removal of fifteen items. An exploratory factor analysis of the first 10-item version with a first PwPD sample identified four components of the SDs among PwPD: "low sexual esteem," "sexual displeasure," "impact on sexual position" and "hypersexuality." With a second PwPD sample, a confirmatory factor analysis demonstrated a satisfactory fit between the model with four components and the data. The 10-item scale had good internal consistency and good temporal reliability. The Parkinson's Disease Sexual Difficulties Scale (PD-SDS) is a valid screening tool that facilitates the investigation of and communication about PD-related SDs. It is intended to improve the identification of vulnerable PwPD and to target the domain of sexual experience impacted by PD to better support PwPD.

Neuropsychiatric Features, Health-Related Quality of Life, and Caregiver Burden in Parkinson's Disease

AIM: Parkinson's disease (PD) is a progressive neurodegenerative disease and significantly impacts patients and their caregivers. The current study aims at recognizing its neuropsychiatric symptoms, its impact on the health-related quality of life (HRQOL) of the patients, and the caregiver burden in a middle- to-low-income country. METHODS: We conducted a cross-sectional survey of 73 idiopathic Parkinson's disease (IPD) patients and their caregivers from January 2021 to June 2021. Neuropsychiatric Inventory (NPI-12) and Parkinson's disease questionnaire (PDQ-39) were used to assess patients' symptoms and HRQOL, respectively. We used the Zarit caregiver burden interview (ZBI) and Hamilton depression scale (Ham-D) for the caregiver's burden assessment. RESULTS: Of the 73 patients, 43 (59%) were men, and 30 (41%) were women. Their mean age was 60.25 years (± 11.1), and the mean duration of PD was 6.4 years (± 3.4). Eighty-six percent of the patients reported having one or more neuropsychiatric symptoms. HRQOL, as indicated by PDQ-39, correlated most significantly with H and Y staging (r = 0.680, P < 0.001) of the disease. Sixty-eight percent of the caregivers felt a disease burden, and 55% had depression. On regression analysis, NPI total score on caregiver burden (beta = 0.883, P < 0.001, confidence interval [CI] of 1.087 to 1.400,) and H and Y staging on depression (beta = 0.772, P < 0.001, [CI of 0.629 to 0.934) were having the most decisive impact. CONCLUSION: Our study showed the presence of frequent neuropsychiatric symptoms in PD patients. It has a detrimental effect on the quality of life of patients and results in a significant increase in caregiver burden and depression among them.

Spontaneous Graft‐Induced Dyskinesias Are Independent of 5‐HT Neurons and Levodopa Priming in a Model of Parkinson's Disease

BACKGROUND: The risk of graft‐induced dyskinesias (GIDs) presents a major challenge in progressing cell transplantation as a therapy for Parkinson's disease. Current theories implicate the presence of grafted serotonin neurons, hotspots of dopamine release, neuroinflammation and established levodopa‐induced dyskinesia. OBJECTIVE: To elucidate the mechanisms of GIDs. METHODS: Neonatally desensitized, dopamine denervated rats received intrastriatal grafts of human embryonic stem cells (hESCs) differentiated into either ventral midbrain dopaminergic progenitor (vmDA) (n = 15) or ventral forebrain cells (n = 14). RESULTS: Of the eight rats with surviving grafts, two vmDA rats developed chronic spontaneous GIDs, which were observed at 30 weeks post‐transplantation. GIDs were inhibited by D(2)‐like receptor antagonists and not affected by 5‐HT1A/1B/5‐HT6 agonists/antagonists. Grafts in GID rats showed more microglial activation and lacked serotonin neurons. CONCLUSIONS: These findings argue against current thinking that rats do not develop spontaneous GID and that serotonin neurons are causative, rather indicating that GID can be induced in rats by hESC‐derived dopamine grafts and, critically, can occur independently of both previous levodopa exposure and grafted serotonin neurons. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Prédire l'évolution de la maladie de Parkinson à l'aide de données cliniques et d'IRM fonctionnelles: reproduction et robustesse d'une étude

Ce travail a gagné le prix du meilleur poster en imagerie médicale, sponsorisé par Guerbet, à la conférence IABM 2024.;National audience; La maladie de Parkinson est une pathologie neurodégénérative fréquente pour laquelle il n’existe pas de biomarqueurs établis pour le diagnostic des stades précoces et la prédiction de la progression de la maladie [1]. Récemment, plusieurs études ont exploré des biomarqueurs de neuro-imagerie avec des techniques d’apprentissage automatique [2]. Néanmoins, ces études sont sensibles à plusieurs sources de variabilité, y compris celles introduites par différentes méthodes d'analyse [3], [4], rendant essentiel l’évaluation de leur robustesse. Nous avons tenté de reproduire (mêmes données, même méthode) et de répliquer (données ou méthode différentes) les modèles décrits dans [5] pour prédire l'état actuel et la progression de la maladie de Parkinson. Nous avons utilisé les données de la base de données Parkinson's Progression Markers Initiative [6] (PPMI), pour reproduire la cohorte, les mesures de neuro-imagerie (fALFF-ReHo) et les modèles d'apprentissage automatique grâce aux informations disponibles dans l'article et le code. Des variations méthodologiques ont aussi été étudiées, notamment pour la sélection de la cohorte, le traitement d’image et les variables utilisées (Figure-1). Les performances obtenues sont supérieures au hasard pour tous nos modèles, mais restent différentes de celles rapportées dans l'étude originale (Figure-2). Les difficultés rencontrées lors de cette reproduction s'expliquent notamment par la complexité des études de neuro-imagerie, en particulier en milieu clinique. Certaines variations ont mené à des performances plus proches de celles de l’étude originale pour certaines prédictions, mais pour d'autres, celles-ci étaient beaucoup plus faibles, suggérant la faible taille de l’effet détecté et la faible puissance de ces modèles.

Community‐based high‐intensity cycling improves disease symptoms in individuals with Parkinson's disease: A six‐month pragmatic observational study

Participation in supervised, laboratory‐based aerobic exercise protocols holds promise in slowing the progression of Parkinson's disease (PD). Gaps remain regarding exercise adherence and effectiveness of laboratory protocols translated to community‐based programs. The aim of the project was to monitor exercise behaviour and evaluate its effect on disease progression over a 6 month period in people with PD participating in a community‐based Pedalling for Parkinson's (PFP) cycling program. A pragmatic, observational study design was utilised to monitor exercise behaviour at five community sites. The Movement Disorders Society‐Unified Parkinson's disease Rating Scale Motor III (MDS‐UPDRS‐III) and other motor and non‐motor outcomes were gathered at enrollment and following 6 months of exercise. Attendance, heart rate, and cadence data were collected for each exercise session. On average, people with PD (N = 41) attended nearly 65% of the offered PFP classes. Average percent of age‐estimated maximum heart rate was 69.3 ± 11.9%; average cadence was 74.9 ± 9.0 rpms. The MDS‐UPDRS III significantly decreased over the 6‐month exercise period (37.2 ± 11.7 to 33.8 ± 11.7, p = 0.001) and immediate recall significantly improved (42.3 ± 12.4 to 47.1 ± 12.7, p = 0.02). Other motor and non‐motor metrics did not exhibit significant improvement. Participants who attended ~74% or more of available PFP classes experienced the greatest improvement in MDS‐UPDRS III scores; of those who attended less than 74% of classes, cycling greater than or equal to 76 rpms lead to  improvement. Attendance and exercise intensity data indicated that a laboratory‐based exercise protocol can be successfully translated to a community setting. Consistent attendance and pedalling at a relatively high cadence may be key variables to PD symptom mitigation. Improvement in clinical ratings coupled with lack of motor and non‐motor symptom progression over 6 months provides rationale for further investigation of the real‐world, disease‐modifying potential of aerobic exercise for people with PD.

Self-normalized Classification of Parkinson's Disease DaTscan Images

Classifying SPECT images requires a preprocessing step which normalizes the images using a normalization region. The choice of the normalization region is not standard, and using different normalization regions introduces normalization region-dependent variability. This paper mathematically analyzes the effect of the normalization region to show that normalized-classification is exactly equivalent to a subspace separation of the half rays of the images under multiplicative equivalence. Using this geometry, a new self-normalized classification strategy is proposed. This strategy eliminates the normalizing region altogether. The theory is used to classify DaTscan images of 365 Parkinson's disease (PD) subjects and 208 healthy control (HC) subjects from the Parkinson's Progression Marker Initiative (PPMI). The theory is also used to understand PD progression from baseline to year 4. ;Comment: To appear in IEEE BIBM 2021

Predictive risk factors of phenoconversion in idiopathic REM sleep behavior disorder: the Italian study “FARPRESTO”

Most patients with idiopathic REM sleep behavior disorder (iRBD) will develop an overt α-synucleinopathy over time, with a rate of phenoconversion of 73.5% after 12 years from diagnosis. Several markers of phenoconversion were identified; however, most studies investigated biomarkers separately, with retrospective study designs, in small cohorts or without standardized data collection methods. The risk FActoRs PREdictive of phenoconversion in idiopathic REM sleep behavior disorder: the Italian STudy (FARPRESTO) is a multicentric longitudinal retrospective and prospective study with a cohort of incident (prospective recruitment) and prevalent (retrospective recruitment) iRBD patients, whose primary aim is to stratify the risk of phenoconversion, through the systematic collection by means of electronic case report forms of different biomarkers. Secondary aims are to (1) describe the sociodemographic and clinical characteristics of patients with iRBD; (2) collect longitudinal data about the development of α-synucleinopathies; (3) monitor the impact of iRBD on quality of life and sleep quality; (4) assess the correlation between phenoconversion, cognitive performance, and loss of normal muscle atony during REM sleep; (5) identify RBD phenotypes through evaluating clinical, biological, neurophysiological, neuropsychological, and imaging biomarkers; and (6) validate vPSG criteria for RBD diagnosis. The FARPRESTO study will collect a large and harmonized dataset, assessing the role of different biomarkers providing a unique opportunity for a holistic, multidimensional, and personalized approach to iRBD, with several possible application and impact at different levels, from basic to clinical research, and from prevention to management. The FARPRESTO has been registered at clinicaltrials.gov (NCT05262543).

Integrative Brain States Facilitate the Expression of Parkinson's Tremor