Geriatrics

Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer’s disease and aging

Alzheimer’s disease (AD) genome-wide association studies (GWAS), typically based on clinical phenotypes, have identified numerous risk loci, yet linking these variants to brain changes and molecular processes remains challenging. We developed a DNE-xQTL framework integrating deep learning-derived dimensional neuroimaging endophenotypes (DNEs) with comprehensive brain molecular quantitative trait l...

A Large-Scale Serum Metabolite Panel for Baseline Detection of Alzheimer’s Disease

Blood-based metabolomic signatures offer promising, non-invasive avenues for Alzheimer’s disease (AD) detection. We aimed to identify a serum metabolite panel integrated with APOE ε4 status for distinguishing AD from cognitively normal (CN) individuals. Baseline data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) were analyzed for 594 participants (237 AD, 357 CN). High-resolution ser...

Large Language Model-Driven Prioritization of Alzheimer’s Disease Drug Targets Across Multidimensional Criteria

Large language models (LLMs) offer new opportunities to synthesize the vast and heterogeneous biomedical literature, yet their potential to support dr...

Physical Activity Shapes Brain Structure, Function, and the Computational Mechanisms of Cognitive Control

Sedentarism is prevalent and associated with poorer mental and physical health. Whether everyday physical activity (PA) maps onto computational decisi...

Machine Learning for Longitudinal Brain-Age Prediction

Cross-sectional brain age models have demonstrated high accuracy and reliability for predicting chronological age based on structural brain features d...

Data Extraction from Oncology Imaging Reports by Large Language Models: A Comparative Accuracy Study

Manual data extraction from clinical text is resource intensive. Locally hosted large language models (LLMs) may offer a privacy-preserving solution, ...

Relationship of the Microbiome with Neurodegenerative Diseases: Development of AI Tools to Detect Alzheimer’s

This study aimed to develop an artificial intelligence (AI) algorithm capable of distinguishing Alzheimer’s disease (AD) from healthy patients using g...

Environmental Chemicals as Modifiers of the Association between Age and Ovarian Reserve

To evaluate whether the well-established age-related reduction in antral follicle counts (AFC) is greater among women with higher concentrations of en...

Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment

Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...

A foundation model for mapping the phenomic and genetic landscape of cerebral small vessel disease biomarkers

Cerebral small vessel disease (CSVD) is a leading cause of age-related cognitive decline and neurological disorders, yet its precise characterization ...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...

Performance of a Chinese Cognitive Decline Risk Model in a Japanese Cohort: A Validation Study

To develop a simple risk prediction model for cognitive decline in a Chinese older adult cohort, and to evaluate its performance and transportability ...

Explainable Machine Learning for Early Detection of Mild Cognitive Impairment, Fall Risk, and Frailty Using Sensor-Based Motor Function Data

This study aimed to design and evaluate an explainable machine learning (ML) framework that integrates sensor-based motor assessments with demographic...

Mapping Neurochemical Signatures onto Brain Structure for Neurotransmitter-Informed Discrimination of Schizophrenia Patients from Healthy Controls

Schizophrenia (SCZ) is associated with widespread gray matter volume (GMV) reductions, yet the underlying mechanisms driving these alterations remain ...

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity Metadata

Alzheimer’s disease (AD) exhibits profound spatial heterogeneity in its molecular and pathological features, yet the basis of this regional selectivit...

Drug-drug interaction identification using large language models

Drug-drug interactions (DDIs) are a significant source of morbidity and adverse drug events (ADEs), particularly in situations of polypharmacy and com...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Multiscale-Multistage Temporal Convolutional Network for EEG-Based Mild Cognitive Impairment Detection

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia, with affected individuals at a higher risk of progressing...

CLIN-SUMM: Temporal Summarization of Longitudinal Clinical Notes

Electronic health records (EHRs) contain years of longitudinal clinical notes that capture evolving patient health, treatments, and outcomes. However,...

Brain Region-Centered MultiModal Hypergraph Fusion for MCI Conversion Prediction

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder, with mild cognitive impairment (MCI) as its prodromal stage. Accurate MCI conver...