Alzheimer's Disease

SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease.

Given the unclear pathogenesis and insidious progression of Alzheimer's disease (AD), the aim of the present study was to identify reliable diagnostic markers for AD detection using a combination of bioinformatics analysis, animal experiments and clinical patient validation. Gene expression profiles were retrieved from the GSE95587 dataset. Weighted gene co‑expression network analysis combined wit...

Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary prevention and targeted trial enrollment, yet remains challenging at disease onset. We investigated whether automated speech analysis could serve as a digital biomarker for early etiological stratification across clinically heterogeneous presentations. MET...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twof...

Machine Learning-Driven Ensemble Screening of Multitarget Kinase Inhibitors for Tauopathy-Associated Neurodegeneration Using All-Atom and Steered MD Simulations.

Tauopathies arise when normal functions of the tau protein in axonal transport and neuronal maintenance are disrupted by an imbalance between kinases ...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...

The association between PM2.5 components and cognitive decline: the impact of measurement error correction.

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may accelerate cognitive decline and increase dementia risk, but the roles ...

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.

Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves m...

MAISNet: A Multi-Species Integrated Graph Neural Network for Acetylcholinesterase Inhibitor Screening.

MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as alzheimer's disease (AD). Although existing dru...

Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated...

Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

OBJECTIVES: Neuropsychological (NP) tests are multi-domain in execution. Reliance on a single score representing specific domains obscures the detecti...

Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.

Dementia, which refers to disorders related to human memory, significantly affects the human brain, and a person with it can experience certain diffic...

Automated Imaging Differentiation for Dementia: Including Alzheimer Disease Dementia and Dementia with Lewy Bodies.

BACKGROUND AND OBJECTIVES: Differentiation of Alzheimer's disease dementia (ADD) and dementia with Lewy bodies (DLB) remains a challenge. Free-water i...

Responsible AI for Predicting Delayed Hospital Discharge Among Older Adults: Development and Evaluation Study for Balancing Accuracy, Equity, and Explainability.

BACKGROUND: Amid growing demands and constrained health care resources, effective hospital bed capacity management is crucial. Delayed hospital discha...

Using Machine Learning to Improve Screening for Oropharyngeal Dysphagia in Hospitalized Versus Primary Care Adult Patients With COVID-19 Disease: Retrospective Observational Study.

BACKGROUND: Oropharyngeal dysphagia (OD) commonly occurs in patients with COVID-19 disease, posing diagnostic challenges due to isolation protocols. O...

Data-driven modelling of tau pathology reveals distinct progressive supranuclear palsy subtypes.

Progressive supranuclear palsy (PSP) is a heterogeneous neurodegenerative disease characterised by the accumulation of misfolded 4-repeat tau within n...

Bridging Trials and Real Life in Fixed-Duration BTKi-Venetoclax for CLL: A Delphi-Enhanced Synthesis Incorporating Artificial Intelligence (AI) Benchmarks.

BACKGROUND: Optimal patient selection for the most effective BTK inhibitor (BTKi) partner of venetoclax in fixed-duration (FD) BTKi-venetoclax regimen...

Explainable machine learning for predicting mechanical ventilation in Alzheimer's patients with pneumonia: a SHAP-guided XGBoost nomogram based on MIMIC-IV.

Alzheimer's disease (AD) patients are particularly vulnerable to pneumonia and subsequent respiratory failure due to neurodegeneration-induced dysphag...

Learning to take it personally: Precision drug repurposing through patient-specific loss on knowledge graphs using Biobank data.

OBJECTIVE: Precision medicine requires drug repurposing methods that adapt to individual patient profiles while working within regulatory frameworks. ...

Geo-Mamba: Geometry-informed state-space learning of functional brain organization.

Functional magnetic resonance imaging (fMRI) derived functional connectivity (FC) is represented as graphs and as correlation or covariance matrices t...