Alzheimer's Disease

The effect of robot-based interventions on depression in older adults with cognitive impairment: A systematic review and meta-analysis.

OBJECTIVE: To assess the effectiveness of robot-based interventions in improving depressive symptoms...

The utility of Speech and Language analytics for screening Alzheimer's Disease.

BACKGROUND: Effective screening and cohort enrichment remain major challenges in clinical trials for...

Popular Online Content as a Treatment-as-Usual Control in Digital Mental Health Intervention Trials: Secondary Analysis of Two Online Randomized Controlled Trials With Repeated Measures.

BACKGROUND: Treatment-as-usual (TAU) conditions are intended to reflect the support typically receiv...

Simultaneous Profiling and Quantification of Tau and Its Phosphorylated Isomers Using Engineered FraC Nanopores.

Tau aggregation and hyperphosphorylation are key pathological hallmarks and early diagnostic biomark...

SSiamese Capsule Network (SNNCap) : Cognitive Analysis for Alzheimer's Disease Classification from MRI Data.

Alzheimer's Disease (AD) detection is essential for timely treatment and better patient care. Magnet...

Literature-derived serum miRNA signatures associated with cognitive decline in Alzheimer's disease: integrated analysis and machine learning-based diagnostic modeling.

Deciphering the molecular network of Trichostatin A in regulating Alzheimer's disease screening of core genes and mechanistic investigation based on multidimensional bioinformatics and molecular simulation.

BACKGROUND: Histone deacetylases (HDACs) regulate neuroprotection; however, Trichostatin A (TSA), an...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesse...

Predicting future cognitive impairment in preclinical Alzheimer's disease using amyloid PET and MRI: A multisite machine learning study.

Predicting the likelihood of developing Alzheimer's disease (AD) dementia in at-risk individuals is ...

Advancing brain age estimation: normative deviation mapping (NDM) for sensitive detection of pathological aging.

Brain age is a valuable neuroimaging-based biomarker for assessing brain health, typically estimated...

Causal inference in real-world dementia research: a systematic review protocol.

BACKGROUND: Dementia presents complex challenges for causal inference due to its multifactorial aeti...

NeuroBACE-ML: A reliability-aware screening framework for high-throughput prioritization of potent BACE1 inhibitors.

Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a key enzyme in amyloid-β generatio...

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...

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 degenera...

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...

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 maintena...

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 challe...

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 ...

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 magnet...

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 ...

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 m...