Dementia

AI-Driven discovery of brain-penetrant mTOR-independent autophagy enhancers for Alzheimer's disease.

Current Alzheimer's disease therapies offer limited efficacy and are often accompanied by significant side effects, underscoring the urgent need for new treatment strategies. Enhancing autophagy represents a promising therapeutic approach, yet most known autophagy inducers act through the mTOR-dependent pathway, which broadly affects cellular metabolism and proliferation, and their clinical potent...

Development of Machine Learning-Driven Dual-Task Gait Test Model for Cognitive Impairment Screening.

OBJECTIVE: To develop an artificial intelligence (AI)-aided dual-task gait test model for scalable, high-throughput cognitive impairment screening. DESIGN: A diagnostic case-control study between 2022 and 2023, community-dwelling adults aged ≥60 years capable of unaided walking was enrolled. SETTING(S): Primary care. PARTICIPANTS: 201 participants were classified into 3 groups: dementia (n=26), mi...

A century of coffee and tea research in cognitive health and Alzheimer's disease: Structural, thematic, and translational insights (1911-2025).

BackgroundAlthough studies have explored tea and coffee in relation to Alzheimer's disease, no century-scale analysis has jointly examined both within...

Multimodal machine learning for early risk stratification of post-stroke cognitive impairment.

BackgroundPost-stroke cognitive impairment (PSCI) is a major vascular contributor to dementia, significantly impacting long-term recovery and quality ...

Common biomarkers of Alzheimer disease and postoperative delirium.

Alzheimer disease (AD) and Postoperative delirium (POD) may share a common mechanism, but their shared genes and potential novel therapeutic targets r...

Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.

Despite robust preclinical evidence, many clinical trials, including several that targeted the purinergic system, fail to demonstrate efficacy in huma...

Explainable machine learning for the prediction of Alzheimer's disease-related cognitive impairment: a consensus feature selection approach.

BACKGROUND: Early identification of Alzheimer's disease-related cognitive impairment remains challenging, and existing machine learning (ML) models of...

EEG biomarkers can predict early-stage Alzheimer's disease and correlate with intracerebral pathology: a multimodal machine learning study.

BACKGROUND: Early recognition of Alzheimer's disease (AD) is crucial for timely intervention and delaying disease progression. Electroencephalogram (E...

Advancing Alzheimer Disease Prediction With Large Language Model-Based Linguistic Feature Analysis: Development and Validation Study.

BACKGROUND: Alzheimer disease (AD) is a progressive neurodegenerative disorder with rapidly growing global prevalence. Early detection is critical for...

Turning Compliance Into Action: An Augmented Intelligence-Enabled Framework for Advocacy Organizations to Drive Population Health Improvement.

Community Health Needs Assessments (CHNAs), mandated by the Affordable Care Act for tax-exempt hospitals, represent an underutilized yet rich data sou...

The emerging role of oligodendrocytes in Alzheimer's disease: Integrating bibliometric insights with molecular pathogenesis.

BackgroundOligodendrocytes (OLs) have received relatively limited attention in Alzheimer's disease (AD) research; however, recent studies highlight th...

Peripheral immune and vascular inflammatory signatures precede Alzheimer's disease onset: A serum proteomic study.

BackgroundNeuroinflammation plays an important role in the pathogenesis of Alzheimer's disease, but systemic immune alterations preceding clinical ons...

Reliability and readability of AI platforms for pediatric health advice: a comparative analysis.

Large language models (LLMs) are increasingly used as a first-line source of information for everyday questions, including pediatric health guidance, ...

Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis.

BACKGROUND: Aspiration pneumonia represents a significant but understudied cause of morbidity and mortality, particularly among aging populations with...

Gray matter brain age predicts cognitive outcome one year after ischemic stroke.

BackgroundPredicting post-stroke cognitive impairment (PSCI) remains challenging.ObjectiveThis study validated two brain age metrics-Gray Matter Brain...

A Voyage on Computer Aided Intelligent Algorithms for the Segmentation of Brain Tissues for Neurodisorder Diagnosis.

Neurodisorders pose a considerable burden to global health, frequently requiring early treatment and diagnosis to avoid irreversible cognitive and mot...

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Glaucoma shares similarities with neurodegenerative conditions like dementia, Parkinson's disease, and ischaemic optic neuropathy, which affect ocular...

Targeting Tau-Mitochondrial Crosstalk in Alzheimer's Disease: Integrative Multi-Omics and Artificial Intelligence-Driven Tools for the Development of Disease-Modifying Therapeutics.

Alzheimer's disease (AD) is a progressive neurodegenerative illness marked by cognitive impairment, synaptic dysfunction and neuronal death. Tau prote...

Identification and Validation of Glycosylation-Related Biomarkers in the Hippocampus for Alzheimer's Disease Diagnosis and Drug Repurposing.

As a crucial type of post-translational modification, glycosylation plays a fundamental role in maintaining cellular homeostasis and is closely associ...

EEG microstate dynamics and metal metabolism in Parkinson's disease dementia: an exploratory study with machine learning-assisted sensitivity analysis.

BACKGROUND: Electroencephalogram (EEG) microstates effectively characterise cognitive-related brain networks, and metal homeostasis is crucial for mai...