Alzheimer's Disease

Connected-speech digital biomarkers for monitoring transcranial pulse stimulation in Alzheimer's disease: A pilot study.

BackgroundAlzheimer's disease (AD) lacks effective disease-modifying therapies and scalable, ecologically valid biomarkers to monitor treatment response. Transcranial pulse stimulation (TPS) is an emerging non-invasive neuromodulation technique with potential to attenuate cognitive decline. Sensitive digital endpoints are needed to quantify intervention-related changes.ObjectiveTo develop and vali...

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades. The recent advent of disease modifying therapies is a welcome development; however, it is also now apparent that early treatment maximizes the benefits of these drugs. Therefore, it is important to develop reliable methods of disease detection, pref...

Speech-based machine learning for detecting neuropsychiatric symptoms in Alzheimer's disease.

BackgroundNeuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet their detection relies on su...

Machine-Learning-Assisted Triple-Gated Raman Enhancement Platform for Selectively Quantifying Lysophosphatidylcholine (16:0) as a Potential Biomarker for Cognitive Impairments.

Accurate quantification of structurally similar metabolites as biomarkers in biofluids has remained a longstanding challenge. Here, we report a semico...

Integrative Transcriptomic, Network, and Machine Learning Analyses Identify Genistein and Resveratrol-Associated Therapeutic Targets in Alzheimer's Disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by complex molecular alterations across multiple brain regions. ...

Evaluating the biological meaning of neural network decisions in EEG-based MCI detection.

OBJECTIVE: High accuracy in medical classification tasks does not ensure that neural networks reason in ways consistent with clinical or neurobiologic...

KGT: Knowledge-guided graph transformer for neurodegenerative disease diagnosis and brain age prediction with MRI.

Deep learning methods have significantly advanced the analysis of brain imaging data for various downstream tasks such as disease diagnosis and age pr...

Unraveling 'F' factor: towards a genetic-clinical framework for the musculoskeletal-heart crosstalk in metabolic aging.

BACKGROUND: The rising co-occurrence of cardiometabolic diseases and musculoskeletal degeneration poses a critical challenge to healthy aging, yet the...

A Cross-Disease Microglial Transcriptional Program Characterizes Neurodegeneration and Highlights SPP1 as a Biomarker.

Microglial cells are key players in maintaining brain homeostasis and responding to pathological conditions. Their multifaceted roles in health and di...

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 n...

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. DE...

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 ...

Spatially resolved single cell analysis suggests an APOE NCF1 associated immunosuppressive niche and its prognostic signature in thyroid cancer.

BACKGROUND: Thyroid carcinoma (TC) presents a rising global incidence, with a subset of cases progressing aggressively despite standard therapies. The...

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...

Enhydrin from yacon attenuates atherosclerosis by modulating the FABP5/PPARγ/ABCA1 axis: An integrated multi-omics and in vivo validation.

Dysregulated lipid metabolism drives atherosclerosis (AS). Yacon, an Andean lipid-modulating tuber, exerts anti-AS potential, but mechanisms remain un...

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...