Geriatrics

Discrete Wavelet Convolution for Learnable Time-Frequency Representation with Application to Seizure Prediction.

Accurate and adaptive time-frequency representation is essential for analyzing nonstationary signals in critical applications, such as epileptic seizure prediction utilizing electroencephalogram (EEG) data. However, existing deep learning approaches often suffer from two major limitations: the reliance on fixed, nonadaptive feature extraction methods and the lack of model interpretability. To brid...

Metabolomic signatures of brain aging: A multimodal and genetic study.

Accelerated brain aging is increasingly recognized as a transdiagnostic risk factor for neuropsychiatric and neurodegenerative disorders, yet its metabolic underpinnings remain poorly understood. Here we integrated multimodal neuroimaging (MRI), plasma metabolomics, and genomic data from the UK Biobank to identify metabolic markers of brain aging and evaluate their causal relevance. Using 1079 ima...

Automation in the IVF laboratory in 2026: four technology domains, an asymmetric evidence base, and the case for parallel development with clinician-side automation.

BACKGROUND: Operator-dependent laboratory tasks-embryo selection, vitrification and warming, and intracytoplasmic sperm injection (ICSI)-have been the...

6PPD-quinone exposure and Alzheimer's disease: insights from integrative network pharmacology, transcriptomics, machine learning, and molecular docking.

OBJECTIVES: To systematically investigate the molecular associations between 6PPD-quinone (6PPD-Q), an environmental transformation product of the tir...

FEDI-CODE: A federated and causally-informed framework for dementia risk prediction using multi-site patient data.

Early detection of dementia is critical for timely intervention and disease management, yet it remains a challenging task due to the fragmented nature...

Identification of key predictors of postmenopausal osteoporosis from routine clinical indicators using explainable machine learning.

BACKGROUND: Machine learning (ML) shows promise in using clinical data to predict chronic diseases. However, its application in PMOP risk assessment u...

Frailty index and type 2 diabetes with renal complications: insights from Mendelian randomization and retrospective observational study.

OBJECTIVES: To investigate the relationship between frailty and diabetic kidney disease (DKD)-related renal complications using Mendelian randomizatio...

Retinal Vessel Traits and the Risk of Stroke: The Canadian Longitudinal Study on Aging.

PURPOSE: Retinal vascular changes could serve as an early, easily visible indicator of cerebrovascular health. Prior research on retinal vessel traits...

Preoperative considerations for nonoperating room anesthesia.

PURPOSE OF THE REVIEW: This review aims to address the unique challenges in nonoperating room anesthesia (NORA) locations, emphasizing the importance ...

Combining Network Pharmacology, Machine Learning, Molecular Docking, and Experimental Validation to Explore the Mechanism of Danggui-Shaoyao-San in treating Rheumatoid arthritis.

BACKGROUND: Danggui-Shaoyao-San (DSS) demonstrates clinical efficacy in rheumatoid arthritis (RA), but its bioactive constituents and molecular mechan...

Are patient-derived models of amyotrophic lateral sclerosis a game changer for novel drug discovery?

INTRODUCTION: ALS drug discovery has long depended on model systems that incompletely capture human disease heterogeneity, aging, and TDP-43 proteinop...

Accuracy of Key Sonographic Markers for Juxta-Articular Fractures: A Prospective Study Using Explainable Machine Learning.

OBJECTIVES: To systematically evaluate sonographic features of long bone juxta-articular fractures and identify key diagnostic predictors using machin...

Machine learning derived abdominal aortic calcification is associated with physical frailty in community-dwelling adults: the UK Biobank Imaging Study.

Clinical cardiovascular disease (CVD) is often present in frail individuals. However, it remains unclear whether subclinical CVD, e.g., abdominal aort...

Noninvasive Electrophysiological Biomarkers of Olfactory Responses Across Cognitive States in Alzheimer Dementia: Cross-Sectional Study.

BACKGROUND: Alzheimer disease (AD) is characterized by progressive cognitive decline, with olfactory dysfunction emerging among its earliest symptoms....

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

Edge AI holds great potential for extending the use of artificial neural networks to resource-constrained edge devices, such as microcontrollers. Desp...

Early prediction and risk assessment of adverse drug combinations using ensemble learning.

Modern day healthcare has seen an increase in polypharmacy, which is the prescription of multiple drugs as medication to treat illnesses simultaneousl...

Applying ensemble machine learning techniques to MRI scans to predict Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder and the leading cause of dementia globally. Early prediction, prior to the onset ...

Bobcat-optimized hybrid quantum-classical spike-driven network for MRI-based Alzheimer's stage prediction.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely affects memory, cognition, and behavioral functions, making early a...

Efficacy and Hepatotoxicity of Psoralea corylifolia L.: A Meta-Analysis and Machine Learning Assessment in Postmenopausal Osteoporosis.

ETHNOPHARMACOLOGICAL RELEVANCE: Psoralea corylifolia L. (P. corylifolia, also known as Cullen corylifolium (L.) Medik.) is a traditional medicinal her...

Alzheimer's disease risk prediction from clinical and social determinants of health: a machine learning cohort study in UK Biobank.

OBJECTIVES: Social determinants of health (SDOH) may improve Alzheimer's disease (AD) risk prediction by capturing upstream contextual risk beyond rou...