Geriatrics

Unsupervised Disentanglement of Brain Heterogeneity for Identifying Subtypes of Alzheimer's Disease.

Neuroanatomical heterogeneity in Alzheimer's disease (AD) hinders precision diagnosis and treatment, as distinct brain phenotypes may correspond to different disease subtypes. However, MRI-based subtype classifications are often confounded by co-occurring pathologies and non-AD factors, such as genetic predisposition and environmental influences, limiting their clinical interpretability. We propos...

Understanding patient experience during Lokomat rehabilitation in children and adolescents: a clinical observational study combining self-evaluation and physiological metrics.

OBJECTIVES: To examine the emotional, cognitive and dispositional experience of children and adolescents undergoing Lokomat rehabilitation by integrating self-evaluation, therapist observations and physiological metrics across repeated sessions, with the aim of characterising how patient experience evolves throughout paediatric robot-assisted gait training. DESIGN: Prospective observational study ...

Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.

Alzheimer's disease (AD), a type of neurodegenerative disorder, has seen an increase in cases over the past decade, necessitating the construction of ...

Machine learning evaluation of the discriminative ability of Castelli Risk Index-I and other non-traditional lipid indices for sarcopenia: a cross-sectional study based on CHARLS.

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

Elucidating the toxicological impact and mechanism of plasticizers exposure on Alzheimer's disease through network toxicology and molecular docking.

This study aimed to explore the underlying mechanisms and key targets of widely used plasticizers, including dimethyl phthalate (DMP), diethyl phthala...

Prenatal alcohol exposure and the retrosplenial cortex: Structural and functional mechanisms across species.

The retrosplenial cortex (RSC) is a structurally and functionally complex hub with broad connectivity, integrating sensory inputs, cognitive states, a...

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Un...

Reactive Neural Network Potential Developed for Asphalt Aging Systems Through Active Learning and Enhanced Sampling.

The atomic-scale mechanisms of asphalt oxidative aging remain poorly understood due to the chemical complexity of asphalt and limitations of conventio...

Research on fall prediction in elderly patients with chronic diseases based on explainable machine learning: an aging perspective.

Integrating standard and native spaces for radiomics and brain network analysis in Alzheimer's disease diagnosis and prognosis.

INTRODUCTION: Structural MRI analysis for Alzheimer's disease (AD) is limited by balancing group-level comparability in standard space with anatomical...

Glymphatic dysfunction in trigeminal neuralgia: A multimodal MRI study.

BACKGROUND: Trigeminal neuralgia (TN) has traditionally been attributed to peripheral neurovascular compression at the trigeminal nerve root entry zon...

[Key Drivers of PM2.5 in Qingdao from 2013 to 2023 Using an Interpretable Machine Learning Model].

To elucidate the deceleration trend in PM2.5 concentration reduction and its formation mechanisms， this study analyzed PM2.5 concentration variations ...

CGLK-GNN : A connectome generation network with large kernels for GNN based Alzheimer's disease analysis.

Alzheimer's disease (AD) is a currently incurable neurodegenerative disease, with early detection representing a high research priority. AD is charact...

Factors Associated with Machine Learning-Based Predictions of Retinal Aging Using Teleretinal Screening Images from Patients with Diabetes.

PURPOSE: To identify factors associated with accelerated retinal aging based on machine learning predictions of age using fundus images from teleretin...

AI-driven biomarker learning for the early diagnosis of neurodegenerative diseases: ABLEDx.

BACKGROUND: Tears are an easily accessible biofluid that reflects both emotional states and disease conditions. They are particularly enriched in extr...

An interpretable machine learning framework with data-informed imaging biomarkers for diagnosis and prediction of Alzheimer's disease.

Machine learning methods based on imaging and other clinical data have shown great potential for improving the early and accurate diagnosis of Alzheim...

From wrinkles to malignancy: small-molecule-mediated stem cell approaches in skin aging.

Skin aging is driven by the progressive exhaustion of stem cell niches, epigenetic drift, and accumulation of senescent cells, which together promote ...

Gut microbiota-derived metabolites as immune modulators in aging and age-related chronic inflammatory diseases.

Age-associated dysbiosis, marked by shifts in the composition of gut microbiota and gut microbiota-derived metabolites (GMDMs), is increasingly implic...

LEFF-ViT: A locally enhanced vision transformer framework for accurate Alzheimer's Disease classification from brain MRI.

Early and accurate diagnosis of Alzheimer's Disease (AD) is critical for effective disease management and progression delay. Researches have been done...