Alzheimer's Disease

Association between leukocyte telomere length and neurodegenerative diseases: a prospective cohort in the UK Biobank.

BACKGROUND: This study aims to investigate the association between leukocyte telomere length (LTL) and the risk of incident NDDs using a large-scale cohort from the UK Biobank. METHODS: Data from 459,902 subjects were analyzed using Cox proportional hazards models and machine learning (ML) algorithms to assess LTL's association with NDD risk. RESULTS: Shorter LTL was associated with an increased r...

Shortening the MacArthur-Bates Communicative Developmental Inventory Using Machine Learning Based Computerized Adaptive Testing (ML-CAT).

Early identification of infants and toddlers at risk for developmental disorders can improve the efficiency of early intervention programs and can reduce healthcare costs. The MacArthur-Bates Communicative Development Inventory (MB-CDI) is a standardized tool for assessing children's early lexical development. However, due to its long list of words, administration is time-consuming and often limit...

A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.

Alzheimer's disease (AD) requires the discovery of new therapeutic targets, but traditional molecular docking methods for virtual screening are often ...

Long-Term Caregivers' Perceptions and Expectations Toward the Use of Nursing Robots: A Meta-Synthesis of Qualitative Research.

This study aims to synthesize the perceptions and expectations of long-term caregivers regarding the use of nursing robots to inform strategies for en...

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 di...

A Multiobjective Evolutionary Algorithm Based on Bipopulation With Uniform Sampling for Neural Architecture Search.

Neural architecture search (NAS) automates neural network design, improving efficiency over manual approaches. However, efficiently discovering high-p...

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 ...

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...

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...

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...

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...

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 improved activation function for the recognition of knee osteoarthritis severity.

BACKGROUND: Existing knee osteoarthritis (KOA) severity classification methods typically rely on a combination of object detection algorithms and clas...

Probabilistic mapping and automated segmentation of human brainstem white matter bundles.

Brainstem white matter (WM) bundles are essential conduits for neural signals that modulate homeostasis and consciousness. Their architecture forms th...

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...

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...

Spatial amyloid-informed multimodal brain age as an early marker of Alzheimer's-related vulnerability and risk stratification.

BACKGROUND: Brain age gap (BAG)-the difference between predicted and chronological age-captures neurobiological aging, but MRI-only models insufficien...

Implementation of artificial intelligence in palliative and supportive care for people with cancer: A scoping review.

BACKGROUND: Cancer remains a leading global health burden. Artificial intelligence offers new opportunities to address complex physical and psychologi...

Comparative analysis of supervised and ensemble models with unsupervised exploration for alzheimer's disease prediction.

Alzheimer's disease is a progressive neurodegenerative disorder characterized by memory loss and cognitive decline, with no known cure. Early detectio...