Alzheimer's Disease

Atrazine Induces Alzheimer's Disease-Like Neurotoxicity by Targeting SDHB and Disrupting Synaptic Function: An Integrated Bioinformatic and In Vivo Study.

Atrazine (ATR) is a globally prevalent herbicide linked to increased risks of cognitive impairment and neurodegeneration, but its molecular mechanism remains unclear. This study integrates bioinformatics, machine learning, and experimental validation to elucidate ATR induced Alzheimer's disease (AD)-like pathology. We combined predicted ATR targets with AD brain transcriptomics, identifying 32 sha...

Comparative evaluation of automated MRI-based brain volumetry software for estimating cognitive domains in early Alzheimer's disease.

BACKGROUND: Brain volumetry software is widely accepted for assessment in Alzheimer's disease. However, direct comparisons for software-specific prediction capabilities across cognitive domains have not been reported. This study evaluates the performance of four brain volumetry software programs in predicting domain-specific cognitive scores in patients with early Alzheimer's disease (AD) using a ...

MINERVA: a public XAI-powered platform advancing multi-target discovery in Alzheimer's disease.

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder involving a complex interplay of interconnected pharmacological targets, a fea...

Exploring Neurological Disorder Therapeutics: The Progress and Future Prospects of Proteins and Peptides Derived from Blue Foods.

INTRODUCTION: Neurological illnesses continue to be a major global health challenge and require novel and safe therapeutic strategies. Blue foods like...

Tetrapeptide inhibitors of BACE-1 revealed by combined data-driven screening and physics-based free-energy refinement.

Alzheimer's disease remains a major global health challenge, and β-site amyloid precursor protein cleaving enzyme 1 (BACE-1) represents a critical the...

Modeling Dyadic Interdependence in Endocrine Functioning: A Multilevel Machine Learning Study of Adults with Cancer and Their Caregivers.

OBJECTIVE: This study evaluated dyadic endocrine interdependence among cancer patients and their spousal caregivers using a machine learning (ML) fram...

[Fall prediction models in older adults with dementia: a rapid review].

INTRODUCTION: Falls in older adults with dementia are common and have multiple consequences for their health and quality of life. Fall prediction mode...

Adversarial attacks on a multimodal Alzheimer's disease detection system reveal complex interdependences between heterogeneous modalities.

Multi-modal models that fuse neuroimaging with clinical assessment data represent the current state of the art for automated Alzheimer's disease detec...

A review of Alzheimer's disease diagnosis and prognosis models based on multimodal deep learning.

Although some drugs have been approved for clinical treatment, early diagnosis and intervention remain the most effective strategies for managing Alzh...

Beyond complex architectures: a streamlined CNN pipeline for robust Alzheimer's disease classification from brain MRI.

BACKGROUND AND PURPOSE: Alzheimer's disease, a common type of dementia, gradually steals memories and impacts daily life as brain cells deteriorate. W...

Explainable foundation model for dementia screening and risk stratification using retinal fundus images.

Dementia is a growing global health challenge, and early identification is essential for timely intervention. We evaluated whether foundation model-ba...

Exploring Informal Caregivers' Perception of the Olera Digital Caregiving Assistance Platform for Dementia Care: Mixed Methods Evaluation Study.

BACKGROUND: Informal caregivers of people living with dementia often experience high rates of caregiver burnout while providing care. Although there a...

Real-Time Smartphone Monitoring Assessments as a Cognitive Biomarker of Alzheimer Disease: Protocol for a Development Study.

BACKGROUND: The diagnosis and monitoring of Alzheimer disease (AD) currently rely on clinician-administered, in-person, and cross-sectional pen-and-pa...

Serum integrative omics reveals predictive signatures for coronary artery disease.

Identifying serum biomarkers that accurately reflect the progression of coronary artery disease (CAD) remains a major challenge. Integrative proteomic...

Multi-omics and metabolic flux modeling reveal that macrophage CYP27A1-derived 26-hydroxycholesterol drives immunosuppression and cancer malignancy.

Emerging evidence indicates that aberrant cholesterol metabolism in macrophages plays a role in tumorigenesis, however, how specific metabolites relea...

Starmate: A Lightweight AI Assistant for Autism Caregivers Developed and Evaluated Through a User-Centered Mixed-Methods Framework.

Autism spectrum disorder (ASD) affects tens of millions of families worldwide, yet parents confront abundant but unreliable online advice and limited ...

CausalTCC: causal temporal contrastive learning for automated Alzheimer's disease biomarker discovery with bio-electrical signals.

OBJECTIVE: Learning robust representations from scarce labeled bio-electrical time-series data remains a critical challenge in clinical diagnosis. Whi...

Variations of global brain asymmetry are associated with aging and related diseases.

Lateralization is a hallmark of brain organization, yet the structural basis underlying this phenomenon remains a critical, unresolved question in cog...

Readability and Linguistic Characteristics of Alzheimer's Disease and Related Dementias Prevention, Symptom, and Treatment Information from Generative Artificial Intelligence Chatbots.

PURPOSE: To examine the readability and linguistic characteristics of Alzheimer's disease and related dementias (ADRD) prevention, symptom, and treatm...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...