Alzheimer's Disease

Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), and chronic traumatic encephalopathy, yet the underlying molecular mechanisms in repetitive TBI remain poorly defined. This study investigates proteomic and behavioral changes following single and repetitive mild...

Graph_RG: Dominating CASP16's Small Molecule Affinity Prediction Subcategory-A Pose-Free Framework for Billion-Scale Virtual Screening.

Protein-ligand interaction prediction is pivotal in early-stage drug development, enabling large-scale virtual screening, drug optimization, and reverse target searching. In this work, we present Graph_RG, our top-performing model in the CASP16 small molecule track's protein-ligand affinity prediction category, achieving a N-weighted Kendall's Tau of 0.42-significantly outperforming other submissi...

A New Insight in Cellular and Molecular Signaling Regulation for Neural Differentiation Program.

Numerous neurological conditions impact the brain, spinal cord, and nerves, including neurodegenerative diseases such as Alzheimer's and Parkinson's d...

Neural networks and econometric models: Advancing brain connectivity for Alzheimer's drug development.

The Rapid Online Cognitive Assessment for the Detection of Neurocognitive Disorder: Open-Label Study.

BACKGROUND: The rising prevalence of dementia necessitates a scalable solution to cognitive screening. Paper-based cognitive screening examinations ar...

Explainable AI predicting Alzheimer's disease with latent multimodal deep neural networks.

PURPOSE: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. We proposed a novel latent multimoda...

Perspectives and Experiences of Family Caregivers Using Supportive Mobile Apps in Dementia Care: Meta-Synthesis of Qualitative Research.

BACKGROUND: Supportive mobile apps are effective tools for family caregivers of persons with dementia to obtain online information and psychological s...

Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.

As digital biomarkers gain traction in Alzheimer's disease (AD) diagnosis, understanding recent advancements is crucial. This review conducts a biblio...

Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.

OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the...

Optimizing Dementia Diagnosis Through Distance-Correlation Feature Space and Dimensionality Reduction.

The reduction of dimensionality in machine learning and artificial intelligence problems constitutes a pivotal element in the simplification of models...

Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, w...

An ensemble-based 3D residual network for the classification of Alzheimer's disease.

Alzheimer's disease (AD) is a common type of dementia, with mild cognitive impairment (MCI) being a key precursor. Early MCI diagnosis is crucial for ...

Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence.

Reliable classification of cognitive states in longitudinal Alzheimer's Disease (AD) studies is critical for early diagnosis and intervention. However...

Early Alzheimer's Detection Through Voice Analysis: Harnessing Locally Deployable LLMs via , a privacy-preserving diagnostic system.

Diagnosing Alzheimer's Disease (AD) early and cost-effectively is crucial. Recent advancements in Large Language Models (LLMs) like ChatGPT have made ...

SLR: A Modified Logistic Regression Model with Sinkhorn Divergence for Alzheimer's Disease Classification.

Logistic regression is a widely used model in machine learning, particularly as a baseline for binary classification tasks due to its simplicity, effe...

An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates.

Mitophagy, the selective autophagic elimination of mitochondria, is essential for maintaining mitochondrial quality and cell homeostasis. Impairment o...

Dynamically weighted graph neural network for detection of early mild cognitive impairment.

Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects the elderly population. The early detection of mild cognitive...

Towards Real-World Dementia Care: Encouraging Activity Engagement Using an Intelligent Humanoid Robot.

Humanoid social assistive robotics (SARs) can promote activity engagement for older adults living with dementia. Few studies, however, evaluate the ca...

Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability.

BACKGROUND: Mild Cognitive Impairment (MCI) is a critical transitional stage between normal aging and Alzheimer's disease, and its early identificatio...

Role of astroglia and microglia in Alzheimer's disease and multiple therapeutic interventions.

Alzheimer's disease (AD) is characterized by deposition of amyloid-β (Aβ) and neurofibrillary tangles (NFTs) formed by aggregates of hyperphosphorylat...