Alzheimer's Disease

Integrative machine learning approach to risk prediction for dementia and Alzheimer's disease.

Dementia, particularly Alzheimer's disease (AD), presents a growing global health challenge characterized by cognitive decline, behavioral changes, and loss of independence. With increasing life expectancy, early diagnosis and improved clinical strategies are urgently needed. This study developed and evaluated machine learning (ML) models to predict AD risk using UK Biobank data, integrating healt...

AlzhiNet: Traversing from 2D-CNN to 3D-CNN, Towards Early Detection and Diagnosis of Alzheimer's Disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the ageing population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework, AlzhiNet, that integrates both 2D convolutional neural networks (2D-CNNs) and 3D convolutional neural networks (3D-CNNs), along with...

Proteomic signature of dementia risk in type 2 diabetes.

INTRODUCTION: Type 2 diabetes (T2D) significantly increases dementia risk, yet the molecular mechanisms underlying this association remain unclear. OB...

Six promising drug repurposing candidates for Alzheimer's disease and their sex-specific mechanisms and efficacy.

Alzheimer's disease is a neurodegenerative disorder that leads to progressive memory loss, cognitive decline, and behavioral changes. Despite ongoing ...

Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson's Disease Through Longitudinal Data-Driven Clustering.

Background: Although primarily characterised as a motor disorder, Parkinson's Disease (PD) also presents with non-motor symptoms, including cognitive ...

Fingernail-based metabolomics reveals a stepwise decline in dodecanoic acid associated with Alzheimer's disease progression.

BACKGROUND: Fingernail metabolomics provides a novel, non-invasive platform that captures long-term biochemical fluctuations for identifying reliable ...

Integrating Time and Frequency Domain Features of fMRI Time Series for Alzheimer's Disease Classification Using Graph Neural Networks.

Accurate and early diagnosis of Alzheimer's Disease (AD) is crucial for timely interventions and treatment advancement. Functional Magnetic Resonance ...

AI fragment based optimization of saffron and chamomile phytochemicals as aryl hydrocarbon receptor inhibitors for dementia therapy an integrated computational approach.

Dementia represents a rapidly rising global health challenge as a progressive neurodegenerative disease with few options for disease-modifyingtreatmen...

Biomarkers and therapeutic strategies targeting microglia in neurodegenerative diseases: current status and future directions.

Recent advances in our understanding of non-cell-autonomous mechanisms in neurodegenerative diseases (NDDs) have highlighted microglial dysfunction as...

Rapid Fluorescence Lifetime Imaging through One-Dimensional Deep Learning Optimization.

Traditional fluorescence lifetime imaging (FLIM) provides valuable quantitative insights for biomedical and molecular biology research, but often reli...

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clini...

Deep ensemble learning with transformer models for enhanced Alzheimer's disease detection.

The progression of Alzheimer's disease is relentless, leading to a worsening of mental faculties over time. Currently, there is no remedy for this ill...

Enhancing automated detection and classification of dementia in individuals with cognitive impairment using artificial intelligence techniques.

Dementia is a degenerative and chronic disorder, increasingly prevalent among older adults, posing significant challenges in providing appropriate car...

Structural and pragmatic language skills in school-age children relate to resting state functional connectivity.

Language difficulties are common in school-age children but their etiology is often unknown. Although neural underpinnings of language have been well-...

Speech production as an artificial intelligence-based 'process' measure of cognition sensitive to mild cognitive impairment and Alzheimer's disease.

Process scores in neuropsychological tests add incremental validity for detecting non-normative cognitive aging trajectories. However, process scores...

EEG Signal Analysis for the Classification of Alzheimer's and Frontotemporal Dementia: A Novel Approach Using Artificial Neural Networks and Cross-Entropy Techniques.

Dementia, a neurological disorder, can cause cognitive decline due to damage to the brain. Our study aims to contribute to the development of computer...

Virtual Hydrolysis-Based Screening of Wheat-Derived DPP-IV Inhibitory Peptides: A Mechanistic Analysis Integrating Cell Experiments and Molecular Dynamics Simulations.

Dipeptidyl peptidase-IV (DPP-IV) inhibitors play a critical role in the treatment of diabetes and metabolic diseases. This study combines computationa...

Enhanced particle swarm optimization for feature selection in SVM-based Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder marked by neuronal loss, leading to cognitive and behavioral decline. With the ag...

Biomimetic Analysis of Neurotransmitters for Disease Diagnosis through Light-Driven Nanozyme Sensor Array and Machine Learning.

Neurological diseases, including Alzheimer's disease, Parkinson's disease, and multiple sclerosis, pose a significant global health challenge due to t...

Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis.

BACKGROUND: Delays in the diagnosis of transthyretin amyloid cardiomyopathy (ATTR-CM) contribute to the significant morbidity of the condition, especi...