Alzheimer's Disease

MIA and CD163 as promising diagnostic biomarkers in vascular dementia: A multi-method study combining WGCNA, machine learning with validation in animal models and clinical samples.

Vascular dementia (VaD), the second most common form of dementia, lacks reliable biomarkers for early diagnosis. Here, we integrated weighted gene co-expression network analysis (WGCNA) with machine learning to identify novel biomarkers and immune-metabolic pathways in VaD. Analysis of the GSE122063 dataset revealed 288 differentially expressed genes (DEGs), with four hub genes (MIA, CD163, OPALIN...

Hybrid Transformer for Early Alzheimer's Detection: Integration of Handwriting-Based 2D Images and 1D Signal Features.

Alzheimer's Disease (AD) is a prevalent neurodegenerative condition where early detection is vital. Handwriting, often affected early in AD, offers a non-invasive and cost-effective way to capture subtle motor changes. State-of-the-art research on handwriting, mostly online, based AD detection has predominantly relied on manually extracted features, fed as input to shallow machine learning models....

Design of a Mobile App and a Clinical Trial Management System for Cognitive Health and Dementia Risk Reduction: User-Centered Design Approach.

BACKGROUND: The rising prevalence of dementia is a major concern, with approximately 45% of cases linked to 14 modifiable risk factors. The European p...

Machine learning-assisted optimization of dietary intervention against dementia risk.

A healthy diet has been associated with a reduced risk of dementia. Here we devised a Machine learning-assisted Optimizing Dietary intERvention agains...

A deep learning model for early diagnosis of alzheimer's disease combined with 3D CNN and video Swin transformer.

Alzheimer's disease (AD) constitutes a neurodegenerative disorder predominantly observed in the geriatric population. If AD can be diagnosed early, bo...

A dual path graph neural network framework for dementia diagnosis.

Dementia typically results from damage to neural pathways and the consequent degeneration of neuronal connections. Graph neural networks (GNNs) have b...

A multi-modal graph-based framework for Alzheimer's disease detection.

We propose a compositional graph-based Machine Learning (ML) framework for Alzheimer's disease (AD) detection that constructs complex ML predictors fr...

Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging.

Early diagnosis of Neurological Disorders (ND) such as Alzheimer's disease (AD) and Brain Tumors (BT) can be highly challenging since these diseases c...

Classifying and diagnosing Alzheimer's disease with deep learning using 6735 brain MRI images.

Traditional diagnostic methods for Alzheimer's disease often suffer from low accuracy and lengthy processing times, delaying crucial interventions and...

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health chall...

Developments in MRI radiomics research for vascular cognitive impairment.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following p...

Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.

The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achi...

Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy.

Neurodegenerative diseases (NDDs), including Alzheimer's disease (AD) and Parkinson's disease (PD), are a growing global health concern, especially am...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical...

Trees vs neural networks for enhancing tau lepton real-time selection in proton-proton collisions.

This paper introduces supervised learning techniques for real-time selection (triggering) of hadronically decaying tau leptons in proton-proton collid...

Potential role of TNFRSF12A in linking glioblastoma and alzheimer's disease via shared tumour suppressor pathways.

Tumor suppressor genes (TSGs) are critical regulators of cellular homeostasis and are extensively studied in cancer biology. However, their roles in n...

Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.

Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using ...

Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.

Alzheimer's Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is...

Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions.

BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems, presenting opportunities to improve clinical outcomes. In t...

An intelligent screener for mild cognitive impairment via integrated eye-tracking and the digital clock drawing test.

BackgroundMild cognitive impairment (MCI) is a risk factor for dementia, and early screening is crucial for patient prognosis.ObjectiveTo construct an...