Alzheimer's Disease

HIF3A-mediated aberrant activation of TXNIP promotes Alzheimer's disease progression.

Oxidative stress (OS) is a hallmark of Alzheimer's disease (AD), yet the cell type-specific mechanisms remain unclear. We analyzed a single-cell RNA sequencing (scRNA-seq) dataset to assess OS-related features in AD. OS scores were calculated using multiple algorithms, and differential expression, enrichment, protein-protein interaction (PPI) network, and machine learning (ML) approaches were appl...

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study, we quantified amyloid-beta (Aβ) deposits, evaluating multiple brain regions across institutions, and evaluated associations with clinical, demographic, and genetic factors in persons pathologically diagnosed with AD. All linear models were adjusted ...

Correction to "Design of Tau Aggregation Inhibitors Using Iterative Machine Learning and a Polymorph-Specific Brain-Seeded Fibril Amplification Assay".

EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. Electroencephalography (EEG), a non-invasive, cost-ef...

Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.

Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.

Artificial intelligence (AI) is transforming biomarker discovery in neurology by overcoming key limitations of conventional approaches that are often ...

2.5D HAU-Net with gated spatial attention for automatic hippocampus segmentation in MRI.

BACKGROUND: The hippocampus is a key brain region and biomarker for Alzheimer's disease (AD). Accurate automated hippocampal segmentation is essential...

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment ...

Machine Learning-Integrated Pharmacophore, DFT Analysis, and molecular dynamics of Diosmetin as a potent ache inhibitor with neuroprotective activity in a Scopolamine-Induced alzheimer's zebrafish model.

Alzheimer's Disease (AD) is a degenerative disorder of the brain that causes a gradual loss of cognitive function. The cholinergic hypothesis suggests...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...

AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.

BACKGROUND: Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, ...

Leveraging Naturalistic Driving Digital Biomarkers for Early Mild Cognitive Impairment Detection: Deep Learning Strategies.

BACKGROUND: Alzheimer disease and related dementias are increasing worldwide, with early detection during the mild cognitive impairment (MCI) stage cr...

Digital support systems to improve child development in Peru: A cluster-randomized controlled open-label trial.

Digital technologies have the potential to transform early childhood development (ECD) interventions by delivering personalized support at scale. We c...

MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions...

Integrating ensemble machine-learning and fibril docking to discover potent, novel triazole-naphthalene tau-aggregation inhibitors.

Tau-protein aggregation is a central pathological feature of Alzheimer's disease, so blocking fibril growth is an attractive therapeutic goal. We cura...

Generative Artificial Intelligence: Implications for Families and Pediatricians.

Generative artificial intelligence (AI) technologies, capable of producing original text, images, audio, and video, are increasingly embedded in child...

Contribution of social determinants to symptoms of generalized anxiety disorder.

Symptoms of anxiety are known to be triggered by a range of life context factors including early life trauma, poor sleep quality, infrequent exercise,...

The role of ionotropic glutamate receptors in Alzheimer's disease: A scientometric analysis.

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by amyloid-β plaques, neurofibrillary tangles, and synapt...

A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.

Dementia is a progressive neurodegenerative disorder that severely impacts cognitive functions and daily living, especially in aging populations. Amon...

Within-Day Variations in Infant Body Position Predict Caregiver Speech Input.

Infants' time spent in different body positions varies substantially within a day: lying supine on their backs, crawling or playing while prone on the...