Alzheimer's Disease

Time Distributed Classification of Alzheimer's Disease on MRI Scans.

The diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MR...

PolyProline Predictor: A web server for empirical sequence-based prediction of polyproline II helices.

Polyproline II (PPII) helices are extended left-handed secondary structures increasingly recognized for their roles in molecular recognition, signaling and within intrinsically disordered regions of proteins. Despite their functional importance, predicting regions with propensity to form PPII helices from sequence alone remains challenging due to subtle sequence determinants and their frequent mis...

The creation and verification of a detection model for mild cognitive impairment by employing eye-tracking and gait metrics.

BackgroundMild cognitive impairment is a prodromal stage of dementia, and early identification is crucial for prognosis.ObjectiveThis study aims to cr...

Latent Space Projections and Atlases, a Cautionary Tale in Deep Neuroimaging using Autoencoders.

This study introduces a deep learning framework for the inferential exploration of latent representations in 3D brain MRI, leveraging a simple convolu...

Esketamine multi-omic biomarker evaluation in major depressive disorder (EMBER-MDD): concept, objectives and methodologies of a non-clinical investigator-initiated study.

Treatment resistance (TR) in major depressive disorder (MDD) affects a substantial minority of patients and is hard to recognize early, delaying inten...

Wearable sensor-based Mild Cognitive Impairment Identification: A Multi-Domain Gait Analysis Approach with Association Rule Mining.

Mild Cognitive Impairment (MCI) involves cognitive decline beyond age-expected norms without impairing daily function. Crucially, it often precedes de...

Machine Learning-Based Identification and Ranking of Mortality Predictors After Hip Fracture Surgery: An Analysis of the Australian and New Zealand Hip Fracture Registry.

BACKGROUND: Hip fractures are a major global health issue with high mortality and morbidity, especially in older adults. One-year mortality post-surge...

From companion technologies to social care infrastructure: A multi-level perspective on loneliness-related support in dementia care in an era of artificial intelligence (AI).

Loneliness and social isolation are important psychosocial concerns in dementia care, but they are difficult to address through pharmacological treatm...

Magnetic resonance imaging-based Alzheimer's disease detection using an EfficientNet-CMSACCN framework.

Alzheimer's Disease (AD) is a degenerative neurological condition characterized by memory loss, cognitive deterioration, and brain tissue shrinkage. D...

Agentic Artificial Intelligence-Driven Explainable Deep Learning for Deciphering Noncoding Pathogenic Mechanisms of Delirium Through Genomic Big Data Integration.

Delirium is a prevalent neuropsychiatric syndrome affecting up to 50% of hospitalized elderly patients, associated with increased mortality, cognitive...

Digital Conversation Companions.

Advances in voice recognition and artificial intelligence (AI) could soon yield digital conversation companions-software that combines conversational ...

Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.

Facial analysis is increasingly being explored as a source of scalable behavioral signals relevant to Alzheimer's disease (AD) and AD-related cognitiv...

Deep contrastive learning framework identifies cell-type-specific drug targets in Alzheimer's disease.

INTRODUCTION: Identifying disease-modifying drug targets is crucial for developing effective Alzheimer's disease (AD) treatments. METHODS: We present ...

Advances in 3D cell culture models for alzheimer's disease drug discovery.

INTRODUCTION: Alzheimer's Disease (AD) affects more than 57 million people, yet drug development faces high failure rates due to the limited translati...

Integrated machine learning, molecular docking, and molecular dynamics simulations for in silico identification of GSK3β inhibitors for Alzheimer's disease.

Glycogen Synthase Kinase-3 Beta is a multifunctional serine/threonine kinase, involved in regulating multiple cellular processes. Its dysregulation pl...

Machine learning classification and regional differentiation of neuropathologically-confirmed Alzheimer's disease and comorbid Lewy body disease.

BACKGROUND: Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) co-occur frequently, and growing evidence, including neuropathology, supports...

Machine Learning-Based single-cell characterization of lipid metabolic reprogramming in prostate cancer.

Prostate cancer (PCa) is one of the most prevalent malignancies in men worldwide, and metabolic reprogramming particularly of lipid metabolism plays a...

Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.

Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective t...

Novel Alzheimer's disease-associated variants and genetic interactions identified from UK biobank whole-exome sequencing data using IBI-DT.

Alzheimer's disease (AD) is a highly heritable neurodegenerative disorder whose genetic architecture remains incompletely understood, particularly wit...

Reinforcement learning-driven adaptive game therapy for cognitive impairment patients with improved vision transformer based detection model.

BACKGROUND: Cognitive impairment is the growing challenge that requires early diagnosis and personalized management of neurodegenerative conditions li...