Alzheimer's Disease

Simultaneous Profiling and Quantification of Tau and Its Phosphorylated Isomers Using Engineered FraC Nanopores.

Tau aggregation and hyperphosphorylation are key pathological hallmarks and early diagnostic biomarkers of Alzheimer's disease (AD). However, the simultaneous profiling and quantitative analysis of Tau and its phosphorylated isoforms remain technically challenging. Here, by introducing an acidic aspartate residue at the G6 site of FraCG13F, we engineered a high-resolution FraCG6DG13F nanopore with...

SSiamese Capsule Network (SNNCap) : Cognitive Analysis for Alzheimer's Disease Classification from MRI Data.

Alzheimer's Disease (AD) detection is essential for timely treatment and better patient care. Magnetic Resonance Imaging (MRI) is a technique in which radio waves and magnetic fields are used to capture high-resolution, multi-dimensional representations of brain structures. This high-resolution imaging capability makes MRI a key tool for diagnosing neurological disorders such as Alzheimer's diseas...

Literature-derived serum miRNA signatures associated with cognitive decline in Alzheimer's disease: integrated analysis and machine learning-based diagnostic modeling.

Deciphering the molecular network of Trichostatin A in regulating Alzheimer's disease screening of core genes and mechanistic investigation based on multidimensional bioinformatics and molecular simulation.

BACKGROUND: Histone deacetylases (HDACs) regulate neuroprotection; however, Trichostatin A (TSA), an HDAC inhibitor, lacks clear molecular mechanisms ...

CDR-Net: A computerized framework to detect Alzheimer's diseases and mild cognitive impairment.

Alzheimer's disease (AD) and mild cognitive impairment (MCI) are two dementia-related brain illnesses that are prevalent among elders in the twenty-fi...

Advancing brain age estimation: normative deviation mapping (NDM) for sensitive detection of pathological aging.

Brain age is a valuable neuroimaging-based biomarker for assessing brain health, typically estimated using machine learning (ML) models. However, ML a...

Causal inference in real-world dementia research: a systematic review protocol.

BACKGROUND: Dementia presents complex challenges for causal inference due to its multifactorial aetiology and slow, heterogeneous progression. Randomi...

NeuroBACE-ML: A reliability-aware screening framework for high-throughput prioritization of potent BACE1 inhibitors.

Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) is a key enzyme in amyloid-β generation and remains an important target in Alzheimer's d...

SRSF1 as a promising biomarker and key player in the pathogenesis of Alzheimer's disease.

Given the unclear pathogenesis and insidious progression of Alzheimer's disease (AD), the aim of the present study was to identify reliable diagnostic...

Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary preven...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twof...

Machine Learning-Driven Ensemble Screening of Multitarget Kinase Inhibitors for Tauopathy-Associated Neurodegeneration Using All-Atom and Steered MD Simulations.

Tauopathies arise when normal functions of the tau protein in axonal transport and neuronal maintenance are disrupted by an imbalance between kinases ...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...

The association between PM2.5 components and cognitive decline: the impact of measurement error correction.

Growing evidence suggests that exposure to fine particulate matter (PM2.5) may accelerate cognitive decline and increase dementia risk, but the roles ...

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.

Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves m...

MAISNet: A Multi-Species Integrated Graph Neural Network for Acetylcholinesterase Inhibitor Screening.

MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as alzheimer's disease (AD). Although existing dru...

Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated...

Evaluating Homoscedastic Uncertainty Weighting and Generative Tau Imputation in a Multimodal, Multitask Deep Learning Framework for Alzheimer's Disease.

Psychometric modeling of Boston process approach data for dementia prediction in the Framingham Heart Study.

OBJECTIVES: Neuropsychological (NP) tests are multi-domain in execution. Reliance on a single score representing specific domains obscures the detecti...

Alzheimer's and Parkinson's Detection with Video-Based Hybrid Deep Learning from Brain MRI.

Dementia, which refers to disorders related to human memory, significantly affects the human brain, and a person with it can experience certain diffic...