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tau Proteins

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A Study on Machine Learning Models in Detecting Cognitive Impairments in Alzheimer's Patients Using Cerebrospinal Fluid Biomarkers.

American journal of Alzheimer's disease and other dementias
Several research studies have demonstrated the potential use of cerebrospinal fluid biomarkers such as amyloid beta 1-42, T-tau, and P-tau, in early diagnosis of Alzheimer's disease stages. The levels of these biomarkers in conjunction with the demen...

XAI-Based Assessment of the AMURA Model for Detecting Amyloid-β and Tau Microstructural Signatures in Alzheimer's Disease.

IEEE journal of translational engineering in health and medicine
Brain microstructural changes already occur in the earliest phases of Alzheimer's disease (AD) as evidenced in diffusion magnetic resonance imaging (dMRI) literature. This study investigates the potential of the novel dMRI Apparent Measures Using Red...

Significance of plasma p-tau217 in predicting long-term dementia risk in older community residents: Insights from machine learning approaches.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Whether plasma biomarkers play roles in predicting incident dementia among the general population is worth exploring.

Integrated Ultrasound-Enrichment and Machine Learning in Colorimetric Lateral Flow Assay for Accurate and Sensitive Clinical Alzheimer's Biomarker Diagnosis.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The colloidal gold nanoparticle (AuNP)-based colorimetric lateral flow assay (LFA) is one of the most promising analytical tools for point-of-care disease diagnosis. However, the low sensitivity and insufficient accuracy still limit its clinical appl...

Ultrasensitive Detection of Blood-Based Alzheimer's Disease Biomarkers: A Comprehensive SERS-Immunoassay Platform Enhanced by Machine Learning.

ACS chemical neuroscience
Accurate and early disease detection is crucial for improving patient care, but traditional diagnostic methods often fail to identify diseases in their early stages, leading to delayed treatment outcomes. Early diagnosis using blood derivatives as a ...

Alzheimer's Disease: Exploring Pathophysiological Hypotheses and the Role of Machine Learning in Drug Discovery.

International journal of molecular sciences
Alzheimer's disease (AD) is a major neurodegenerative dementia, with its complex pathophysiology challenging current treatments. Recent advancements have shifted the focus from the traditionally dominant amyloid hypothesis toward a multifactorial und...

Predicting conversion in cognitively normal and mild cognitive impairment individuals with machine learning: Is the CSF status still relevant?

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Machine learning (ML) helps diagnose the mild cognitive impairment-Alzheimer's disease (MCI-AD) spectrum. However, ML is fed with data unavailable in standard clinical practice. Thus, we tested a novel multi-step ML approach to predict ...

Using Machine Learning to Design a FeMOF Bidirectional Regulator for Electrochemiluminescence Sensing of Tau Protein.

ACS applied materials & interfaces
The single-luminophore-based ratiometric electrochemiluminescence (ECL) sensor coupling bidirectional regulator has become a research hotspot in the detection field because of its simplicity and accuracy. However, the limited bidirectional regulator ...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Scientific reports
Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in ...

Machine learning prediction of tau-PET in Alzheimer's disease using plasma, MRI, and clinical data.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.