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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Cardiac biomarkers are critical in diagnosing, prognosing, and managing cardiovascular diseases. Routine measurement of specific biomarkers such as B-type natriuretic peptide (BNP), C-reactive protein (CRP), and homocysteine (Hcy) is common practice in clinical settings to evaluate heart function and predict cardiovascular events.
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Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Tong Chen1, Minheng Chen2, Yan Zhuang2

  • 1The University of Texas at Arlington, Arlington, TX, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Disease Embedding Tree (DET) framework to differentiate Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) using MRI scans. The DET model effectively models disease progression and improves diagnostic accuracy compared to traditional methods.

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Area of Science:

  • Neuroscience
  • Medical Imaging Analysis
  • Machine Learning in Medicine

Background:

  • Alzheimer's Disease (AD) and Lewy Body Dementia (LBD) share overlapping symptoms and neuropathology, complicating differential diagnosis.
  • Current diagnostic approaches often struggle with distinguishing between AD and LBD, necessitating advanced analytical methods.
  • Investigating the progression and interactions between AD and LBD can reveal shared features and underlying connections.

Purpose of the Study:

  • To develop a novel framework, the Disease Embedding Tree (DET), for modeling continuous relationships among Cognitively Normal (CN), AD, and LBD subjects.
  • To leverage structural MRI data and cortical features for improved differential diagnosis of dementia subtypes.
  • To enhance understanding of disease progression and inter-subject variability in neurodegenerative disorders.

Main Methods:

  • Utilized T1-weighted structural MRI data from 106 subjects (AD, CN, LBD).
  • Reconstructed cortical surfaces and employed a novel Gyral Network representation to identify cortical hubs (3 hinge gyri).
  • Extracted cortical features (thickness, curvature, etc.) from 3HGs to train the DET model, incorporating Mini-Mental State Examination (MMSE) scores for continuous relationship modeling.

Main Results:

  • The DET framework demonstrated superior classification performance compared to traditional machine learning methods across all evaluation metrics.
  • Subjects (CN, AD, LBD) were successfully projected onto the DET based on learned representations in the embedding space, visualizing relationships.
  • The model effectively captured continuous relationships and diagnostic capabilities.

Conclusions:

  • The Disease Embedding Tree (DET) framework effectively models continuous relationships among CN, AD, and LBD subjects.
  • DET outperforms traditional models in classification tasks, offering enhanced diagnostic capabilities for dementia subtypes.
  • Leveraging cortical hub features and MMSE scores provides valuable insights into disease progression and inter-group relationships.