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Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

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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.

Yanjun Lyu1, Xiaowei Yu1, Michael Qu2

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

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces the Gyral-Sulcal Net, a novel brain network model, to improve the diagnosis of mild cognitive impairment (MCI) from cognitively normal (CN) individuals by integrating structural and functional brain data.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Biomedical Engineering

Background:

  • Alzheimer's disease (AD) is irreversible; early diagnosis of mild cognitive impairment (MCI) is crucial.
  • AD impacts brain structure and function, necessitating integrated diagnostic approaches.
  • Distinguishing MCI from cognitively normal (CN) individuals requires comprehensive analysis of brain features.

Purpose of the Study:

  • To propose a novel brain network representation, the Gyral-Sulcal Net, for enhanced MCI diagnosis.
  • To integrate finer-scale brain structural landmarks with corresponding functional features.
  • To improve the diagnostic accuracy in differentiating MCI from CN individuals.

Main Methods:

  • Utilized structural MRI (sMRI) and functional MRI (fMRI) data from 126 MCI and 141 CN subjects (ADNI dataset).
  • Constructed the Gyral-Sulcal Net from sMRI, identifying gyral and sulcal nodes and extracting fMRI signals.
  • Developed a graph transformer attention network using the Gyral-Sulcal Net's topology and functional connectivity matrix for classification.

Main Results:

  • Achieved a mean accuracy of 73.80% (±2.97% SD) in MCI/CN classification using 5-fold cross-validation.
  • Demonstrated promising results in distinguishing between MCI and CN groups.
  • The proposed method shows effectiveness in leveraging integrated brain network features.

Conclusions:

  • Introduced the Gyral-Sulcal Net, a novel network integrating gyri and sulci for finer-scale anatomical analysis.
  • Effectively combined structural and functional information within the Gyral-Sulcal Net for node features.
  • The novel graph transformer network utilizing the Gyral-Sulcal Net shows significant potential for improving MCI diagnostic accuracy.