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Related Concept Videos

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.
The essential diagnostic tools for detecting myocardial necrosis and monitoring individuals suspected of having acute coronary syndrome (ACS) include:
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Troponins, particularly cardiac troponins I and T, are the most precise and sensitive markers of myocardial injury. They are detectable within 4-6 hours of myocardial injury and remain...
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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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Related Experiment Video

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
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Biomarkers.

Barbora Rehak Buckova1

  • 1Radboud University Medical Center, Nijmegen, Netherlands.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary

This study introduces a novel normative modeling approach for neuroimaging data, integrating large cross-sectional and longitudinal information to track individual brain changes over time and detect deviations from typical aging or disease progression.

Area of Science:

  • Neuroscience
  • Biostatistics
  • Medical Imaging

Background:

  • Large-scale neuroimaging collaborations enable analysis of population variation and disease patterns.
  • Normative modeling relates individual neuroimaging data to population standards for subject-level inferences.
  • Existing methods struggle to integrate cross-sectional and longitudinal data for temporal analysis.

Purpose of the Study:

  • To develop an innovative normative modeling approach for analyzing longitudinal neuroimaging data.
  • To leverage heterogeneity in large cross-sectional datasets and incorporate longitudinal dynamics for subject-specific inferences.
  • To enable precise detection of atypical changes and deviations from typical trajectories over time.

Main Methods:

  • Utilized pre-trained normative models from over 58,000 individuals.

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  • Integrated large-scale cross-sectional data with longitudinal dynamics.
  • Developed an individualized score to quantify significant changes over time.

    • Main Results:

    • The approach offers scalability, flexibility, and applicability across studies.
    • Enables detection of atypical changes by comparing individual trajectories with population norms.
    • Provides individualized scores quantifying temporal changes.

    Conclusions:

    • The novel framework enhances the detection of subtle neuroimaging changes over time.
    • Holds promise for early detection and tracking of neurodegeneration, particularly in Alzheimer's disease.
    • Versatile for analyzing longitudinal neuroimaging data in various neurological and psychiatric conditions.