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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

749
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:
Troponins
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...
749
Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers01:19

Blood Studies for Cardiovascular System II: CRP, Hcy, and Cardiac Natriuretic Peptide Markers

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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.
These markers indicate stress or strain on the heart muscle:
Natriuretic Peptides (BNP)
Cardiac myocytes produce these hormones in response to ventricular stretching...
516

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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.

Yue Yang1, Ting Li2, Borui Tang1

  • 1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

This study identifies key protein biomarkers like GFAP and NEFL and neuroimaging markers linked to Alzheimer's disease progression. These findings advance understanding and potential treatments for this neurodegenerative condition.

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Area of Science:

  • Neuroscience
  • Biomarker Discovery
  • Machine Learning

Background:

  • Alzheimer's disease (AD) is a leading cause of dementia with no cure, necessitating early detection methods.
  • Progressive cognitive decline in AD highlights the need for predictive tools.
  • Proteomics and neuroimaging offer potential biomarkers and brain changes for AD research.

Purpose of the Study:

  • To de-bias analysis of complex, block-missing multi-omics data using a novel double machine learning framework.
  • To evaluate causal relationships between plasma biomarkers (GFAP, NEFL, GDF15, LTBP2) and hippocampal atrophy in AD.
  • To enhance understanding of Alzheimer's disease progression through integrated biomarker and neuroimaging analysis.

Main Methods:

  • Developed a novel double machine learning framework for analyzing multi-omics data with missing values.
  • Combined rigorous statistical analysis with advanced machine learning techniques.
  • Investigated causal links between specific plasma protein biomarkers and hippocampal atrophy.

Main Results:

  • Identified significant protein biomarkers (GFAP, NEFL, CST5) linked to astroglial activation, axonal injury, and inflammation.
  • Detected functional connectivity disruptions and alterations in large-scale brain networks.
  • Revealed white matter microstructural damage (diffusion MRI) and hippocampal subfield changes relevant to AD pathology.

Conclusions:

  • The study provides a comprehensive analysis of key biomarkers and neuroanatomical changes in Alzheimer's disease.
  • Findings advance the understanding of AD progression mechanisms.
  • Opens new avenues for developing targeted therapeutic interventions for Alzheimer's disease.