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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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Updated: Jan 7, 2026

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

Leif Er Simmatis1,2, Emma E Russo1,2, Tayo Steininger1

  • 1Cove Neurosciences, Toronto, ON, Canada.

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

This study introduces an automated electroencephalography (EEG) pipeline to detect Alzheimer's disease (AD) biomarkers. The method shows promise for improved AD diagnosis and monitoring.

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Area of Science:

  • Neuroscience
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) lacks validated functional biomarkers for diagnosis and classification.
  • There is a need for non-invasive methods to detect and monitor AD progression.
  • Electroencephalography (EEG) shows potential but faces challenges like poor signal-to-noise ratio and standardization.

Purpose of the Study:

  • To develop and pilot an automated preprocessing and feature extraction pipeline for EEG data.
  • To identify EEG-based indicators of AD and correlates of disease progression.

Main Methods:

  • A custom automated pipeline for EEG data preprocessing and feature extraction was analyzed.
  • The pipeline was tested for its ability to identify AD indicators.

Main Results:

  • The automated pipeline successfully identified novel and known EEG measures associated with AD status.
  • These EEG measures demonstrated strong external validity for indicating AD and progression.

Conclusions:

  • The automated EEG approach offers a viable framework for implementing EEG biomarkers in AD.
  • This methodology could lead to enhanced diagnostic and monitoring strategies for Alzheimer's disease patients.