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

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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.

Anran Ran1,2, Yuen Tung Ng3,4,5,6,7, Bonnie Y K Lam3,4,5,6,7

  • 1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, NA, Hong Kong.

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

The RetinAD deep learning model identified older, non-demented individuals with retinal microvasculopathy, suggesting it may predict future Alzheimer's disease (AD) risk.

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is associated with retinal changes.
  • Deep learning model RetinAD differentiates AD dementia from healthy controls using retinal images.
  • Retinal microvasculopathy may precede cognitive symptoms of AD.

Purpose of the Study:

  • To investigate if RetinAD can detect retinal microvasculopathy in non-demented elderly subjects.
  • To compare retinal vessel network measures between RetinAD-classified "positive" and "negative" cases in this cohort.

Main Methods:

  • Recruited 187 non-demented elderly subjects (59-80 years) with subjective cognitive decline (SCD) from the BEAT AD program.
  • Utilized RetinAD to classify subjects as "positive" or "negative" for AD-related retinal changes.
  • Quantified retinal vessel networks using Singapore I Vessel Assessment (SIVA) software.

Main Results:

  • 15.5% of subjects were classified as RetinAD "positive".
  • "Positive" subjects were significantly older and showed higher arteriolar and venular branching coefficients.
  • Venular branching coefficient remained significant after adjusting for age and blood pressure.

Conclusions:

  • RetinAD identified non-demented elderly with poorer retinal microvasculopathy and "biologically older" brains.
  • These findings suggest RetinAD's potential to identify individuals at future risk of AD dementia.