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

Theyaneshwaran Jayaprakash1, Connor Lee Cornelison1, Plamena P Powla2

  • 1Indiana University, Bloomington, IN, USA.

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

Deep learning identified three distinct Alzheimer disease (AD) subtypes based on tau-PET patterns. These subtypes show unique cognitive and functional connectivity differences, highlighting AD heterogeneity.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer disease (AD) is characterized by amyloid plaques and neurofibrillary tangles.
  • AD patients display heterogeneous tau-PET accumulation patterns across the brain.

Purpose of the Study:

  • To identify distinct population subtypes within the Alzheimer disease (AD) spectrum.
  • To leverage a novel deep learning algorithm analyzing tau-PET spatial patterns for subtype discovery.

Main Methods:

  • Analysis of tau-PET data from 318 participants (ADNI Phase 3) using a self-supervised Gaussian Mixture Model framework.
  • Subtype identification based on tau accumulation across 68 brain regions (Desikan-Killiany atlas).
  • Validation of subtypes using clinical assessments, volumetric data, APOE4 genetics, and functional connectomics.

Main Results:

  • Three distinct Alzheimer disease (AD) subtypes (S1, S2, S3) were identified with 93% validation accuracy.
  • Subtypes exhibited unique tau accumulation patterns: minimal (S1), medial/posterior (S2), and widespread cortical (S3).
  • Significant differences in cognitive function (memory, language) and functional connectivity were observed between subtypes.

Conclusions:

  • Tau-PET imaging reveals significant spatial heterogeneity in Alzheimer disease (AD) pathology.
  • Deep learning models effectively identify distinct AD subtypes with multimodal differences.
  • Findings underscore the potential of AI in understanding AD progression and heterogeneity.