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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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Cardiac myocytes produce these hormones in response to ventricular stretching...
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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.

Claudia Aponte1,2, Laura García3,4, María José Hidalgo Ramírez5,6

  • 1Grupo Neuropsicología y Conducta, Universidad de Antioquia, Medellín, Antioquia, Colombia.

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

Graph metrics reveal altered brain connectivity in asymptomatic carriers of the PSEN1 E280A mutation. Betweenness Centrality is higher in non-carriers, suggesting local network differences in early Alzheimer's disease stages.

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Area of Science:

  • Neuroimaging
  • Network Neuroscience
  • Alzheimer's Disease Research

Background:

  • Graph metrics analyze functional connectivity in Alzheimer's Disease (AD).
  • Nodal and global graph metrics differ in AD and MCI patients compared to controls.
  • Autosomal dominant Alzheimer's disease (ADAD) mutation carriers may exhibit varying graph metrics across disease stages.

Purpose of the Study:

  • To investigate graph metric characteristics within the Default Mode Network (DMN).
  • To specifically examine asymptomatic carriers of the PSEN1 E280A mutation.

Main Methods:

  • rs-fMRI data acquired from 32 asymptomatic carriers (aC) and 25 non-carrier controls (nC).
  • Data pre-processed using CONN toolbox; DMN and Resting State Networks (RSNs) extracted with Schaefer atlas (79 ROIs).
  • Graph metrics computed using GRETNA toolbox across sparsity thresholds (0.05-0.5); statistical analyses included Wilcoxon tests and Linear Mixed Models (LMM) with Bonferroni correction.

Main Results:

  • Education differed significantly between groups (p < 0.001), but age and sex did not.
  • Betweenness Centrality (Bc) was higher in nC than aC (p=0.0051).
  • Linear Mixed Models showed group differences in nodal graph metric intercepts, with clustering coefficient, betweenness centrality, local efficiency, and degree centrality slopes differing between aC and nC.

Conclusions:

  • Betweenness Centrality distinguishes asymptomatic carriers (aC) from non-carriers (nC), being higher in nC.
  • This suggests a local network organization where nodes are more central in nC, potentially reflecting early pathophysiological alterations in aC.
  • Observed local changes in aC may represent DMN reorganization to preserve cognitive function despite underlying AD-related changes.