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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

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...
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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.

João Areias Saraiva1,2, Martin Dyrba1,2, Martin Becker1

  • 1University of Rostock, Rostock, Mecklenburg-Vorpommern, Germany.

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

Electroencephalography (EEG) features can predict cognitive status, aiding Alzheimer's disease (AD) monitoring. Machine learning models using EEG data show promise for early cognitive decline detection.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Gerontology

Background:

  • Alzheimer's disease (AD) poses a significant burden on healthcare systems due to global population aging.
  • Continuous monitoring and early detection of cognitive decline are crucial for managing AD.
  • Electroencephalograms (EEG) offer a potential method for ambulatory cognitive status monitoring.

Purpose of the Study:

  • To identify key electroencephalogram (EEG) features indicative of cognitive decline.
  • To assess the feasibility of using machine learning (ML) to estimate cognitive status from EEG data.
  • To correlate EEG features with Mini-Mental State Examination (MMSE) scores.

Main Methods:

  • A cross-sectional study involving 510 older adults from diverse international cohorts.
  • Collected stationary EEG recordings and corresponding MMSE scores (ranging from 4 to 30).
  • Developed a Gradient Boosting ML regressor using EEG spectrum, complexity, and connectivity features to estimate cognitive status, validated via leave-one-out cross-validation.

Main Results:

  • Significant correlations found between MMSE scores and EEG features: Hjorth Complexity (left temporal lobe, r=0.58), alpha coherence (left-right temporal lobes, r=0.48), and beta occipital edge frequency (r=0.42).
  • Eighty combined EEG features were identified as cognitive status predictors.
  • The ML model estimated cognitive status with an average error of 2.53 MMSE points (R²=0.80).

Conclusions:

  • Specific EEG features, particularly temporal and occipital activity, reliably predict cognitive status.
  • The study's diverse cohort enhances generalizability, though more data in lower MMSE ranges is needed.
  • Future ML applications using wearable EEG could automate cognitive health monitoring, especially in resource-limited settings.