Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

Blood Studies for Cardiovascular System I: Cardiac Biomarkers

746
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...
746
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

511
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...
511

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ATOX1 overexpression mitigates copper homeostasis in microglia: Implications for Alzheimer's disease therapy.

Genes & diseases·2026
Same author

Lecanemab treatment for mild alzheimer's disease with high risk of cerebral hemorrhage: a case report.

BMC neurology·2026
Same author

Correlation of prefrontal-based resting state functional connectivity with the degree of cognitive impairment in Alzheimer's disease: a functional near-infrared spectroscopy study.

Behavioral and brain functions : BBF·2025
Same author

Computerized cognitive training enhances cognitive function in Alzheimer's disease by downregulating Ruminococcus-TMAO pathway.

Journal of translational medicine·2025
Same author

CHIT1 regulates the neuroinflammation and phagocytosis of microglia and suppresses Aβ plaque deposition in Alzheimer's disease.

The Journal of pathology·2025
Same author

Cerebrospinal fluid β2-microglobulin promotes the tau pathology through microglia-astrocyte communication in Alzheimer's disease.

Alzheimer's research & therapy·2025

Related Experiment Video

Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

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

Published on: January 28, 2014

37.1K

Biomarkers.

Wenbo Zhang1, Ming Chen1, Fuxin Zhong1

  • 1The First Affiliated Hospital of Chongqing Medical University, Chongqing, Chongqing, China.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary

Functional near-infrared spectroscopy (fNIRS) combined with machine learning accurately detects mild cognitive impairment (MCI) and Alzheimer's disease (AD). This approach offers insights into brain activity changes during cognitive tasks for earlier diagnosis.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

699

Related Experiment Videos

Last Updated: Jan 7, 2026

Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
07:20

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

Published on: January 28, 2014

37.1K
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K
Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans
08:14

Ecotoxicological Methodologies to Evaluate Biomarkers at Different Scales in Neotropical Anurans

Published on: April 28, 2023

699

Area of Science:

  • Neuroscience
  • Medical Imaging
  • Machine Learning

Background:

  • Early detection of cognitive impairment, including normal cognition (NC), mild cognitive impairment (MCI), and Alzheimer's disease (AD), remains a challenge.
  • Functional near-infrared spectroscopy (fNIRS) offers a non-invasive method to assess brain activity.

Purpose of the Study:

  • To differentiate between stages of cognitive impairment (NC, MCI, AD) using fNIRS during the Clock Drawing Test (CDT).
  • To develop and validate a machine learning model for predicting cognitive progression based on fNIRS data.

Main Methods:

  • Recorded neural activation using a multi-channel fNIRS system during the CDT.
  • Extracted features (Integral, Centroid, Slope, Difference, Mean) from Oxy-Hb signals and analyzed using Generalized Linear Model (GLM).
  • Developed a three-class classification model using machine learning and interpreted feature importance with Shapley Additive Explanations (SHAP).

Main Results:

  • Significant differences in centroid values were observed across NC, MCI, and AD groups (P < 0.05).
  • The mean centroid value increased progressively from NC to AD, with MCI showing the smallest values in specific channels.
  • A machine learning model combining SMOTE and Extra Trees achieved a macro-average AUC of 0.776, with SHAP identifying key features from the dorsolateral prefrontal cortex, superior temporal gyrus, and Broca's area.

Conclusions:

  • fNIRS combined with machine learning improves the early detection of cognitive impairment, especially MCI.
  • The study provides clinical insights into the neural mechanisms associated with cognitive decline.