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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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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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Updated: Jan 7, 2026

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

Eloïse Da Cunha1,2,3,4, Valeria Manera2, Raphael Zory5

  • 1Speech and Language Pathology Department, Université Côte d'Azur, Nice, Alpes Maritimes, France.

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

Speech analysis can differentiate Alzheimer's disease (AD) subtypes and underlying pathologies. This non-invasive method aids in early prognosis and tailored care for neurodegenerative conditions.

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Area of Science:

  • Neuroscience
  • Computational Linguistics
  • Biomarker Discovery

Background:

  • Alzheimer's disease (AD) presents diverse clinical forms requiring tailored care.
  • Logopenic variant primary progressive aphasia (lvPPA) prognosis is challenging due to varied underlying pathologies (AD or FTLD).
  • Accurate diagnosis necessitates multidisciplinary approaches including CSF analysis, imaging, and psychometric assessments.

Purpose of the Study:

  • To evaluate speech markers for cross-classifying AD phenotypes with Cerebro-Spinal Fluid (CSF) profiles.
  • To provide a non-invasive method for early prognosis of neurodegenerative evolution.
  • To assess the potential of machine learning models in differentiating AD subtypes and pathologies.

Main Methods:

  • 42 patients were classified into lvPPA- (FTLD), lvPPA+ (AD), and AD groups.
  • Speech recordings from a 14-sentence repetition task were analyzed for prosodic and temporal features.
  • Supervised machine learning models (Random Forest, KNN, SVM) were trained and validated for classification.

Main Results:

  • Random Forest model achieved 91% cross-validation accuracy in differentiating the three groups.
  • Speech-based classification accurately identified clinical phenotypes and underlying pathologies.
  • The study demonstrated speech markers as valuable tools for early prognostic identification.

Conclusions:

  • Speech-based classification offers early prognostic insights by linking clinical phenotypes with CSF profiles.
  • Enhanced prognostic precision through speech analysis can improve early intervention strategies.
  • Larger studies are needed to validate speech classification as a reliable non-invasive tool for differential prognosis.