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Related Concept Videos

Blood Studies for Cardiovascular System I: Cardiac Biomarkers01:20

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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.
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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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Related Experiment Video

Updated: Jan 7, 2026

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

Zexu Li1, Christina B Young2, Cody Karjadi3

  • 1Dept of Anatomy & Neurobiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Speech features from Logical Memory Delay Recall tests correlate with early tau pathology. Automated and manual methods show consistent associations, suggesting scalable biomarker potential.

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Area of Science:

  • Neuroscience
  • Biomarkers
  • Speech Analysis

Background:

  • Speech features from Logical Memory Delay Recall (LMd) tests are linked to early tau burden.
  • Existing software tools for speech feature extraction use varied methodologies.
  • Validating the robustness of these speech features is crucial for reliable biomarker development.

Purpose of the Study:

  • To compare speech features generated by two distinct software tools (ki:elements and CLAN).
  • To assess the association between these speech features and tau pathology.
  • To validate the robustness and consistency of automated speech feature extraction methods.

Main Methods:

  • Analysis of data from 237 Framingham Heart Study participants with PET imaging and LMd tests.
  • Extraction of five speech features using ki:elements' SIGMA platform and CLAN software.
  • Correlation analysis (Spearman) and multiple linear regression to examine associations with tau SUVR, adjusting for covariates.

Main Results:

  • All speech features extracted by both tools showed significant correlations (p < 0.001).
  • Consistent patterns were observed in the association between speech features and tau SUVR across brain regions.
  • Longer between-utterance pause duration correlated with higher tau SUVR in entorhinal, inferior temporal, and inferior parietal regions.

Conclusions:

  • Speech features, including automated ones, demonstrate compatible associations with early tau burden across different extraction methods.
  • Automated speech analysis holds promise as a scalable biomarker for neurodegenerative diseases.
  • Further investigation into automated linguistic and semantic features is warranted for enhanced diagnostic capabilities.