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Related Concept Videos

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Cardiac output (CO) is an integral aspect of human physiology, reflecting the heart's efficiency and responsiveness to the body's needs. It represents the volume of blood that the left or right ventricle ejects into the aorta or pulmonary trunk each minute. The CO is calculated by multiplying the heart rate (HR)—the number of heartbeats per minute—by the stroke volume (SV)—the amount of blood pumped out with each heartbeat.
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Related Experiment Video

Updated: Oct 26, 2025

A Thrombotic Stroke Model Based On Transient Cerebral Hypoxia-ischemia
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Mining a stroke knowledge graph from literature.

Xi Yang1,2,3, Chengkun Wu2, Goran Nenadic4

  • 1College of Computer, National University of Defence Technology, Changsha, 410073, China.

BMC Bioinformatics
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

This study integrates Western and Traditional Chinese Medicine knowledge to create StrokeKG, a comprehensive knowledge graph for stroke research. StrokeKG aids in discovering new prevention methods and treatments by connecting diverse stroke-related entities.

Keywords:
Biomedical text miningKnowledge graphStrokeTraditional Chinese Medicine

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Area of Science:

  • Biomedical Informatics
  • Computational Biology
  • Traditional Chinese Medicine Research

Background:

  • Stroke is a leading cause of mortality with high impact worldwide.
  • Both Western biomedicine and Traditional Chinese Medicine (TCM) offer insights into stroke biology and treatment.
  • Existing research and databases often treat these approaches in isolation.

Purpose of the Study:

  • To integrate knowledge from Western biomedicine and TCM literature and databases for stroke research.
  • To develop a comprehensive knowledge graph for stroke, named StrokeKG.
  • To facilitate the discovery of effective stroke prevention and treatment strategies.

Main Methods:

  • Utilized biomedical text mining and named-entity recognition to identify stroke-related entities.
  • Applied a rule-based approach combined with a pre-trained BioBERT model for relationship extraction.
  • Integrated data from multiple databases including CID, TCMID, and ETCM.

Main Results:

  • Constructed StrokeKG, a knowledge graph with approximately 46,000 nodes and 157,000 links.
  • StrokeKG connects diverse entities such as diseases, genes, symptoms, drugs, pathways, herbs, chemicals, and patent medicines.
  • The knowledge graph captures relationships from both biomedical and TCM domains.

Conclusions:

  • StrokeKG offers practical and reliable knowledge to advance stroke research.
  • The knowledge graph can guide new research directions and identify opportunities for drug repurposing and discovery.
  • StrokeKG and its source data are publicly available to the research community.