Salvia miltiorrhiza: A Potential Red Light to the Development of Cardiovascular Diseases
Bibliographic record
Abstract
Salvia miltiorrhiza Bunge, also known as Danshen in Chinese, has been widely used to treat cardiovascular diseases (CVD) in China and other Asia countries. Here, we summarize literatures of the historical traditional Chinese medicine (TCM) interpretation of the action of Salvia miltiorrhiza, its use in current clinical trials, its main phytochemical constituents and its pharmacological findings by consulting Pubmed, China Knowledge Resource Integrated, China Science and Technology Journal, and the Web of Science Databases. Since 2000, 39 clinical trials have been identified that used S. miltiorrhiza in TCM prescriptions alone or with other herbs for the treatment of patients with CVD. More than 200 individual compounds have been isolated and characterized from S. miltiorrhiza, which exhibited various pharmacological activities targeting different pathways for the treatment of CVD in various animal and cell models. The isolated compounds may provide new perspectives in alternative treatment regimes and reveal novel chemical scaffolds for the development of anti-CVD drugs. Meanwhile, there are also some rising concerns of the potential side effects and drug-drug interactions of this plant. The insights gained from this study will help us to better understanding of the actions of this herb for management of cardiovascular disorders. As an herb of red root, S. miltiorrhiza will act as a potential red light to prevent the development of CVD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".