Medicinal Plants as Potential Hemostatic Agents
Bibliographic record
Abstract
PURPOSE: Medicinal plants with a variety of phytochemical ingredients remain a potential source for new drug discovery. The use of medicinal herbs in a wide range of diseases and symptoms, such as bleeding, is prevalent in traditional and ethno medicine worldwide. Thus, this work provides a comprehensive review of medicinal plants or their isolated compounds, with respect to their ethno-medicinal use, which have demonstrated the stimulating effect on the hemostasis process. METHODS: The relevant studies were withdrawn from electronic databases including Pubmed, EMBASE and Web of Science with a structured search methodology. RESULTS: The total of 17 medicinal plants with hemostatic activity were extracted. The most frequently studied plant families were Compositae, Lamiaceae, Fabaceae, and Asteraceae. Bioactive compounds exerting hemostatic activity included tannins, iridoid glycosides, glycoconjugate, lignan, saponins and phenolic compounds. The most attributed mechanisms include coagulation stimulation via increasing the factor XII activity and plasma fibrinogen levels, the fibrinolysis inhibition, vascular or smooth muscle constriction and platelet aggregation. The most important adverse effects of high dose extract or isolated compounds administration were hepatotoxicity and nephrotoxicity. CONCLUSION: This review provides a list of medicinal plants with hemostatic activity that could be used as valuable sources of new plant-based hemostatic agents. Furthermore, this could be practical in detecting possible interactions of plants with anticoagulant, antiplatelet, fibrinolytic and antifibrinolytic medications.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".