Salvianolic acid B inhibits thrombosis and directly blocks the thrombin catalytic site
Bibliographic record
Abstract
Background Salvianolic acid B (SAB) is a major component of Salvia miltiorrhiza root (Danshen), widely used in East/Southeast Asia for centuries to treat cardiovascular diseases. Danshen depside salt, 85% of which is made up of SAB, is approved in China to treat chronic angina. Although clinical observations suggest that Danshen extracts inhibited arterial and venous thrombosis, the exact mechanism has not been adequately elucidated. Objective To delineate the antithrombotic mechanisms of SAB. Methods We applied platelet aggregation and coagulation assays, perfusion chambers, and intravital microscopy models. The inhibition kinetics and binding affinity of SAB to thrombin are measured by thrombin enzymatic assays, intrinsic fluorescence spectrophotometry, and isothermal titration calorimetry. We used molecular in silico docking models to predict the interactions of SAB with thrombin. Results SAB dose-dependently inhibited platelet activation and aggregation induced by thrombin. SAB also reduced platelet aggregation induced by adenosine diphosphate and collagen. SAB attenuated blood coagulation by modifying fibrin network structures and significantly decreased thrombus formation in mouse cremaster arterioles and perfusion chambers. The direct SAB-thrombin interaction was confirmed by enzymatic assays, intrinsic fluorescence spectrophotometry, and isothermal titration calorimetry. Interestingly, SAB shares key structural similarities with the trisubstituted benzimidazole class of thrombin inhibitors, such as dabigatran. Molecular docking models predicted the binding of SAB to the thrombin active site. Conclusion Our data established SAB as the first herb-derived direct thrombin catalytic site inhibitor, suppressing thrombosis through both thrombin-dependent and thrombin-independent pathways. Purified SAB may be a cost-effective agent for treating arterial and deep vein thrombosis.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".