Abstract 140: Magnesium Lithospermate B Inhibits Blood Coagulation and Platelet Aggregation: Novel Mechanism of a Traditional Drug for Cardiovascular Diseases
Bibliographic record
Abstract
Magnesium lithospermate B (MLB) is one of the major components of Salvia miltiorrhiza root (Danshen). Danshen extracts have been used to control cardiovascular disease for centuries. In 2005, intravenous injection of Danshen depside salt was approved in China for treatment of chronic angina. Although clinical observations have suggested that Danshen extracts inhibited thrombosis, the exact mechanism has not been adequately explored. Using an in vitro whole blood clotting assay, we observed that MLB (250 μM) significantly reduced clot size. Both the clot wet and dry weights were decreased following treatment (108.3 mg vs. 63.5 mg, and 32.8 mg vs. 18.5 mg, p<0.05, respectively). Using thromboelastography, we found that MLB markedly decreased the mechanical strength of the clot and modestly delayed initiation of coagulation in cell-free blood plasma prepared by centrifugation (10,000 хg, 10 min). Under confocal microscopy, we further observed that MLB significantly reduced the density of the fibrin network formed in plasma following thrombin treatment, suggesting that MLB targets coagulation factors to inhibit coagulation. Recent network pharmacology analyses predict that MLB may interact with VWF, factor XIII (FXIII), or thrombin in the coagulation cascade. We found that MLB did not inhibit VWF-dependent platelet agglutination induced by botrocetin. ELISA revealed that MLB also did not significantly alter the binding of activated FXIII (FXIIIa) to fibrinogen. However, when native FXIII from blood plasma was used for the same assay, MLB significantly reduced the binding of FXIIIa to fibrinogen. Since generation of FXIIIa from FXIII is thrombin-dependent, these data suggest that MLB inhibits thrombin activity or thrombin generation. Indeed, we found that MLB markedly inhibited thrombin-induced gel-filtered human and mouse platelet aggregation. These data demonstrated a novel role for MLB in the inhibition of blood coagulation and platelet aggregation, likely through direct inhibition of thrombin function, although we cannot exclude its additional anti-thrombotic activities. Thus, purified MLB may represent an efficient, low-cost agent for treatment of artery 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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".