Effect of Different Doses of Acetylsalicylic Acid on the Antithrombotic Activity of Clopidogrel in a Mouse Arterial Thrombosis Model
Bibliographic record
Abstract
Objective— Dual-antiplatelet therapy with acetylsalicylic acid and a P2Y 12 antagonist, such as clopidogrel, is the standard of care for acute coronary syndromes. However, the drugs have divergent effects on the formation of cAMP, an inhibitory second messenger. Thus, by inhibiting the synthesis of prostacyclin, acetylsalicylic acid reduces cAMP formation, whereas clopidogrel potentiates it. Therefore, with higher doses of acetylsalicylic acid, the potentiation of cAMP production by clopidogrel may be attenuated, which could limit the antithrombotic potential of the drug combination. The purpose of this study was to examine this possibility in vivo. Approach and Results— Mice were given oral acetylsalicylic acid at varying doses, oral clopidogrel (5 mg/kg body weight), or both. At doses of 0.15 and 0.6 mg/kg, acetylsalicylic acid inhibited arachidonic acid–induced platelet aggregation, but only 0.6 mg/kg acetylsalicylic acid, or higher, decreased the plasma levels of 6-keto-prostaglandin-F 1α , the stable metabolite of prostacyclin. When given with clopidogrel, laser injury–induced arterial thrombi were significantly larger with the 0.6 mg/kg dose of acetylsalicylic acid than with the 0.15 mg/kg dose. Thrombi in mice treated with clopidogrel and the 0.15 mg/kg dose of acetylsalicylic acid were smaller than in mice treated with clopidogrel alone, suggesting that acetylsalicylic acid can add to the antithrombotic effect of clopidogrel but that higher doses of acetylsalicylic acid blunt the antithrombotic effect of clopidogrel. Conclusions— These findings support the use of lower, prostacyclin-preserving, doses of acetylsalicylic acid in conjunction with clopidogrel.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".