Aspirin Reduces the Potentiating Effect of CD40L on Platelet Aggregation via Inhibition of Myosin Light Chain
Bibliographic record
Abstract
Background Antiplatelet therapy with aspirin (acetylsalicylic acid [ASA]) is less efficient in some coronary patients, which increases their risk of developing thrombosis. Elevated blood levels of thromboinflammatory mediators, like soluble CD40L (sCD40L), may explain such variabilities. We hypothesized that in the presence of elevated levels of sCD 40L, the efficacy of ASA may vary and aimed to determine the effects of ASA on CD 40L signaling and aggregation of platelets. Methods and Results The effects of ASA on CD 40L‐treated human platelets, in response to suboptimal concentrations of collagen or thrombin, were assessed at levels of aggregation, thromboxane A 2 secretion, and phosphorylation of p38 mitogen‐activated protein kinase, nuclear factor kappa B, transforming growth factor‐β–activated kinase 1, and myosin light chain. sCD 40L significantly elevated thromboxane A 2 secretion in platelets in response to suboptimal doses of collagen and thrombin, which was reversed by ASA . ASA did not inhibit the phosphorylation of p38 mitogen‐activated protein kinase, nuclear factor kappa B, and transforming growth factor‐β–activated kinase 1, with sCD 40L stimulation alone or with platelet agonists. sCD 40L potentiated platelet aggregation, an effect completely reversed and partially reduced by ASA in response to a suboptimal dose of collagen and thrombin, respectively. The effects of ASA in sCD 40L‐treated platelets with collagen were related to inhibition of platelet shape change and myosin light chain phosphorylation. Conclusions ASA does not affect platelet sCD 40L signaling but prevents its effect on thromboxane A 2 secretion and platelet aggregation in response to collagen, via a mechanism implying inhibition of myosin light chain. Targeting the sCD40L axis in platelets may have a therapeutic potential in patients with elevated levels of sCD 40L and who are nonresponsive or less responsive to ASA.
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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".