Mechanisms of action of proteinase‐activated receptor agonists on human platelets
Bibliographic record
Abstract
1. We studied the activation of human platelets by thrombin and proteinase activated receptor (PAR)-activating peptides (PAR-APs) [SFLLRNPNDKYEPF-amide (TRAP), TFLLR-amide (PAR1AP) and AYPGKF-amide (PAR4AP)]. 2. PAR agonist-induced platelet aggregation, glycoprotein (GP) Ib and GPIIb/IIIa surface expression and ADP release were measured by light aggregometry, flow cytometry and chemiluminescence. 3. Aggregation inhibitors, including prostacyclin (PGI(2)), nitric oxide-releasing agent (S-nitroso-glutathione, GSNO), aspirin, apyrase, and phenanthroline were used to study the susceptibility of PAR agonist-induced aggregation to pharmacological inhibition. 4. Thrombin was the most potent platelet agonist, followed by PAR1AP, TRAP and PAR4AP. 5. The aggregatory potencies of PAR-APs were not modified by the aminopeptidase inhibitor, amastatin. 6. Subthreshold concentrations of PAR1AP potentiated the effects of PAR4AP to stimulate maximal aggregation. 7. Both PGI(2) and GSNO reduced PAR agonist-induced aggregation and diminished GPIIb/IIIa up-regulation. 8. PAR agonist-induced aggregation was aspirin-insensitive indicating a minor role for TXA(2). 9. In contrast, phenanthroline and apyrase significantly enhanced the anti-aggregatory effects of aspirin against thrombin-, PAR1AP- and TRAP-induced aggregation suggesting the involvement of ADP- and MMP-2-dependent pathways. 10. PAR4AP-induced aggregation (but not PAR1AP-induced aggregation) was entirely ADP-dependent (abolished by apyrase) and resistant to phenanthroline (MMP-2-independent). 11. Thus, the mechanisms of PAR1 and 4-induced platelet aggregation are distinct and depend differentially on their ability to interact with pathways of aggregation, along with the subsequent activation of GPIIb/IIIa receptors.
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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 | 0.000 |
| 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".