Coordinate Activation of Human Platelet Protease-activated Receptor-1 and -4 in Response to Subnanomolar α-Thrombin
Bibliographic record
Abstract
We previously demonstrated that human platelets activated with SFLLRN release PAR-1 activation peptide, PAR-1-(1–41), even in the presence of hirudin. This observation suggests that during their activation, platelets generate a protease that activates PAR-1. In this study, PAR-1 and -4 activation peptides were detected 10 s after ≤1.0 nm α-thrombin, 10 μm SFLLRN, or 100 μm AYPGKF were added to platelets. When SFLLRN or AYGPKF were added to platelets, generation of PAR-1 and -4 activation peptides was complete at 10 s. Generation of both PAR-1 and -4 activation peptides in response to 1 nm α-thrombin was significantly inhibited by affinity-purified anti-PAR-1-(35–62) IgY, anti-PAR-4-(34–54) IgY, and by the specific PAR-1 antagonist BMS 200261, but not by the PAR-4 antagonist YD3. Effective inhibition of platelet aggregation in response to 1.0 nm α-thrombin occurred only in the presence of both anti-PAR span antibodies. We conclude that platelet activation initiated with ≤1.0 nm α-thrombin proceeds via simultaneous PAR-1 and -4 activation. Inhibiting the activation of either PAR inhibits activation of the other. Both PAR-1 and -4 activation must be inhibited to prevent platelet activation subsequent to thrombin binding to platelets. The more efficient generation of PAR activation peptides by platelets activated with SFLLRN or AYGPKF, compared with α-thrombin, indicates that a platelet-derived serine protease that is inactivated by soybean trypsin inhibitor propagates PAR-1 and -4 activation.
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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) | 0.001 | 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".