Anti-platelet Effects of GPIIb/IIIa and P-Selectin Antagonism, Platelet Activation, and Binding to Neutrophils
Bibliographic record
Abstract
Platelet activation with GPIIb/IIIa binding to fibrinogen, aggregation and interaction with leukocytes constitute the principal mediator of thrombosis. Although the clinical benefits of GPIIb/IIIa antagonists have been documented, the relationship between their anti-platelet properties, platelet activation and binding to leukocytes is still debated. We investigated the effects of abciximab, tirofiban, roxifiban, and an anti-P-selectin blocking monoclonal antibody (Mab) on isolated human platelet aggregation using optical aggregometer, and on platelet P-selectin and GPIIb/IIIa expression, and platelet-neutrophil binding using flow cytometry. Thrombin at 0.025 U/ml induced maximal platelet aggregation (76.3 +/- 2.6%), P-selectin expression (88.5 +/- 4%), GPIIb/IIIa activation (PAC-1 binding, 86.2 +/- 8.9%) and platelet-neutrophil binding (58.0 +/- 6.4%). The GPIIb/IIIa antagonists inhibited in a concentration-dependent manner platelet aggregation (IC50 of 100 nM for abciximab and tirofiban and 50 nM for roxifiban) and PAC-1 binding, without any effect on P-selectin. None of these agents affected significantly platelet-neutrophil binding, whereas an anti-P-selectin Mab abolished this binding and amplified the effect of abciximab on platelet aggregation. These results indicate that the effects of these GPIIb/IIIa antagonists on platelet aggregation are not related to inhibition of platelet activation, as P-selectin levels and platelet-neutrophil binding remained unaffected, and highlight the participation of P-selectin with GPIIb/IIIa in platelet aggregation.
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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 | 0.001 |
| 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".