Inhibition and reversal of platelet aggregation by <i>α</i>IIb<i>β</i>3 antagonists depends on the anticoagulant and flow conditions: differential effects of Abciximab and Lamifiban
Bibliographic record
Abstract
Shear influences platelet aggregate formation and stability, as well as the inhibitory capacities of antithrombotic drugs. We compared the inhibitory and disaggregating properties of two distinct alphaIIbbeta3 antagonists, Abciximab and Lamifiban, on platelet aggregation induced by adenosine diphosphate (ADP) (5 micromol/l) in platelet-rich plasma (PRP), in an aggregometer (poorly defined low shear, <100/s) and in a microcouette at arterial shear rate (1,000/s). Platelet aggregation was detected by changes in light transmission in the aggregometer (TA), and by particle counting with a flow cytometer (PA). Lamifiban (1 mumol/l) completely inhibited TA or PA induced by ADP in citrated PRP in the aggregometer or microcouette. In contrast, Abciximab (2 micromol/l) only partially inhibited PA in the microcouette while blocking both TA and PA in the aggregometer. Moreover, Abciximab did not reverse platelet aggregates formed either in the microcouette or in the aggregometer, whereas Lamifiban caused complete reversal. On the contrary, Abciximab completely inhibited platelet aggregation induced by ADP in hirudin/d-Phe-Pro-Arg-chloromethylketone PRP in the microcouette. Our results demonstrate a marked dependence of inhibitory capacity of Abciximab on shear conditions, with citrate anticoagulant responsible for the residual aggregation, in contrast to Lamifiban, another alphaIIbbeta3 antagonist interacting with a distinct site on beta3.
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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 | 0.000 |
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".