Neutrophil P-selectin-glycoprotein-ligand-1 binding to platelet P-selectin enhances metalloproteinase 2 secretion and platelet-neutrophil aggregation
Bibliographic record
Abstract
Platelets and neutrophils constitute a high source of metalloproteinases (MMPs), and their interactions via P-selectin and P-selectin-glycoprotein-ligand-1 (PSGL-1) are involved in thrombosis, vascular remodelling, and restenosis. We investigated the impact of these interactions on platelet MMP-2 secretion and function in platelet and neutrophil aggregation. The secretion of MMP-2 from human platelets was significantly increased three-fold after thrombin activation, and enhanced two-fold in the presence of neutrophils. Neutrophil supernatant had no effect on platelet MMP-2 secretion. While no MMP-2 was detected in the supernatant of neutrophils, a high amount of MMP-9 was released by neutrophils, and remained unchanged upon thrombin activation or in the presence of platelets. Platelet P-selectin, which increased significantly after activation, triggered platelet binding to neutrophils that was completely inhibited by P-selectin or PSGL-1 antagonists, and was reduced by 50% with a GPIIb/ IIIa antagonist. P-selectin or PSGL-1 antagonism abolished the enhanced secretion of platelet MMP-2 in the presence of neutrophils and reduced platelet-neutrophil aggregation. Platelet activation and binding to neutrophils enhance the secretion of platelet MMP-2 via an adhesive interaction between P-selectin and PSGL-1, which contribute to increase platelet-neutrophil 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) | 0.001 | 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".