Leukocyte urokinase plasminogen activator receptor and PSGL1 play a role in endogenous arterial fibrinolysis
Bibliographic record
Abstract
Fibrin is an integral component of arterial thrombi. Using a mouse model of arteriolar thrombosis, high-speed fluorescence microscopy reveals that, within minutes, the fibrin content of thrombi rapidly increases and then decreases. The decrease in fibrin coincides with leukocyte binding to the thrombi, a process mediated by the interaction of leukocyte P-selectin glycoprotein ligand-1 (PSGL-1) with P-selectin on the surface of activated platelets. Because leukocytes possess urokinase-type plasminogen activator (uPA) activity, we used mice deficient in uPA or the uPA receptor (uPAR) to explore the contribution of leukocyte-associated uPA to the loss of fibrin from these thrombi. Fibrin loss in both uPA-deficient mice and uPAR-deficient mice was reduced compared with that in wild-type controls. Transfusion of leukocytes from wild-type mice into uPAR-deficient mice restored fibrin loss to levels similar to that in wild-type mice. In contrast, transfusion of leukocytes from mice deficient in uPAR or PSGL-1 did not enhance fibrin loss. Thus, fibrin loss from microarteriolar thrombi is mediated, at least in part, by leukocyte-associated uPA in a process that requires leukocyte uPAR and PSGL-1.
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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 | 0.001 | 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".