Regulation of fibrinolysis by S100A10 in vivo
Bibliographic record
Abstract
Endothelial cells form the inner lining of vascular networks and maintain blood fluidity by inhibiting blood coagulation and promoting blood clot dissolution (fibrinolysis). Plasmin, the primary fibrinolytic enzyme, is generated by the cleavage of the plasma protein, plasminogen, by its activator, tissue plasminogen activator. This reaction is regulated by plasminogen receptors at the surface of the vascular endothelial cells. Previous studies have identified the plasminogen receptor protein S100A10 as a key regulator of plasmin generation by cancer cells and macrophages. Here we examine the role of S100A10 and its annexin A2 binding partner in endothelial cell function using a homozygous S100A10-null mouse. Compared with wild-type mice, S100A10-null mice displayed increased deposition of fibrin in the vasculature and reduced clearance of batroxobin-induced vascular thrombi, suggesting a role for S100A10 in fibrinolysis in vivo. Compared with wild-type cells, endothelial cells from S100A10-null mice demonstrated a 40% reduction in plasminogen binding and plasmin generation in vitro. Furthermore, S100A10-deficient endothelial cells demonstrated impaired neovascularization of Matrigel plugs in vivo, suggesting a role for S100A10 in angiogenesis. These results establish an important role for S100A10 in the regulation of fibrinolysis and angiogenesis in vivo, suggesting S100A10 plays a critical role in endothelial cell function.
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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".