Thrombin-thrombomodulin connects coagulation and fibrinolysis: more than an in vitro phenomenon
Bibliographic record
Abstract
Thrombin activatable fibrinolysis inhibitor (TAFI), when activated, forms a basic carboxypeptidase that can inhibit fibrinolysis. Potential physiologic activators include both thrombin and plasmin. In vitro, thrombomodulin and glycosaminoglycans increase the catalytic efficiency of TAFI activation by thrombin and plasmin, respectively. The most relevant (patho-) physiologic activator of TAFI has not been disclosed. Our purpose was to identify the physiologic activator of TAFI in vivo. Activation of protein C (a thrombin-thrombomodulin-dependent reaction), prothrombin, and plasminogen occurs during sepsis. Thus, a baboon model of Escherichia coli-induced sepsis, where multiple potential activators of TAFI are elaborated, was used to study TAFI activation. A monoclonal antibody (mAbTAFI/TM#16) specifically inhibiting thrombin-thrombomodulin-dependent activation of TAFI was used to assess the contribution of thrombin-thrombomodulin in TAFI activation in vivo. Coinfusion of mAbTAFI/TM#16 with a lethal dose of E coli prevented the complete consumption of TAFI observed without mAbTAFI/TM#16. The rate of fibrin degradation products formation is enhanced in septic baboons treated with the mAbTAFI/TM#16; therefore, TAFI activation appears to play a key role in the extent of fibrin(ogen) consumption during E coli challenge, and thrombin-thrombomodulin, in a baboon model of E coli-induced sepsis, appears to be the predominant activator of TAFI.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".