Activated Thrombin-activatable Fibrinolysis Inhibitor Reduces the Ability of High Molecular Weight Fibrin Degradation Products to Protect Plasmin from Antiplasmin
Bibliographic record
Abstract
Activated thrombin-activable fibrinolysis inhibitor (TAFIa) is a carboxypeptidase B-like plasma enzyme that can slow clot lysis by removing lysine residues exposed on fibrin as it is cleaved by plasmin. Previously, it was shown that fibrin treated with TAFIa is less able to promote plasminogen activation by tissue-type plasminogen activator. In this study, the effect of TAFIa modification of a fibrin surface on the rate of plasmin inhibition by antiplasmin was studied using high molecular weight fibrin degradation products (HMw-FDPs) as a soluble model for intact plasmin-modified fibrin. To quantify the inhibition, a novel end point assay was employed where plasmin, antiplasmin, and cofactors were mixed in the presence of a chromogenic substrate and the end point in the substrate hydrolysis reaction was used to measure the second order rate constant of inhibition. When HMw-FDPs were titrated in the presence of plasmin and antiplasmin, the rate constant for inhibition decreased by 16-fold at saturation (9.6 x 10(6) m(-1) s(-1) to 0.59 x 10(6) m(-1) s(-1)). When HMw-FDPs were pretreated with TAFIa, nearly two-thirds of the protective effect was lost. When 730 nm HMw-FDPs were treated for 20 min with TAFIa, the rate constant for plasmin inhibition was increased 3-fold from 1.9 x 10(6) m(-1) s(-1) to 6.2 x 10(6) m(-1) s(-1). Therefore, a novel mechanism was identified whereby TAFIa can modulate plasmin levels by increasing the susceptibility of plasmin to inhibition by antiplasmin.
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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".