Thrombin-activable Fibrinolysis Inhibitor Attenuates (DD)E-mediated Stimulation of Plasminogen Activation by Reducing the Affinity of (DD)E for Tissue Plasminogen Activator
Bibliographic record
Abstract
A complex of d-dimer noncovalently associated with fragment E ((DD)E), a degradation product of cross-linked fibrin that binds tissue plasminogen activator (t-PA) and plasminogen (Pg) with affinities similar to those of fibrin, compromises the fibrin specificity of t-PA by stimulating systemic Pg activation. In this study, we examined the effect of thrombin-activable fibrinolysis inhibitor (TAFI), a latent carboxypeptidase B (CPB)-like enzyme, on the stimulatory activity of (DD)E. Incubation of (DD)E with activated TAFI (TAFIa) or CPB (a) produces a 96% reduction in the capacity of (DD)E to stimulate t-PA-mediated activation of Glu- or Lys-Pg by reducing k(cat) and increasing K(m) for the reaction; (b) induces the release of 8 mol of lysine/mol of (DD)E, although most of the stimulatory activity is lost after release of only 4 mol of lysine/mol (DD)E; and (c) reduces the affinity of (DD)E for Glu-Pg, Lys-Pg, and t-PA by 2-, 4-, and 160-fold, respectively. Because TAFIa- or CPB-exposed (DD)E produces little stimulation of Glu-Pg activation by t-PA, (DD)E is not degraded into fragment E and d-dimer, the latter of which has been reported to impair fibrin polymerization. These data suggest a novel role for TAFIa. By attenuating systemic Pg activation by (DD)E, TAFIa renders t-PA more fibrin-specific.
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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 | 0.000 |
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".