Like Fibrin, (DD)E, the Major Degradation Product of Crosslinked Fibrin, Protects Plasmin from Inhibition by α2-antiplasmin
Bibliographic record
Abstract
Plasmin generation is localized to the fibrin surface because tissue-type plasminogen activator (t-PA) and plasminogen bind to fibrin, an interaction that stimulates plasminogen activation over a hundred-fold. To ensure efficient fibrinolysis, plasmin bound to fibrin is protected from inhibition by alpha2-antiplasmin. (DD)E, a major soluble degradation product of cross-linked fibrin that is a potent stimulator of t-PA, compromises the fibrin-specificity of t-PA by promoting systemic activation of plasminogen. In this study we investigated whether (DD)E also protects plasmin from inhibition by alpha2-antiplasmin, facilitating degradation of this soluble t-PA effector. (DD)E and fibrin reduce the rate of plasmin inhibition by alpha2-antiplasmin by 5- and 10-fold, respectively. Kringle-dependent binding of plasmin to (DD)E and fibrin, with Kd values of 52 and 410 nM, respectively, contributes to the protective effect. When (DD)E is extensively degraded by plasmin, yielding uncomplexed fragment E and (DD), protection of plasmin from inhibition by alpha2-antiplasmin is attenuated. These studies indicate that (DD)E-bound plasmin, whose generation reflects the ability of (DD)E to stimulate plasminogen activation by t-PA, has the capacity to degrade (DD)E by virtue of its resistance to inhibition. This provides a mechanism to limit the concentration of (DD)E and maintain the fibrin-specificity of t-PA.
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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".