A Kinetic Analysis of the Tissue Plasminogen Activator and DSPAα1 Cofactor Activities of Untreated and TAFIa-treated Soluble Fibrin Degradation Products of Varying Size
Bibliographic record
Abstract
The kinetics of tissue plasminogen activator (t-PA) and DSPAalpha1-catalyzed plasminogen activation using untreated and TAFIa-treated fibrin degradation products (FDPs), ranging in weight average molecular weight (M(w)) from 0.48 x 10(6) to 4.94 x 10(6) g/mol, were modeled according to the steady-state template model. The FDPs served as effective cofactors for both activators. The intrinsic catalytic efficiencies of both t-PA (17.4 x 10(5) m(-1) s(-1)) and DSPAalpha1 (6.0 x 10(5) m(-1) s(-1)) were independent of FDP M(w). The intrinsic catalytic efficiency of t-PA was 12-fold higher than that measured under identical conditions with intact fibrin as the cofactor. At sub-saturating levels of cofactor and substrate, rates were strongly dependent on FDP M(w) with DSPAalpha1 but not t-PA. Loss of activity with decreasing FDP M(w) correlated with loss of finger-dependent binding of the activators to the FDPs. TAFIa treatment of the FDPs resulted in 90- and 215-fold decreases in the catalytic efficiencies of t-PA (0.20 x 10(5) m(-)(1) s(-1)) and DSPAalpha1 (0.028 x 10(5) m(-1) s(-1)), yielding cofactors that were still 30- and 50-fold better than fibrinogen with t-PA and DSPAalpha1, respectively. Our results show that for both activators the products released during fibrinolysis are very effective cofactors for plasminogen activation, and both t-PA and DSPAalpha1 cofactor activity are strongly down-regulated by TAFIa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".