Zinc delays clot lysis by attenuating plasminogen activation and plasmin-mediated fibrin degradation
Bibliographic record
Abstract
Zinc circulates free in plasma at a concentration of 0.1-2 µM, but its levels increase locally when it is released from activated platelets. Although zinc influences many processes in haemostasis, its effect on fibrinolysis has not been thoroughly investigated. Using a fluorescent zinc-binding probe, we demonstrated that zinc binds tissue-type plasminogen activator (tPA) and plasmin with high affinity (Kd values of 0.2 µM), and surface plasmon resonance studies revealed that zinc binds fibrin with a Kd of 12.8 µM. Zinc had no effect on the affinity of plasminogen or plasmin for fibrin, but increased the affinity of tPA by two-fold. In the presence of 5 µM zinc, the catalytic efficiency of plasminogen activation by tPA was reduced by approximately two-fold, both in the absence or presence of fibrin. Zinc attenuated plasmin-mediated degradation of the fibrinogen alpha-chain by 43 %, but had no effect on trypsin degradation. tPA-mediated fibrin clot lysis was prolonged 2.5-fold by zinc in a concentration-dependent fashion, and tPA-mediated plasma clot lysis was attenuated by 1.5-fold. Therefore, our data indicate that zinc modulates fibrinolysis by attenuating tPA-mediated plasminogen activation and plasmin-induced fibrin degradation. These findings suggest that local release of zinc by platelets attenuates fibrinolysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".