Zinc promotes clot stability by accelerating clot formation and modifying fibrin structure
Bibliographic record
Abstract
Zinc released from activated platelets binds fibrin(ogen) and attenuates fibrinolysis. Although zinc also affects clot formation, the mechanism and consequences are poorly understood. To address these gaps, the effect of zinc on clot formation and structure was examined in the absence or presence of factor (F) XIII. Zinc accelerated a) plasma clotting by 1.4-fold, b) fibrinogen clotting by 3.5- and 2.3-fold in the absence or presence of FXIII, respectively, c) fragment X clotting by 1.3-fold, and d) polymerisation of fibrin monomers generated with thrombin or batroxobin by 2.5- and 1.8-fold, respectively. Whereas absorbance increased up to 3.3-fold when fibrinogen was clotted in the presence of zinc, absorbance of fragment X clots was unaffected by zinc, consistent with reports that zinc binds to the αC-domain of fibrin(ogen). Scanning electron microscopic analysis revealed a two-fold increase in fibre diameter in the presence of zinc and in permeability studies, zinc increased clot porosity by 30-fold with or without FXIII. Whereas FXIII increased clot stiffness from 128 ± 19 Pa to 415 ± 27 Pa in rheological analyses, zinc reduced clot stiffness by 10- and 8.5-fold in the absence and presence of FXIII, respectively. Clots formed in the presence of zinc were more stable and resisted rupture with or without FXIII. Therefore, zinc accelerates clotting and reduces fibrin clot stiffness in a FXIII-independent manner, suggesting that zinc may work in concert with FXIII to modulate clot strength and stability.
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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".