Functional analysis of fibrin γ-chain cross-linking by activated factor XIII: determination of a cross-linking pattern that maximizes clot stiffness
Bibliographic record
Abstract
Activated coagulation factor XIII (FXIIIa) cross-links the gamma-chains of fibrin early in clot formation. Cross-linking of the alpha-chains occurs more slowly, leading to high molecular weight multimer formations that can also contain gamma-chains. To study the contribution of FXIIIa-induced gamma-chain cross-linking on fibrin structure and function, we created 2 recombinant fibrinogens (gammaQ398N/Q399N/K406R and gammaK406R) that modify the gamma-chain cross-linking process. In gammaK406R, gamma-dimer cross-links were absent, but FXIIIa produced a cross-linking pattern similar to that observed in tissue transglutaminase cross-linked fibrin(ogen) with mainly alpha-gamma cross-links. In Q398N/Q399N/K406R, cross-links with any gamma-chain involvement were completely absent, and only alpha-chain cross-linking occurred. Upon cross-linking, recombinant normal fibrin yielded a 3.5-fold increase in stiffness, compared with a 2.5-fold increase by alpha-chain cross-linking alone (gammaQ398N/Q399N/K406R). gammaK406R fibrin showed a 1.5-fold increase in stiffness after cross-linking. No major differences in clot morphology, polymerization, and lysis rates were observed, although fiber diameter was slightly lower in cross-linked normal fibrin relative to the variants. Our results show that gamma-chain cross-linking contributes significantly to clot stiffness, in particular through gamma-dimer formation; alpha-gamma hybrid cross-links had the smallest impact on clot stiffness.
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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".