Coagulation factor XII contributes to hemostasis when activated by soil in wounds
Bibliographic record
Abstract
Bleeding is a common contributor to death and morbidity in animals and provides strong selective pressure for the coagulation system to optimize hemostasis for diverse environments. Although coagulation factor XII (FXII) is activated by nonbiologic surfaces, such as silicates, which leads to blood clotting in vitro, it is unclear whether FXII contributes to hemostasis in vivo. Humans and mice lacking FXII do not appear to bleed more from clean wounds than their counterparts with normal FXII levels. We tested the hypothesis that soil, a silicate-rich material abundant in the environment and wounds of terrestrial mammals, is a normal and potent activator of FXII and coagulation. Blood loss was compared between wild-type (WT) and FXII-knocked out (FXII-/-) mice after soil or exogenous tissue factor was applied to transected tails. The activation of FXII and other components of the coagulation and contact system was assessed with in vitro coagulation and enzyme assays. Soils were analyzed by time-of-flight secondary ionization mass spectrometry and dynamic light scattering. Soil reduced blood loss in WT mice, but not FXII-/- mice. Soil accelerated clotting of blood plasma from humans and mice in a FXII-dependent manner, but not plasma from a cetacean or a bird, which lack FXII. The procoagulant activity of 13 soils strongly correlated with the surface concentration of silicon, but only moderately correlated with the ζ potential. FXII augments coagulation in soil-contaminated wounds of terrestrial mammals, perhaps explaining why this protein has a seemingly minor role in hemostasis in clean wounds.
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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".