Arterial thrombosis is accelerated in mice deficient in histidine-rich glycoprotein
Bibliographic record
Abstract
Factor (F) XII, a key component of the contact system, triggers clotting via the intrinsic pathway, and is implicated in propagating thrombosis. Although nucleic acids are potent activators, it is unclear how the contact system is regulated to prevent uncontrolled clotting. Previously, we showed that histidine-rich glycoprotein (HRG) binds FXIIa and attenuates its capacity to trigger coagulation. To investigate the role of HRG as a regulator of the intrinsic pathway, we compared RNA- and DNA-induced thrombin generation in plasma from HRG-deficient and wild-type mice. Thrombin generation was enhanced in plasma from HRG-deficient mice, and accelerated clotting was restored to normal with HRG reconstitution. Although blood loss after tail tip amputation was similar in HRG-deficient and wild-type mice, carotid artery occlusion after FeCl3 injury was accelerated in HRG-deficient mice, and HRG administration abrogated this effect. To confirm that HRG modulates the contact system, we used DNase, RNase, and antisense oligonucleotides to characterize the FeCl3 model. Whereas DNase or FVII knockdown had no effect, carotid occlusion was abrogated with RNase or FXII knockdown, confirming that FeCl3-induced thrombosis is triggered by RNA in a FXII-dependent fashion. Therefore, in a nucleic acid-driven model, HRG inhibits thrombosis by modulating the intrinsic pathway of coagulation.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".