Histidine-rich glycoprotein binds factor XIIa with high affinity and inhibits contact-initiated coagulation
Bibliographic record
Abstract
Histidine-rich glycoprotein (HRG) circulates in plasma at a concentration of 2μM and binds plasminogen, fibrinogen, and thrombospondin. Despite these interactions, the physiologic role of HRG is unknown. Previous studies have shown that mice and humans deficient in HRG have shortened plasma clotting times. To better understand this phenomenon, we examined the effect of HRG on clotting tests. HRG prolongs the activated partial thromboplastin time in a concentration-dependent fashion but has no effect on tissue factor-induced clotting, localizing its effect to the contact pathway. Plasma immunodepleted of HRG exhibits a shortened activated partial thromboplastin time that is restored to baseline with HRG replenishment. To explore how HRG affects the contact pathway, we examined its binding to factors XII, XIIa, XI, and XIa. HRG binds factor XIIa with high affinity, an interaction that is enhanced in the presence of Zn²(+), but does not bind factors XII, XI, or XIa. In addition, HRG inhibits autoactivation of factor XII and factor XIIa-mediated activation of factor XI. These results suggest that, by binding to factor XIIa, HRG modulates the intrinsic pathway of coagulation, particularly in the vicinity of a thrombus where platelet release of HRG and Zn²(+) will promote this interaction.
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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".