Polyphosphate-induced thrombosis in mice is factor XII dependent and is attenuated by histidine-rich glycoprotein
Bibliographic record
Abstract
Histidine-rich glycoprotein (HRG) is an abundant plasma protein that binds factor XIIa (FXIIa) and inhibits factor XII (FXII) autoactivation and FXIIa-mediated activation of FXI. Polyphosphate (polyP), a potent procoagulant released from activated platelets, may serve as a physiological activator of the contact system. Previously, we showed that HRG binds DNA and neutralizes its procoagulant activity. Consequently, our goal was to determine whether the capacity of HRG to bind polyanions enables it to regulate polyP-induced thrombosis. In a plate-based assay, immobilized polyP bound HRG, FXII, and FXIIa in a zinc-dependent manner. Basal and polyP-induced thrombin generation was greater in plasma from HRG-deficient mice than in plasma from wild-type mice. Intraperitoneal injection of polyP shortened the activated partial thromboplastin time, enhanced thrombin generation, increased thrombin-antithrombin levels, reduced lung perfusion, and promoted pulmonary fibrin deposition to a greater extent in HRG-deficient mice than in wild-type mice, effects that were abrogated with FXII knockdown. HRG thus attenuates the procoagulant and prothrombotic effects of polyP in an FXII-dependent manner by modulating the contact system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".