Prothrombin, alone or in complex concentrates or plasma, reduces bleeding in a mouse model of blood exchange-induced coagulopathy
Bibliographic record
Abstract
Prothrombin complex concentrates (PCC) are fractionated plasma protein drugs that reverse warfarin anticoagulation. PCC may control more general bleeding. We sought to identify the dominant procoagulant factor in PCC in vivo. We tested PCC or coagulation factor (F) treatment in CD1 mice made coagulopathic by exchange of whole blood for washed red cells. Anesthetized mice were transfused with murine fresh-frozen plasma (mFFP), PCC, mixtures of human vitamin K-dependent proteins (VKDP) (prothrombin, FVII, FIX, or FX), or purified single human VKDP, immediately prior to tail transection (TT), liver laceration (LL), or intravascular laser injury (ILI). Plasma donor mice were treated with vehicle or control antisense oligonucleotide (ASO-CON) or ASO specific for prothrombin (FII) (ASO-FII) to yield mFFP or ASO-CON mFFP or ASO-FII mFFP. Blood losses were determined spectrophotometrically (TT) or gravimetrically (LL). Thrombus formation was quantified by intravital microscopy of laser-injured arterioles. PCC or four factor- (4F-) VKDP or prothrombin significantly reduced bleeding from TT or LL. Omission of prothrombin from 4F-VKDP significantly reduced its ability to limit bleeding. Mice transfused with ASO-FII mFFP demonstrated inferior haemostasis versus those transfused with ASO-FII following TT, LL, or ILI. Prothrombin is the dominant procoagulant component of PCC and could limit bleeding in trauma.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".