Correction of haemorrhagic shock-associated coagulopathy and impaired haemostasis by plasma, prothrombin complex concentrates or an activated protein C-targeted DNA aptamer in mice
Bibliographic record
Abstract
Even with extensive transfusion support, trauma-induced bleeding often leads to death. Early intervention may improve outcomes, yet which blood products, factor concentrates, or other drugs constitute optimal treatment is unclear. Patients with acute traumatic coagulopathy (ATC), arising from trauma and haemorrhagic shock, have the worst prognosis. Here, multiple interventions were compared in a mouse model of ATC. After the trauma of tissue excision, anaesthetized mice were bled to 35 mm Hg mean arterial pressure, maintained under shock for 60 min, and resuscitated with fluids equal in volume to the shed blood. Resuscitated mice were subjected to liver laceration to test haemostasis and blood loss was quantified. Saline-treated mice lost two- to three-fold more blood than sham-treated animals and were coagulopathic by prothrombin time elevation post- versus pre-procedure. Murine fresh-frozen plasma (mFFP), anti-activated protein C aptamer HS02-52G, or prothrombin complex concentrates eliminated the bleeding diathesis and coagulopathy; fibrinogen, plasminogen activator inhibitor-1, or tranexamic acid ameliorated bleeding or coagulopathy, but not both. HS02-52G and mFFP also eliminated the changes in plasma aPC and tissue plasminogen activator levels observed in saline-treated mice, as judged via microtiter plate biomarker assays. Procoagulant interventions, especially inhibiting aPC, could be beneficial in human ATC.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".