Factor V Leiden mutation does not affect coagulopathy or outcome in lethal H1N1 influenza
Bibliographic record
Abstract
Influenza A is a major cause of mortality. Knowledge on coagulation activation in influenza infection is limited. The factor V Leiden (FVL) mutation is possibly subject to positive selection pressure. It is unknown whether this mutation impacts on the outcome of severe influenza. In the present study, the effect of lethal influenza on pulmonary and systemic coagulation activation and whether or not FVL mutation alters coagulation activation in and the course of lethal influenza, was determined. Wild-type mice, and mice heterozygous or homozygous for FVL were infected intranasally with a lethal dose of H1N1 (haemagglutinin 1 and neuraminidase 1) influenza A. Mice were sacrificed after 48 or 96 h for determination of coagulation activation, histopathology, pulmonary inflammatory parameters and viral load, or were observed in a survival study. Extensive local and systemic coagulation activation during lethal influenza was demonstrated by increased lung and plasma levels of thrombin-antithrombin complexes and fibrin degradation products, and by pulmonary fibrin deposition. FVL mutation did not influence the procoagulant response, lung histopathology or survival. FVL mice demonstrated elevated viral loads 48 h after infection. In conclusion, coagulation is activated locally and systemically during lethal murine influenza A infection. The FVL mutation does not influence coagulation activation, lung inflammation or survival in lethal influenza A.
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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".