Impact of Endogenous Protein C on Pulmonary Coagulation and Injury during Lethal H1N1 Influenza in Mice
Bibliographic record
Abstract
Influenza accounts for 5-10% of community-acquired pneumonia cases, and is a major cause of mortality. Sterile and bacterial lung injury are associated with procoagulant and inflammatory derangements in the lungs and down-regulation of the protein C (PC) pathway has been correlated with disease severity and mortality in severe bacterial pneumonia and sepsis. In addition, during lethal influenza pneumonia, pulmonary and systemic coagulation are activated, which can be attenuated by the administration of recombinant activated (A) PC. We here determined the role of endogenous PC in lethal H1N1 influenza A infection. Male C57BL/6 mice pretreated with an inhibitory monoclonal antibody directed against murine PC or a control antibody were intranasally infected with a lethal dose of a mouse-adapted H1N1 influenza A strain. Mice were killed at 48 or 96 hours after infection, after which lungs and bronchoalveolar lavage fluid were harvested, or observed for up to 9 days. Anti-PC antibody treatment aggravated pulmonary activation of coagulation as compared with control antibody treatment, as reflected by increased lung concentrations of thrombin-antithrombin complexes and fibrin degradation products, as well as intravascular thrombus formation. Anti-PC antibody treatment aggravated lung histopathology, but lowered bronchoalveolar neutrophil influx and total protein levels, and delayed mortality. In conclusion, endogenous PC has strong effects on the host response to lethal influenza A infection, inhibiting pulmonary coagulopathy and inflammation on the one hand, but facilitating neutrophil influx and protein leak and accelerating mortality on the other hand.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".