Therapeutic Recombinant Murine Activated Protein C Attenuates Pulmonary Coagulopathy and Improves Survival in Murine Pneumococcal Pneumonia
Bibliographic record
Abstract
BACKGROUND: Recombinant human activated protein C (APC) improves survival of patients with severe sepsis; this beneficial effect is especially apparent in patients with pneumococcal pneumonia. The aim of this study was to determine the effect of APC treatment initiated after induction of pneumococcal pneumonia on pulmonary coagulation, inflammation, and survival, with or without concurrent antibiotic therapy. METHODS: Mice were infected intranasally with viable Streptococcus pneumoniae and were treated intraperitoneally after 24 h of infection with vehicle, recombinant mouse (rm) APC (125 μg), ceftriaxone (500 μg), or rm-APC plus ceftriaxone. Treatment with rm-APC or vehicle was repeated every 8 h for a maximum of 96 h. Animals were either killed 48 h after infection or were monitored in a survival study (with an extra dose of ceftriaxone given after 72 h). RESULTS: Rm-APC treatment inhibited pulmonary activation of coagulation, as reflected by lower levels of thrombin-antithrombin complexes and D-dimer. Rm-APC did not affect the pulmonary levels of 55 inflammatory mediators in the context of antibiotic therapy. Rm-APC added to ceftriaxone markedly improved survival, compared with ceftriaxone treatment alone. CONCLUSIONS: Rm-APC inhibits pulmonary activation of coagulation and, when added to antibiotic therapy, improves survival in murine pneumococcal pneumonia.
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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 | 0.000 |
| 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 | 0.001 |
| 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".