Nebulized Anticoagulants Limit Coagulopathy But Not Inflammation in Pseudomonas aeruginosa-Induced Pneumonia in Rats
Bibliographic record
Abstract
Disturbed alveolar fibrin turnover is a characteristic feature of pneumonia. Inhibitors of coagulation could exert lung-protective effects via anticoagulant (inhibiting fibrin deposition) and possibly anti-inflammatory pathways, but could also affect host defense. In this randomized controlled in vivo laboratory study, rats were challenged intratracheally with Pseudomonas aeruginosa, inducing pneumonia, and randomized to local treatment with normal saline (placebo), recombinant human activated protein C (rh-APC), plasma-derived antithrombin (AT), heparin, or danaparoid. Induction of P. aeruginosa pneumonia resulted in activation of pulmonary coagulation and inhibition of pulmonary fibrinolysis, as reflected by increased pulmonary levels of thrombin-AT complexes and fibrin degradation products and decreased pulmonary levels plasminogen activator activity. Pseudomonas aeruginosa pneumonia was accompanied by systemic coagulopathy, since systemic levels of thrombin-AT complexes increased, and systemic levels of plasminogen activator activity decreased. Although rh-APC and plasma-derived AT potently limited pulmonary coagulopathy, neither heparin nor danaparoid affected net pulmonary fibrin turnover. Recombinant human APC also displayed systemic anticoagulant effects. Neither bacterial clearance nor pulmonary inflammation was affected by anticoagulant therapy. Nebulization of rh-APC or plasma-derived AT attenuated pulmonary coagulopathy, but not bacterial clearance or inflammation, in a rat model of P. aeruginosa pneumonia.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
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".