Platelet-Activating Factor Receptor Contributes to Host Defense against <i>Pseudomonas aeruginosa</i> Pneumonia but Is Not Essential for the Accompanying Inflammatory and Procoagulant Response
Bibliographic record
Abstract
Pseudomonas aeruginosa is a major cause of nosocomial pneumonia, which is associated with high morbidity and mortality. Because of its ubiquitous nature and its ability to develop resistance to antibiotics, it is a problematic pathogen from a treatment perspective. Platelet-activating factor receptor (PAFR) is involved in phagocytosis of several pathogens. To determine the role of PAFR in the innate immune response to P. aeruginosa pneumonia, pafr gene-deficient (PAFR-/-) mice and normal wild-type (Wt) mice were intranasally inoculated with P. aeruginosa. PAFR deficiency impaired host defense as reflected by increased bacterial outgrowth and dissemination in mice with a targeted deletion of the PAFR gene. PAFR-/- neutrophils showed a diminished phagocytosing capacity of P. aeruginosa in vitro. Relative to Wt mice, PAFR-/- mice demonstrated increased lung inflammation and injury as reflected by histopathology, relative lung weights and total protein concentrations in bronchoalveolar lavage fluid, which was accompanied by higher levels of proinflammatory cytokines in lung homogenates and plasma. In addition, PAFR deficiency was associated with exaggerated local and systemic activation of coagulation as determined by fibrin staining of lung tissue and pulmonary and plasma concentrations of thrombin-antithrombin complexes and D-dimer. These data suggest that PAFR is an essential component of an effective host response to P. aeruginosa pneumonia, at least partly via its contribution to the phagocytic properties of professional granulocytes. Additionally, our results indicate that PAFR signaling is not essential for the induction of a local and systemic inflammatory and procoagulant response to Pseudomonas 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) | 0.000 | 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 | 0.001 |
| 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".