Impact of Cigarette Smoke on Clearance and Inflammation after <i>Pseudomonas aeruginosa</i> Infection
Bibliographic record
Abstract
The object of this study was to investigate the impact of cigarette smoke on bacterial clearance and immune inflammatory parameters after infection with Pseudomonas aeruginosa in mice. We observed a delayed rate of bacterial clearance in smoke-exposed compared with sham-exposed mice. This was associated with increased inflammation characterized by greater numbers of neutrophils and mononuclear cells in the bronchoalveolar lavage. After infection, we observed increased levels of proinflammatory cytokines (tumor necrosis factor-alpha, interleukin-1beta, and interleukin-6) and chemokines (monocyte chemoattractant protein-1 [MCP-1] and macrophage inflammatory protein-2 [MIP-2]) as well as myeloperoxidase and proteolytic activity in the lungs of smoke-exposed compared with sham-exposed animals. Delayed clearance was associated with increased morbidity and greater weight loss of smoke-exposed mice. After delivery of inactivated bacteria, we observed a similar inflammatory response, clinical score, and tumor necrosis factor-alpha expression in smoke- and sham-exposed animals, suggesting that increased inflammation and altered clinical presentation are due to the delayed rate of bacterial clearance. Our findings suggest that cigarette smoke affects respiratory immune-inflammatory responses elicited by bacteria. We postulate that altered respiratory host defense may be implicated in smoking-related diseases such as chronic obstructive pulmonary disease.
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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".