Mechanisms of clearance of nontypeable<i>Haemophilus influenzae</i>from cigarette smoke-exposed mouse lungs
Bibliographic record
Abstract
Inflammation is prevalent in all stages of chronic obstructive pulmonary disease, and, furthermore, individuals undergo periods of exacerbation, during which pulmonary inflammation increases, often a result of bacterial infection. The present study investigates the in vivo consequences of cigarette smoke exposure on bacterial challenge with nontypeable Haemophilus influenzae (NTHi). BALB/c and C57 black 6 (C57BL/6) mice were exposed to cigarette smoke once or twice daily for a total period of 8 weeks. Exacerbated inflammation was observed in cigarette smoke-exposed compared to room-air-exposed mice following challenge with live or heat-inactivated NTHi. Accelerated clearance of live NTHi from cigarette smoke-exposed mice was independent of the establishment of chronic inflammation or direct toxic effects of cigarette smoke components on bacteria. Mechanistically, a cell-free factor in the bronchoalveolar lavage fluid contributed to accelerated clearance following passive transfer to naive mice. Further investigation demonstrated increased titres of immunoglobulin A in the bronchoalveolar lavage fluid, but not the blood, of cigarette smoke-exposed mice, including increased titres of NTHi-specific immunoglobulin A, whereas heavy chain joining element (J(H))(-/-) B-cell-deficient cigarette smoke-exposed mice did not demonstrate decreased bacterial burden following challenge. The present results demonstrate that cigarette smoke exposure results in exacerbated inflammation following challenge with NTHi, as well as increased titres of antibodies that contribute to bacterial clearance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".