Cigarette Smoke Primes the Pulmonary Environment to IL-1α/CXCR-2–Dependent Nontypeable <i>Haemophilus influenzae</i>–Exacerbated Neutrophilia in Mice
Bibliographic record
Abstract
Cigarette smoke has a broad impact on the mucosal environment with the ability to alter host defense mechanisms. Within the context of a bacterial infection, this altered host response is often accompanied by exacerbated cellular inflammation, characterized by increased neutrophilia. The current study investigated the mechanisms of neutrophil recruitment in a murine model of cigarette smoke exposure and, subsequently, a model of both cigarette smoke exposure and bacterial infection. We investigated the role of IL-1 signaling in neutrophil recruitment and found that cigarette smoke-induced neutrophilia was dependent on IL-1α produced by alveolar macrophages. In addition to being the crucial source of IL-1α, alveolar macrophages isolated from smoke-exposed mice were primed for excessive IL-1α production in response to bacterial ligands. To test the relevance of exaggerated IL-1α production in neutrophil recruitment, a model of cigarette smoke exposure and nontypeable Haemophilus influenzae infection was developed. Mice exposed to cigarette smoke elaborated an exacerbated CXCR2-dependent neutrophilia in response to nontypeable Haemophilus influenzae. Exacerbated neutrophilia was dependent on IL-1α priming of the pulmonary environment by cigarette smoke as exaggerated neutrophilia was dependent on IL-1 signaling. These data characterize a novel mechanism of cigarette smoke priming the lung mucosa toward greater IL-1-driven neutrophilic responses to bacteria, with a central role for the alveolar macrophage in this process.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".