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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".