Adjuvant and Anti-Inflammatory Properties of Cigarette Smoke in Murine Allergic Airway Inflammation
Bibliographic record
Abstract
The impact of cigarette smoke on allergic asthma remains controversial both clinically and experimentally. The objective of this study was to investigate, in a murine model, how cigarette smoke affects immune inflammatory processes elicited by a surrogate allergen. In our experimental design, mice were concurrently exposed to cigarette smoke and ovalbumin (OVA), an innocuous antigen that, unless introduced in the context of an adjuvant, induces inhalation tolerance. We show that cigarette smoke exposure has adjuvant properties, allowing for allergic mucosal sensitization to OVA. Specifically, concurrent exposure to cigarette smoke and OVA for 2 weeks led to airway eosinophilia and goblet cell hyperplasia. In vivo OVA recall challenge 1 month after the last smoke exposure showed that concurrent exposure to OVA and cigarette smoke induced antigen-specific memory. Robust eosinophilia and OVA-specific IgG1 and IgE characterized the ensuing inflammatory response. Mechanistically, allergic sensitization was, in part, granulocyte macrophage colony-stimulating factor (GM-CSF) dependent, as a significant reduction in BAL eosinophilia was observed in mice treated with an anti-GM-CSF antibody. Of note, continuous smoke exposure attenuated the OVA recall response; decreased airway eosinophilia was observed in mice continuously exposed to cigarette smoke compared with mice that ceased the smoke exposure protocol. In conclusion, we demonstrate experimentally that while cigarette smoke acts as an adjuvant allowing for allergic sensitization, it also attenuates the ensuing eosinophilic inflammatory response.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".