Smoking status impacts treatment efficacy in smoke-induced lung inflammation: A pre-clinical study
Bibliographic record
Abstract
Rationale:Smoking status and smoking history remain poorly accounted for as variables that could affect the efficacy of new drugs being tested in chronic obstructive pulmonary disease (COPD) patients. As a proof of concept, we used a pre-clinical model of cigarette smoke (CS) exposure to compare the impact of treatment during active CS exposure or during the cessation period on the anti-inflammatory effects IL-1α signaling blockade. Methods:Mice were exposed to CS for 2 weeks, followed by a 1-week cessation, then acutely re-exposed for 2 days. Mice were treated with an anti-IL-1α antibody either during CS exposure or during cessation and inflammatory outcomes were assessed. Results:We found that mice re-exposed to CS displayed reduced neutrophil counts and cytokine levels in the bronchoalveolar lavage (BAL) compared to mice exposed only acutely. Moreover, we found that treatment with an anti-IL-1α antibody during the initial CS exposure delayed inflammatory processes and interfered with pulmonary adaptation, leading to rebound pulmonary neutrophilia, increased BAL cytokine secretion (CCL2) and upregulatedMmp12expression. Conversely, administration of anti-IL-1α during cessation had the opposite effect, improving BAL neutrophilia, decreasing CCL2 levels and reducingMmp12expression. Discussion:These results suggest that pulmonary adaptation to CS exposure dampens inflammation and blocking IL-1α signaling during CS exposure delays the inflammatory response. More importantly, the same treatment administered during cessation hastens the return to pulmonary inflammatory homeostasis, strongly suggesting that smoking status and treatment timing should be considered when testing new biologics in COPD.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".