Investigating the effect of pretreatment with azithromycin on inflammatory mediators in bronchial epithelial cells exposed to cigarette smoke
Bibliographic record
Abstract
Purpose of the study: Macrolide therapy is effective in reducing chronic obstructive pulmonary disease (COPD) exacerbations. Our recent study has shown the effectiveness of taking azithromycin in COPD patients, not only ex-smokers but also current smokers. Beyond their anti-microbial effects, macrolides have anti-inflammatory and immunomodulatory properties. The aim of this study was to determine if pretreatment with azithromycin modulates cigarette smoke-induced inflammation in airway epithelial cells. We hypothesized that pretreatment with azithromycin decreases exacerbation frequency by modulating inflammation in human airway epithelial cells exposed to cigarette smoke.Materials and methods: BEAS-2B bronchial epithelial cells were incubated with 5% cigarette smoke extract (CSE) for 3 h, 6 h, and 24 h. Then, airway epithelial cells were pretreated with azithromycin and exposed to 5% CSE. In each stage, the expression and release of IL-6 and IL-8 mRNA were analyzed by quantitative real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA), respectively.Results: There was a significant increase of IL-6 and IL-8 mRNA, as well as an increase in extracellular IL-8 protein following exposure to 5% CSE. When cells were pretreated with azithromycin and exposed to 5% CSE for 3 h, there was a significant dose-dependent decrease in the expression of IL-6 mRNA. A final concentration of 9 µg/mL of azithromycin was sufficient to decrease IL-6, IL-8 mRNA, and extracellular IL-8 levels.Conclusion: Pretreatment with azithromycin decreased the expression of IL-6 and IL-8 mRNA and the release of IL-8 in bronchial epithelial cells exposed to cigarette smoke. These results demonstrate the direct effect of azithromycin on inflammatory mediators in bronchial epithelial cells exposed to cigarette smoke.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".