Comparison of two in vitro models of cigarette smoke exposure
Bibliographic record
Abstract
Cigarette smoke is associated with a high morbidity and mortality, and affects particularly the respiratory tract. Various in vitro models have been developed to study the effects of cigarette smoke on bronchial epithelial cells. To identify an adequate exposure model of cigarette smoke, we analysed the effects of cigarette smoke extract (CSE) and a smoking chamber on bronchial epithelial cells. The release of monocyte chemoattractant protein (MCP)-1, interleukin (IL)-10, and vascular endothelial growth factor (VEGF) was measured. Bronchial epithelial cells isolated from Sprague-Dawley rat (NRBE) were exposed to 3% CSE or air control every day for 3 days. In the second model, NRBE were placed in an air/liquid interface and exposed, in a smoking chamber, to whole smoke from 2 cigarettes, twice daily for 3 days. Levels of MCP-1, IL-10, and VEGF were measured by enzyme-linked immunosorbent assay (ELISA), 24 h after the last exposure. The pattern of MCP-1 production by bronchial epithelial cells was different between the two models. MCP-1 release was increased after 3 days of exposure in the CSE model, but was inhibited using the smoking chamber model. Production of IL-10 by NRBE was reduced after 3 days in both models. Finally, no difference was observed in the production of VEGF between the two models. CSE and the smoking chamber differently modulate bronchial epithelial cell mediator production, demonstrating that the model of cigarette smoke exposure used can influence the data obtained.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".