Exposure to nicotine-free and flavor-free e-cigarette vapors modifies the pulmonary response to tobacco cigarette smoke in female mice
Bibliographic record
Abstract
Most of electronic cigarette (e-cigarette) users are also smoking tobacco cigarettes. Because of the relative novelty of this habit, very little is known on the impact of vaping on pulmonary health, even less on the potential interactions of dual e-cigarette and tobacco cigarette use. Therefore, we used well-established mouse models to investigate the impact of dual exposure to e-cigarette vapors and tobacco cigarette smoke on lung homeostasis. Groups of female BALB/c mice were exposed to room air, tobacco smoke only, nicotine-free flavor-free e-cigarette vapors only or both tobacco smoke and e-cigarette vapors. Moreover, since tobacco smoke and electronic cigarette vapors both affect circadian processes in the lungs, groups of mice were euthanized at two different time points during the day. We found that dual-exposed mice had altered lung circadian gene expression compared with mice exposed to tobacco smoke alone. Dual-exposed mice also had different frequencies of dendritic cells, macrophages, and neutrophils in the lung tissue compared with mice exposed to tobacco smoke alone, an observation also valid for B-lymphocytes and CD4+ and CD8+ T lymphocytes. Exposure to e-cigarette vapors also impacted the levels of immunoglobulins in the bronchoalveolar lavage and serum. Finally, e-cigarette and dual exposures increased airway resistance compared with mice exposed to room air or tobacco smoke alone, respectively. Taken together, these data suggest that e-cigarette vapors, even without nicotine or flavors, could affect how the lungs react to tobacco cigarette smoke exposure in dual users, potentially altering the pathological course triggered by smoking.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".