Exposure to electronic cigarette vapors affects pulmonary and systemic expression of circadian molecular clock genes
Bibliographic record
Abstract
E-cigarette use has exploded in the past years, especially among young adults and smokers desiring to quit. While concerns are mostly based on the presence of nicotine and flavors, pulmonary effects of propylene glycol and glycerol inhalation, the main solvents of e-liquid have not been thoroughly investigated. In this preclinical study, mice were exposed 2 h daily for up to 8 weeks to vapors of propylene glycol and/or glycerol generated by an e-cigarette. Lung transcriptome analysis revealed it affected the expression level of genes of the circadian molecular clock, despite causing no inflammatory response. Periodical sacrifices showed that the rhythmicity of these regulatory genes was indeed altered in the lungs, but also in the liver, kidney, skeletal muscle, and brain. E-cigarette exposure also altered the expression of rhythmic genes (i.e., hspa1a and hspa1b), suggesting that alterations to the ‘clock genes’ could translate into systemic biological alterations. This study reveals that the major solvents used in e-cigarettes propylene glycol and glycerol, not nicotine or flavors, have unsuspected effects on gene expression of the molecular clock that are to be taken seriously, especially considering the fundamental role of the circadian rhythm in health and disease.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| 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".