Genotoxic profile among e-cigarette consumers, cigarette smokers, and nonsmokers
Bibliographic record
Abstract
It is widely believed that e-cigarettes are less harmful than conventional cigarettes because they have a lower nicotine content. In contrast to this notion, several in vitro studies have evaluated and demonstrated the genotoxicity associated with e-cigarette smoking. However, there is a lack of human studies on the genotoxicity of e-cigarettes. This pilot study evaluated and compared indicators of genotoxicity in e-cigarette users, cigarette smokers, and nonsmokers. A total of 84 healthy participants, including 20 e-cigarette users, 31 cigarette smokers, and 33 nonsmokers, were recruited. Genotoxicity was evaluated by measuring tail moment (TM), tail length (TL), and % tail DNA intensity (%T) using the comet assay as an indicator of DNA damage in blood and detecting micronuclei in buccal cells with the buccal micronucleus (MN) cytome assay. Bivariate analyses showed that there was no significant difference in TM and TL between e-cigarette users and cigarette smokers, but in both groups, the three parameters were significantly higher than that in nonsmokers (p<0.02). In contrast, the frequency of micronuclei in e-cigarette users (40%) was higher than that in cigarette smokers (27.5%). Our findings indicate that e-cigarettes have a similar genotoxic effect to regular cigarettes and, therefore, contradict the notion that e-cigarettes are safer than regular cigarettes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".