Effect of Electronic Cigarettes on Oral Microbial Flora
Bibliographic record
Abstract
Background: Despite well-documented adverse effects of tobacco consumption, cigarettes use is still rising and part of this increase is related to the popularization of alternative electronic nicotine delivery devices, such as electronic cigarettes (ECs). The aim of the performed research was to assess the effect of electronic cigarettes aerosol on the oral microbiota, using culture methods. Methodology: 30 ten-week-old WAG rats (female 76-81 g and male 86-94 g) were randomly distributed in two groups, as follows: Group 1 – control animals (n = 10); Group 2 – EC aerosol exposed (n = 20). EC aerosol exposures were carried out by using the Boyarchuck chamber. During the study, the rat oral microbiota were collected four times: at the beginning of the experiment, on the 30th, 60th and 90th days. Microorganisms were identified using standard microbiological techniques. Results: EC exposure to Group 2 rats resulted in a depletion of colonies commensal microbes and a greater incidence of atypical species such as Klebsiella pneumoniae, Acinetobacter lwoffii, Candida albicans compared to Group 1 on day 90. The test of independence between frequency distibution of opportunistic microbes and duration of EC exposure showed a significance for Klebsiella pneumoniae – χ2= 8.017, p=0.0456, Acinetobacter lwoffii – χ2= 36.772, p=0.0001, and Candida albicans – χ2=8.689, p=0.0337. Conclusions: The impact of electronic cigarettes facilitated colonization of the oral cavity by opportunistic bacteria and yeast.
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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) | 0.003 | 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".