Electronic cigarette vapor increases <i>Streptococcus mutans</i> growth, adhesion, biofilm formation, and expression of the biofilm‐associated genes
Bibliographic record
Abstract
OBJECTIVE: It still not known whether electronic cigarettes (e-cigarettes) contribute to dental caries. This study aimed to evaluate the effect of e-cigarettes on the growth of Streptococcus mutans, the formation of biofilm, and the expression of certain virulence genes. MATERIALS AND METHODS: Streptococcus mutans cells were exposed or not to e-cigarettes with and without nicotine or to cigarette smoke twice a day for 15 min each exposure period. The bacterial growth and the expression of glucosyltranferase, competence, and glucan-binding genes were evaluated after 24 hr. Biofilm formation was assessed after 1, 2, and 3 days. S. mutans adhesion and growth to e-cigarette exposed human teeth were assessed. RESULTS: We observed an increase in S. mutans growth with e-cigarettes, mainly at the early culture period. This was confirmed by an increase of biofilm mass ranging from 8 ± 0.5 mg with the control to 47 ± 5 mg after six exposures to nicotine-rich e-cigarettes. S. mutans cells adhered better to e-cigarette exposed teeth. E-cigarettes increased the expression of glucosyltranferase, competence, and glucan-binding genes. CONCLUSIONS: E-cigarettes increased the growth of S. mutans and the expression of virulent genes. E-cigarettes promoted the adhesion to, and formation of biofilms on teeth surfaces.
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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) | 0.005 | 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".