Electronic cigarette vapor increases <i>Streptococcus mutans</i> growth, adhesion, biofilm formation, and expression of the biofilm‐associated genes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 it