Effect of Violet-Blue Light on Streptococcus mutans-Induced Enamel Demineralization
Bibliographic record
Abstract
Background: This in vitro study determined the effectiveness of violet-blue light (405 nm) on inhibiting Streptococcus mutans-induced enamel demineralization. Materials and Methods: S. mutans UA159 biofilm was grown on human enamel specimens for 13 h in 5% CO2 at 37 °C with/without 1% sucrose. Wet biofilm was treated twice daily with violet-blue light for five minutes over five days. A six-hour reincubation was included daily between treatments excluding the final day. Biofilms were harvested and colony forming units (CFU) were quantitated. Lesion depth (L) and mineral loss (∆Z) were quantified using transverse microradiography (TMR). Quantitative light-induced fluorescence Biluminator (QLF-D) was used to determine mean fluorescence loss. Data were analyzed using one-way analysis of variance (ANOVA) to compare differences in means. Results: The results demonstrated a significant reduction in CFUs between treated and non-treated groups grown with/without 1% sucrose. ∆Z was significantly reduced for specimens exposed to biofilms grown without sucrose with violet-blue light. There was only a trend on reduction of ∆Z with sucrose and with L on both groups. There were no differences in fluorescence-derived parameters between the groups. Conclusions: Within the limitations of the study, the results indicate that violet-blue light can serve as an adjunct prophylactic treatment for reducing S. mutans biofilm formation and enamel mineral loss.
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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".