Effect of Violet-Blue Light on Streptococcus mutans-Induced Enamel Demineralization
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
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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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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