Enamel Remineralization Competence of a Novel Fluoride-Incorporated Bioactive Glass Toothpaste—A Surface Micro-Hardness, Profilometric, and Micro-Computed Tomographic Analysis
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Bibliographic record
Abstract
This study aimed to analyze the enamel remineralization efficacy of a novel fluoridated bioactive glass (F-BG) toothpaste compared to a standard fluoride toothpaste. Seventy-two enamel blocks (N = 72) were divided into groups of twenty-four blocks according to the toothpaste exposure—group 1: brushed with distilled water, group 2: brushed with fluoride toothpaste (ColgateTM), and group 3: brushed with F-BG toothpaste (BioMinFTM). Pre-brushing, enamel blocks were demineralized using 6 wt.% citric acid (pH = 2.4). Tooth brushing was performed using a mixture of respective toothpaste and artificial saliva (AS), and each enamel block received 5000 linear strokes. The samples were assessed for surface micro-hardness (to estimate Vickers hardness number, VHN), surface roughness (Ra), and volume loss/gain using micro-computed tomography (micro-CT). The highest increase in the VHN was noticed for group 3 (117.81) followed by group 2 (61.13), and all the intragroup comparisons were statistically significant (p < 0.05). Demineralization increased the Ra values, and a decrease was observed post-remineralization for all the groups. The maximum Ra decrease was observed for group 3 (−223.2 nm) followed by group 2 (−55.6 nm), and all the intragroup comparisons were again statistically significant (p < 0.05). Micro-CT investigation revealed that the enamel volume decreased after demineralization and increased after remineralization among all groups. The F-BG toothpaste showed greater enamel surface micro-hardness (increased VHN), smoother surface (low roughness), and better volume restoration (remineralization) in comparison to the fluoride toothpaste.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| 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) | 0.000 | 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