Enamel Remineralization Competence of a Novel Fluoride-Incorporated Bioactive Glass Toothpaste—A Surface Micro-Hardness, Profilometric, and Micro-Computed Tomographic Analysis
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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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) | 0.000 | 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 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".