Effect of adding CPP-ACP into a daily-use toothpaste on remineralization of enamel white spot lesions
Bibliographic record
Abstract
Objective: The purpose of this study was to investigate the effect of adding CPP-ACP into a daily-use toothpaste on the remineralization of enamel caries lesions.Methods: Thirty enamel blocks were obtained from bovine incisors. Each specimen was divided into three equal parts. One-third of each block was coated with varnish to serve as a sound control area, while the remaining two-thirds underwent a demineralization process. After demineralization, another one-third of the surface was varnished, leaving only one-third of the enamel to undergo remineralization. The enamel blocks were divided into three groups (n=10), according to the remineralization treatment applied as follows: Group 1: fluoride-containing toothpaste, Group 2: CPP-ACP-containing toothpaste, and Group 3: fluoride- and CPP–ACP–containing toothpaste. Remineralization was assessed through the Vickers microhardness test at various depths (20, 50, 120 and 200 µm). The data were analyzed by ANOVA and LSD test, and P< 0.05 was considered statistically significant.Results: There was a significant difference in remineralization efficacy between the groups at the depth of 20 µm (P 0.001). Pairwise comparisons revealed that the toothpaste containing both fluoride and CPP-ACP had a significantly greater microhardness than other experimental groups (P 0.05). No significant difference was observed between the study groups concerning microhardness at 50, 120 and 200 µm depths (P 0.05).Conclusions: CPP-ACP can serve as a suitable alternative to fluoride in daily-use toothpaste for enamel remineralization. The concurrent use of fluoride and CPP-ACP in toothpaste can generate a synergistic remineralizing effect at the enamel surface layer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".