In Vitro Efficacy of Tricalcium Phosphate and Casein Phosphopeptide Amorphous Calcium Phosphate Fluoride for Remineralization of Enamel White Spot Lesions
Bibliographic record
Abstract
Objectives: The main purpose of this study was to compare the remineralizing effects of casein phosphopeptide amorphous calcium phosphate fluoride (CPP-ACPF) and tricalcium phosphate (TCP) on artificially induced enamel white spot lesions (WSLs). Materials and Methods: In this in vitro study, 45 sound extracted premolars were immersed in a demineralizing solution (pH=4.5) for 96 hours, and were randomly divided into 3 groups of TCP, MI Paste Plus, and control. They were exposed to the remineralizing agents for 5 minutes once a day for 30 days. After mounting the teeth in resin blocks and polishing, they underwent a microhardness test at 3 different depths from the enamel surface. Data were analyzed by Prism software, two-way ANOVA, and Tukey’s test (α=0.05). Results: The volume percentage of mineral content (VPM) was significantly different among the three groups at 30-, 60- and 90µm depths (P<0.0001). At 30µm depth, CPP-ACPF was significantly more effective than TCP (P<0.0001). At 60- and 90µm depths, there was no significant difference between CPP-ACPF and TCP (P>0.05). Conclusion: Both CPP-ACPF and TCP had significant efficacy for remineralization of artificially induced enamel WSLs under in vitro conditions.
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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 | 0.000 |
| 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".