Effect of duck eggshell nano-calcium carbonate and phosphopeptide-amorphous calcium phosphate casein on re-mineralization of deciduous enamel surface
Bibliographic record
Abstract
Introduction: Dental caries has been the most prevalent disease in humans, particularly in children.Objectives: This study aimed to determine the difference between duck eggshell's nano-calcium carbonate (NCDe) and casein phosphopeptide-amorphous calcium phosphate (CPP-ACP) in increasing enamel re-mineralization.Material and methods: 5% and 10% NCDe were obtained from 50 g of thick NCDe; then, 2.5 g were taken with 2.5 g of carboxymethyl cellulose (CMC) and 45 ml of distilled water to obtain 5% NCDe gel.Twenty-four primary maxillary first incisor teeth were divided into four groups, i.e., (1) negative control, (2) posi tive control (10% CPP-ACP), (3) 5% NCDe, and (4) 10% NCDe.Each tooth sample was measured for the enamel surface's micro-hardness using Vickers hardness test.Meanwhile, the enamel surface morphology was captured by means of electron microscopy scanning with magnification of 5,000×.Results: The average micro-hardness of the enamel in the 10% NCDe and 10% CPP-ACP groups were 343.66 HV and 305.28 HV, respectively.In addition, the 10% NCDe group showed the lowest enamel porosity with strongest positive correlation between treatments' duration, with an increase in enamel re-mineralization.In terms of statistical data, the correlation and linearity parameters for the 10% NCDe group were .800and 64.1%, respectively.Conclusions: The 10% NCDe group tended to be more effective than 5% NCDe and 10 CPP-APP groups in increasing re-mineralization of tooth enamel.Therefore, it was confirmed that NCDe 10% group has met the criteria as an excellent agent to re-mineralize primary teeth enamel.
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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) | 0.003 | 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".