Comparative Assessment of Enamel Microhardness Using Various Remineralising Agents on Artificially Demineralized Human Enamel
Bibliographic record
Abstract
Introduction: Various remineralizing agents can be used to remineralize initial carious lesions. Aim: The study aims to compare and evaluate the remineralizing efficiency of Remin Pro (VOCO GmbH, Cuxhaven, Germany), tricalcium phosphate, and HealOzone (CurOzone USA Inc., Ontario, Canada) by measuring the microhardness of enamel. Materials and method: Forty-five mandibular premolars were collected and divided into three groups (A, B, and C). After sectioning mesiodistally, they were subdivided into the control and test groups. The test group was further subdivided into demineralized (A2a, B2a, and C2a) and remineralized (A2b, B2b, and C2b) groups. All test group samples were demineralized by immersing in demineralizing solutions for 24 hours. Afterwards, A2b, B2b, and C2b samples were remineralized by remineralizing agents (Remin Pro, tricalcium phosphate, and HealOzone) for three minutes (twice a day) for 14 days, and then Vickers microhardness testing (VHN) was performed. Result: The microhardness values of the demineralized group were lower compared to the samples of the control groups. In the remineralized group, the mean microhardness values were maximum for HealOzone (293.22 kgmm-2), followed by Remin Pro (287.5660 kgmm-2) and then tricalcium phosphate (282.4660 kgmm-2). Conclusion: The application of remineralizing paste proved potent in improving the remineralization in the demineralized enamel surface.
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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".