Role of Silver Ions in Preventing Demineralisation of Human Enamel
Bibliographic record
Abstract
Aim: To determine the changes in Ca2+ release before and after the application of Riva Star, AgNO3 and to analyse the changes in surface roughness of the samples. Methods: The Riva Star, AgNO3 were used to measure their effects on the demineralisation processes of the windowed human enamel in 50ml pH4.0 acetic solution under 370C. The sound human enamel samples were sliced and varnished using nail polish and 5mm2 was left on the surface of enamel sample in order to make windowed-type enamel. The demineralised enamel samples were treated by these agents after 4hours of demineralisation, and later put back into the same acetic acid solutions for further 4hours of demineralisation to detect the changes. Results: The results demonstrated that Riva Star (SDF/KI) treatment exerted the inhibition ability in demineralisation remarkably. However, when the silver nitrate was applied alone, it slightly accelerated the demineralisation firstly and then followed a straight line. The treatment of silver nitrate alone also caused black staining. Reducing agent such as potassium iodide following the treatment of silver nitrate can inhibit the demineralisation instantly without causing the initial acceleration in demineralisation and the final black staining. Conclusions: Riva Star (SDF/KI) is an effective demineralisation inhibition agent for up to 48 hours following the topical application. The potassium iodide application following the treatment of silver nitrate can not only accelerate the inhibition performance, but also effectively avoid black staining. Keywords: Silver ions, Demineralization, Caries, Riva Star, SDF, Surface roughness
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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 | 0.001 | 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".