The Impact of Stannous, Fluoride Ions and Its Combination on Enamel Pellicle Proteome and Dental Erosion Prevention
Bibliographic record
Abstract
OBJECTIVES: To compare the effects of stannous (Sn) and fluoride (F) ions and their combination on acquired enamel pellicle (AEP) protein composition (proteome experiment), and protection against dental erosion (functional experiment). METHODS: In the proteome experiment, bovine enamel specimens were incubated in whole saliva supernatant for 24h for AEP formation. They were randomly assigned to 4 groups (n=10), according to the rinse treatment: Sn (800ppm/6.7mM, SnCl2), F (225ppm/13mM, NaF), Sn and F combination (Sn+F) and deionized water (DIW, negative control). The specimens were immersed 3× in the test rinses for 2min, 2h apart. Pellicles were collected, digested, and analyzed for protein content using liquid chromatography electrospray ionization tandem mass spectrometry. In the functional experiment, bovine enamel specimens (n=10) were similarly treated for pellicle formation. Then, they were subjected to a five-day erosion cycling model, consisting of 5min erosive challenges (15.6 mM citric acid, pH 2.6, 6×/d) and 2min treatment with the rinses containing Sn, F or Sn+F (3×/d). Between the treatments, all specimens were incubated in whole saliva supernatant. Surface loss was determined by profilometry. RESULTS: Our proteome approach on bovine enamel identified 72 proteins that were common to all groups. AEP of enamel treated with Sn+F demonstrated higher abundance for most of the identified proteins than the other groups. The functional experiment showed reduction of enamel surface loss for Sn+F (89%), Sn (67%) and F (42%) compared to DIW (all significantly different, p<0.05). CONCLUSION: This study highlighted that anti-erosion rinses (e.g. Sn+F) can modify quantitatively and qualitatively the AEP formed on bovine enamel. Moreover, our study demonstrated a combinatory effect that amplified the anti-erosive protection on tooth 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 | 0.000 | 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) | 0.001 | 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".