Effects of Different Toothpastes on the Nanomechanical Properties and Chemical Composition of Resin-Modified Glass Ionomer Cement and Composite Resin Restorations
Bibliographic record
Abstract
Purpose: This study evaluates the effects of different toothpastes on the nanohardness and chemical compositions of restorative materials and dental surfaces. Methods: Bovine enamel (n = 72) and dentin (n = 72) blocks were obtained and restored using RMGIC (n = 36) or CR (n = 36) to create the following surfaces: dentin adjacent to RMGIC (DRMGIC), enamel adjacent to RMGIC (ERMGIC), dentin adjacent to CR (DCR), and enamel adjacent to CR (ECR). After restoration, one hemiface of each specimen was coated with an acid-resistant varnish to facilitate the creation of control (C) and eroded (E) sides; the latter were achieved by erosion–abrasion cycles as follows: erosion with 1% citric acid: 5 days, four times for 2 min each day; 1% citric acid/abrasion, two times for 15 s, followed by immersion in a toothpaste slurry for 2 min. Toothpastes without fluoride (WF; n = 12), with sodium fluoride (NaF; n = 12), and with stannous fluoride (SnF2; n = 12) were used for RMGIC or CR. The specimens were analyzed for nanohardness (H), and chemical composition using energy-dispersive X-ray spectroscopy and Raman microscopy. The data were statistically analyzed using two-way repeated measures ANOVA and Tukey’s test (α = 0.05). Results: Lower H values were obtained with NaF for DRMGIC-C, with a statistically significant difference from the H value obtained with WF (p < 0.05). The calcium and phosphorus concentrations in DCR-E were significantly lower with WF than with the other types of toothpaste (p < 0.05). Fluoride-containing toothpastes are capable of preserving the main chemical components of the dentin adjacent to the restorative materials under erosive–abrasive conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".