Preventive effect of Cranberry gel on dentin submitted to erosion
Bibliographic record
Abstract
Objectives: Considering that Cranberry’s components might inhibit dentin metalloproteinases exposed to erosive agents, the aim of this study was to evaluate in situ effect of a Cranberry gel application on dentin before an erosive challenge. Methods: This crossover double-blinded study was performed in 2 phases of 5 days each, with 10 healthy volunteers who wore 2 palatal devices (1 for each phase) with 4 dentin specimens (2 specimens for each group). The groups under study were; First Phase: G1 - Erosive challenge (Coca-cola®) over dentin without any previous treatment (1st negative control group); G2 - Erosive challenge over dentin previously treated with Cranberry gel (test group); and Second Phase: G3 - Erosive challenge over dentin previously treated with a gel without any active principle (2ndnegative control group); G4 - Erosive challenge over dentin previously treated with 0.12% Chlorhexidine gel (positive control group). Each device was immersed into the acid beverage, 3 times daily for 5 minutes during 5 days. Profilometry (µm) was used to quantify the dentin wear. Data were analyzed by Repeated Measures Analysis of Variance followed by Fisher's test (p<0.05). Results: Data (G1: 4.98 ± 1.36a; G2: 3.29 ± 1.16b; G3: 4.38 ± 1.19a; G4: 3.32 ± 1.55b) showed no statistical difference between G1 and G3. There was also no difference between G2 and G4. However, G2 and G4 presented lower wear when compared to G1 and G3, and this difference was statistically significant. Conclusion: The results of this study suggest a significant efficacy of Cranberry gel in preventing wear of dentin subjected to dental erosion. Financial Support: FAPESP (Process 2012/16295-7) and MaRS IPoP Fund Keywords: Collagen, Dentin, Erosion, Preventive dentistry and Wear
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".