Effect of Proanthocyanidin-enriched extracts on the inhibition of wear and degradation of dentin demineralized organic matrix
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the effect of Cranberry and Grape seed-enriched extract gels in inhibiting wear and degradation of demineralized organic matrix (DOM). DESIGN: 225 dentin specimens obtained from bovine incisors were randomly allocated into 5 groups (n=45): 10% Grape seed extract gel (GSE), 10% Cranberry extract gel (CE), 0.012% Chlorhexidine gel (CX), 1.23% NaF gel (F), and no active compound gel (P, placebo). Before the treatments, samples were demineralized by immersion in 0.87M citric acid, pH 2.3 (36h). Then, the studied gels were applied once over dentin for 1min. Next, the samples were immersed in artificial saliva containing collagenase obtained from Clostridium histolyticum for 5days. The response variable for dentin wear was depth of dentin loss measured by profilometry and for collagen degradation was hydroxyproline determination. Data were analyzed by ANOVA followed by Tukey's test and Pearson Correlation Test (p<0.05). RESULTS: Grape seed extract significantly reduced dentin wear compared to the other groups (p<0.05). Cranberry extract and Chlorhexidine did not differ statistically and were able to reduce wear when compared to NaF and placebo treatments. The hydroxyproline analysis showed that there was no significant difference among groups for all treatments (p<0.05). Correlation analysis showed a significant correlation between the amount of degraded DOM evaluated by profilometry and the determination of hydroxyproline. CONCLUSION: Cranberry extract was able to reduce the dentin wear and collagen degradation, likely due to the proanthocyanidin content and its action. Therefore, Cranberry could be suggested as an interesting natural-based agent to prevent dentin erosion.
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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) | 0.002 | 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".