Use of polyphenols as a strategy to prevent bond degradation in the dentin–resin interface
Bibliographic record
Abstract
This study evaluated the effect of dentin pretreatment with the polyphenols quercetin and resveratrol on the resin‐dentin microtensile bonding strength (μTBS) and collagen fibrils stability of the adhesive interface. Different concentrations (100, 250, 500, or 1,000 μg ml−1) of quercetin or resveratrol, or a mixture of quercetin and resveratrol (3:1, 1:1, 1:3; vol:vol), as well as distilled water or 2% chlorhexidine digluconate, were applied to etched dentin. Then, a two‐step etch‐and‐rinse adhesive was applied followed by composite restoration. Measurements of resin–dentin μTBS were made after 1 and 120 d. The stability of collagen fibrils in the hybrid layer was evaluated using transmission electron microscopy. The Student's t‐test and two‐way factorial anova with Tukey's test were used to analyze the effects of dentin pretreatment and storage time on μTBS values. Comparisons between μTBS measurements made on 1 and 120 d showed that resveratrol had the best performance, with significantly higher μTBS values after 120 d for all concentrations of resveratrol tested. Quercetin pretreatment resulted in a significant rise of μTBS when used at concentrations of 100 and 500 μg ml−1. Quercetin + resveratrol at the ratio of 1:1 performed better than when used at ratios of either 3:1 or 1:3. Resveratrol might represent a potential approach to achieve desirable bonding stability and reduce the frequent replacement of composite restorations.
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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 | 0.001 |
| 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".