Mechanical-physicochemical properties and biocompatibility of catechin-incorporated adhesive resins
Bibliographic record
Abstract
OBJECTIVES: Several anti-proteolytic dentin therapies are being exhaustively studied in an attempt to reduce dentin bond degradation and improve clinical performance and longevity of adhesive restorations. This study assessed the effect of epigallocatechin-3-gallate (EGCG) on long-term bond strength when incorporated into adhesives. MATERIAL AND METHODS: Adhesive systems were formulated with EGCG concentrations of 0 wt%: (no EGCG; control); 0.5 wt% EGCG; 1.0 wt% EGCG, and 1.5 wt% EGCG. Flexural strength (FS), modulus of elasticity (ME), modulus of resilience (MR), compressive strength (CS), degree of conversion (DC), polymerization shrinkage (PS), percentage of water sorption (%WS), percentage of water solubility (%WL) and cytotoxicity properties were tested. Dentin microtensile bond strength (µTBS) was evaluated after 24 h and again after 6 months of water storage. The adhesive interface was analyzed using scanning electron microscopy (SEM). RESULTS: No significant differences were found among the groups in terms of FS, ME, MR, CS and PS. EGCG-doped adhesives increased the DC relative to the control group. EGCG concentrations of 1.0 wt% and 0.5 wt% decreased the WS of adhesives. WL decreased in all cases in which EGCG was added to adhesives, regardless of the concentration. EGCG concentrations of 1.0 wt% and 0.5 wt% reduced cytotoxicity. EGCG concentrations of 1.0 wt% and 0.5 wt% preserved µTBS after 6 months of storage, while 1.5 wt% EGCG significantly decreased µTBS. SEM: the integrity of the hybrid layer was maintained in the 0.5 wt% and 1.0 wt% EGCG groups. CONCLUSION: EGCG concentrations of 1.0 wt% and 0.5 wt% showed better biological and mechanical performance, preserved bond strength and adhesive interface, and reduced cytotoxicity.
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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".