Epigallocatechin‐Gallate Methacrylate as a Novel Addition to Pit and Fissure Sealants: An In Vitro Analysis of Physical Properties and Microscopy
Bibliographic record
Abstract
To enhance the longevity of resin sealants on the occlusal surfaces of posterior teeth, improvements are needed in antibacterial properties, mechanical resistance, and bond strength to enamel. This study investigated the effectiveness of incorporating epigallocatechin-gallate methacrylate (EGCG-M) into a pit and fissure sealant (SEAL, PacSeal) by assessing its penetration depth, degree of conversion, color properties, and surface morphology. Thirty blocks of bovine enamel were divided into three groups: SEAL (control), SEAL with neat EGCG (E0), and SEAL with EGCG-M. Analyses were conducted including penetration depth with confocal microscopy, degree of conversion using FTIR-ATR, color analysis using CIELab, and surface morphology via 3D laser microscope. The EGCG-M group exhibited the highest degree of conversion (44.9% ± 2.1%) compared to the SEAL (41.7% ± 2.1%) and E0 (42.1% ± 1.1%) groups (p = 0.040). Deeper penetration was observed in the EGCG-M group (9.7 ± 10 μm), followed by SEAL (-0.1 ± 12.2 μm) and E0 (-5.3 ± 9.7 μm) groups (p = 0.002). Although no significant difference was found in L* (lightness) coordinate values between the groups (p = 0.060), the EGCG-M group exhibited a more heterogeneous surface. Therefore, incorporating EGCG methacrylate into the sealant improved the degree of conversion and penetration capability without notable changes in color.
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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) | 0.000 | 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 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".