Effect of EGCG–Methacrylate-Functionalized Resin Infiltrant on White Spot Lesions: An In Vitro Study
Bibliographic record
Abstract
This study evaluated the color change (ΔE00) and penetration depth (PD) of white spot lesions (WSLs) infiltrated with the resin infiltrant (Icon®) functionalized with methacrylate epigallocatechin-3-gallate (EGCG). To introduce polymerizable double bonds, EGCG was reacted with methacryloyl chloride (EM). Subsequently, the Icon resin infiltrant (I) was loaded with neat EGCG (IE) or EGCG–methacrylate (IEM) at 2 wt% each. WSLs were created on bovine enamel blocks and treated with I, IE, or IEM. Sound and untreated enamel surfaces were used as controls (C). Infiltrant PD (%) was determined by Confocal Laser Scanning Microscopy (CLSM, n = 12) analysis. For color change (ΔE00) determination (n = 14), ΔL, Δa, and Δb, half of each sample was kept sound as a reference area. The color was determined with a spectrophotometer. Data were statistically evaluated (p = 0.05). Surface morphology was obtained as a qualitative response variable using 3D CLSM. PD (%) did not differ statistically for I, IE, and IEM (p = 0.780). Groups I and IEM showed similar performance on color change (ΔE00) compared to the control group, while IE exhibited intermediate results, with no significant difference observed between the untreated, I, and IEM groups (p < 0.001). IEM promoted the masking of the WSL color without interfering with the PD.
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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) | 0.001 | 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 | 0.000 | 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".