Methacrylation of epigallocatechin-gallate for covalent attachment with a dental polymer
Bibliographic record
Abstract
OBJECTIVE: Synthesize novel epigallocatechin-gallate (EGCG) methacrylate monomers with the ability to copolymerize with dental methacrylate resins. METHODS: EGCG was reacted with 1/3 (E33), 2/3 (E67) and 1 (E100) molar equivalents of methacyloyl chloride introducing three degrees of polymerizablility. EGCG-methacrylates were characterized by Fourier Transform Infrared Spectroscopy (FTIR) and Nuclear Magnetic Resonance (NMR). E33, E67, E100 and neat EGCG were incorporated into TEGDMA at 0.5-20% ratios (m/m). Copolymers were tested for degree of conversion (%DC), EGCG release, gel content (%GC), degree of swelling (%DS), flexural properties and bacterial viability (Streptococcus mutans, baseline/30-days). Neat TEGDMA and TEGDMA passively loaded with EGCG (E0) were used as controls. Data were analysed by one-way ANOVA, Tukey, and Dunnett's method (α=5%). Two-way ANOVA and Bonferroni were used to investigate factor interaction. RESULTS: FTIR/NMR confirmed synthesis of desired compounds. All of E100 incorporated ratios had %DC similar to TEGDMA. Remaining groups had reduction in %DC at 2% in E0, 10% in E33 and 20% in E67 ratios. EGCG was stable within ECGC-methacrylate copolymers. Release of EGCG from E0 significantly increased with higher EGCG ratios. Except for E100, higher EGCG or EGCG-methacrylate ratios led to decreased %CG and %DS. At baseline, E0 had the lowest bacterial survival rates (1-10% survival) at all ratios compared to E33, E67, E100, and neat TEGDMA. However, E33, E67 and E100 still had statistically lower survival rates (7-53%) compared with neat TEGDMA. After 30-days, all compounds had similar survival rates for all ratios, which were lower than that of neat TEGDMA. SIGNIFICANCE: Demonstration of methacrylate functionalized EGCG- with inherited antibacterial activity for improved restoration longevity.
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".