Biodegradation of a dental composite by esterases: dependence on enzyme concentration and specificity
Bibliographic record
Abstract
Studies have shown that inflammatory (cholesterol esterase, CE) and salivary (pseudo-cholinesterase, PCE) enzymes can cause the breakdown of bisphenol-A diglycidyl dimethacrylate (bisGMA) and triethylene glycol dimethacrylate (TEGDMA) components from composite resins. Based on the above consideration, it was desired to show how CE- and PCE-catalyzed hydrolysis of resin components was dependent on the enzymes' concentration and to determine their distinct specificities (if any) towards resin components. Photopolymerized model composite resin samples (60% weight fraction silanated barium glass filler) based on bisGMA and TEGDMA monomers (55/45 weight ratio of the matrix, respectively) were incubated with PBS and either 0.01, 0.05, 0.1 or 1 unit/ml of CE or PCE for 16 days (pH 7.0, 37 degrees C). Incubation solutions were analyzed by high-performance liquid chromatography (HPLC), UV spectroscopy and mass spectrometry. The composite samples were characterized by scanning electron microscopy (SEM). Degradation rates of bisGMA and TEGDMA monomers were assessed. The results showed that CE had a greater specificity towards cleaving bisGMA while PCE showed a greater specificity towards TEGDMA. A strong enzyme concentration dependence was observed which suggests that the level of degradation products generated for a material will depend on the esterase make-up of an individual's saliva in combination with the specific formulation of monomer components used.
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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 | 0.001 |
| 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 | 0.000 |
| 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".