Composite resin degradation products from BisGMA monomer modulate the expression of genes associated with biofilm formation and other virulence factors in <i>Streptococcus mutans</i>
Bibliographic record
Abstract
Bacterial microleakage along the tooth/composite resin dental restoration interface contributes to postoperative sensitivity, recurrent caries, and necrosis. Studies have confirmed that enzymes in human saliva degrade composite resin monomers 2,2-bis [4-(2-hydroxy-3-methacryloxypropoxy) phenyl] propane (BisGMA) and triethylene glycol dimethacrylate (TEGDMA) to release methacrylic acid (MA), bishydroxypropoxyphenyl propane (Bis-HPPP), and triethylene glycol (TEG) at levels of 50 microM in vivo. Studies have found that TEGDMA degradation products alter the growth and gene expression of cariogenic Streptococcus mutans. Specifically, TEG was shown to alter S. mutans gene expression levels of gtfB, a known virulence factor, and yfiV, a putative transcriptional regulator of cell-surface fatty acid genes. The objective of this study was to examine the effect of BisGMA degradation products on the growth and gene expression of S. mutans NG8 cells. Results demonstrated slight inhibition of bacterial growth at Bis-HPPP concentrations of 1.0 x 10(2) and 2.5 x 10(2) microM at pH 5.5. Furthermore, both MA and Bis-HPPP affected gtfB and yfiV expression in a concentration-dependent manner. Because BisGMA is universally used across most dental restorative materials, with millions of placement procedures performed annually, these findings are relevant due to the potential influence of resin monomer-derived biodegradation products on biofilm formation, acid tolerance, and proliferation of S. mutans cells.
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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".