The influence of fillers and protease inhibitors in experimental resins in the protein profile of the acquired pellicle formed in situ on enamel-resin specimens
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the influence of the addition of fillers and/or protease inhibitors [(epigallocatechin gallate - EGCG) or (chlorhexidine - CHX)] in experimental resins in the protein profile of the acquired pellicle (AP) formed in situ on enamel-resin specimens. DESIGN: 324 samples of bovine enamel were prepared (6 × 6 × 2 mm). The center of each sample was added with one of the following experimental resins (Bis-GMA+TEGDMA): no filler, no inhibitor (NF-NI); filler no inhibitor (F-NI); no filler plus CHX (NF-CHX); filler plus CHX (F-CHX); no filler plus EGCG (NF-EGCG); filler plus EGCG (F-EGCG). Nine subjects used a removable jaw appliance (BISPM - Bauru in situ pellicle model) with 2 slabs from each group. The AP was formed for 120 min, in 9 days and collected with electrode filter paper soaked in 3% citric acid. The pellicles collected were processed for analysis by LC-ESI-MS/MS. RESULTS: A total of 140 proteins were found in the AP collected from all the substrates. Among them, 16 proteins were found in common in all the groups: 2 isoforms of Basic salivary proline-rich protein, Cystatin-S, Cystatin-AS, Cystatin-SN, Histatin-1, Ig alpha-1 chain C region, Lysozyme C, Mucin-7, Proline-rich protein 4, Protein S100-A9, Salivary acidic proline-rich phosphoprotein ½ and Statherin. Proteins with other functions, such as metabolism and transport, were also identified. CONCLUSION: The composition of the experimental resins influenced the protein profile of the AP. This opens a new avenue for the development of new materials able to guide for AP engineering, thus conferring protection to the adjacent teeth.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".