Polycarboxylated microfillers incorporated into light-curable resin-based dental adhesives evoke remineralization at the mineral-depleted dentin
Bibliographic record
Abstract
This study aimed at evaluating the remineralizing properties of three experimental light-curable resin-based dental adhesives containing tailored polycarboxylated microfillers. A co-monomers blend was firstly formulated and then mixed with each of the following microfillers: polycarboxylated bioactive glass (PBAG), polycarboxylated calcium silicates (PCS), and polycarboxylated calcium silicates-doped brushite (PDP). The three experimental and a filler-free control resins were applied onto 10% orthophosphoric acid treated dentin discs and light cured. The specimens were soaked in artificial saliva (AS) for 3, 7, and 14 days. Dentin mineral variation was monitored using attenuated total reflection-Fourier transform infrared (ATR-FTIR) and Raman spectroscopy. Confocal laser scanning microscopy (CLSM) was employed to observe the ultra-morphology/nanoleakage along the resin-dentin interface. The bonding ability and the durability of the resin-dentin bonds were investigated through microtensile bond strength (μTBS) test. ATR-FTIR and Raman showed a significant increase of the mineral matrix area ratio and phosphate peak intensity in specimens treated with the experimental resins within 14 days (p < 0.05). No significant increment of minerals was found in untreated specimens or specimens treated using the control filler-free resin (p > 0.05). Dentin treated using PBAG or PCS exhibited higher level of remineralization than the specimens in PDP group. CLSM showed reduction in nanoleakage, although the remineralization of the hybrid layer induced a significant drop in the μTBS after 3-month storage (p < 0.05). The experimental resin-based dental adhesives containing bioactive microfillers remineralize the resin-dentin interfaces when in intimate contact with biological fluids.
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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".