Evaluation of a long-term antimicrobial dental adhesive via in vitro biodegradation and in vivo rodent secondary caries models
Bibliographic record
Abstract
Objectives Bacterial-derived secondary caries is a primary cause of dental treatment failure at the artificial material-tissue interface. We previously developed ultra-long-term antimicrobial/antidegradative drug-silica particles (DSPs) to counter this interfacial failure. The aim of the current study was to evaluate a novel DSP-filled-adhesive system via in vitro and in vivo (rat) anti-secondary-caries studies. Methods DSPs were incorporated into commercial total-etch dental adhesive at 10 % wt. to make DSP-SBMP. Interfacial specimens of DSP-SBMP-dentin and control SBMP-dentin were incubated 0- or 6-months in simulated salivary esterase, and subsequently with S. mutans/L. rhamnosus co-culture. Interfacial biomarkers were assessed via confocal microscopy and micro-computed-tomography. DSP-SBMP and SBMP were used to restore teeth in 16 SD rats in a 7-week split-mouth secondary caries study followed by clinical and µCT caries analysis and organ histology to assess biocompatibility. Results In vitro, interfacial biofilm viability (-23.1 ± 4.3 %) and biomass (-19.2 ± 4.9) were reduced by DSP-SBMP, as was cavitated (-78.6 ± 13.8 %) and demineralized (-33.4 ± 8 %) volume (ANOVA, Tukey HSD, p < 0.05). In vivo clinically observed primary and secondary caries counts were reduced on DSP-SBMP-restored teeth (χ 2 p < 0.05). No significant toxic effects were observed. Significance This comprehensive in vitro and in vivo antimicrobial/antidegradative analysis of a new dental biomaterial, accurately modeling the chemical and biological environment these materials must perform in, provided comprehensive understanding of potential material performance that strongly supports continued development and clinical evaluation. The clinical relevance of the in vitro model used in this study was validated by the in vivo animal model and could be used to assess new dental biomaterials.
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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 | 0.001 | 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) | 0.001 | 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".