Impact of curcumin loading on the physicochemical, mechanical and antimicrobial properties of a methacrylate-based experimental dental resin
Bibliographic record
Abstract
Abstract Oral biofilms are directly linked to one of the most common chronic human diseases, dental caries. Resin-based dental materials have significant potential to replace amalgam, however they lack sufficient antimicrobial power. This innovative study investigates a curcumin-loaded dental resin which can be utilized in an antimicrobial photodynamic therapy (aPDT) approach. The study evaluated the effects of curcumin loading on resin physicochemical, mechanical, and adhesive properties, as well as the antimicrobial response associated with blue light activation. Preliminary tests involving degree of conversion (DC) and sample integrity determined the optimal loading of curcumin to be restricted to 0.05 and 0.10 wt%. These optimal loadings were tested for flexural strength (FS), water sorption (WS) and solubility (SL), shear bond strength to dentin (SBS), and viability of Streptococcus mutans under 14.6 J/cm2 blue light or dark conditions, in 6 h and 24 h biofilms. The results demonstrated that 0.10 wt% curcumin had minimal impact on either FS or SBS, but detectably increased WS and SL. A 2 log10 (CFU/mL) reduction in S. mutans after light application in both 6 h and 24 h biofilms were corroborated by CLSM imaging and highlighted the significant potential of this novel aPDT approach with resin-based dental materials.
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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) | 0.002 | 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".