Antibacterial Properties of an Experimental Dental Resin Loaded with Gold Nanoshells for Photothermal Therapy Applications
Bibliographic record
Abstract
This study explored the chemical and antibacterial properties of a dental resin loaded with gold nanoshells (AuNPs) in conjunction with photothermal therapy (PTT) as a novel method against Streptococcus mutans (S. mutans) to prevent secondary caries. First, a 20-h minimum inhibitory concentration (MIC) assay was performed on solutions of AuNPs with planktonic S. mutans under an LED device and laser at 660 nm. Next, resin blends containing 0, 1 × 1010, or 2 × 1010 AuNPs/mL were fabricated, and the degree of conversion (DC) was measured using an FTIR spectroscopy. Lastly, a colony forming unit (CFU) count was performed following 24 h growth of S. mutans on 6 mm diameter resin disks with different light treatments of an LED device and a laser at 660 nm. The MIC results only showed a reduction in S. mutans at AuNP concentrations less than 3.12 µg/mL under a laser illumination level of 95.5 J/cm2 compared to the dark treatment (p < 0.010 for each). CFU and DC results showed no significant dependence on any light treatment studied. The AuNPs expressed antibacterial effects following PPT against planktonic S. mutans but not in a polymerized dental adhesive resin. Future studies should focus on different shapes, structure, and concentrations of AuNPs loaded in a resin blend.
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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".