Application of Silver Nanoparticles to Improve the Antibacterial Activity of Orthodontic Adhesives: An In Vitro Study
Bibliographic record
Abstract
There is a significant change in the bacterial plaque populations in the oral cavity during and after orthodontic treatment. Numerous studies have demonstrated that 2−96% of patients could increase the risk of white spot lesions. Streptococcus mutans and Lactobacilli ssp. are responsible for these white spot lesions. In this work, silver nanoparticles (AgNPs) with a diameter of 11 nm and dispersed in water were impregnated onto three different commercial orthodontic adhesives at 535 μg/mL. The shear bond strength (SBS) was assessed on 180 human premolars and metallic brackets. The premolars were divided into six groups (three groups for the commercial adhesives and three groups for the adhesives with AgNPs). All the groups were tested for their bactericidal properties, and their MIC, MBC, and agar template diffusion assays were measured. After adding AgNPs, the SBS was not significantly modified for any adhesive (p > 0.05), and the forces measured during the SBS did not exceed the threshold of 6 to 8 MPa for clinical acceptability in all groups. An increase in the bactericidal properties against both S. mutans and L. acidophilus was measured when the adhesives were supplemented with AgNPs. It was concluded that AgNPs can be supplement commercial orthodontic adhesives without modifying their mechanical properties with improved bactericidal activity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".