Comparative Antibacterial Efficacy of Orthodontic Brackets Coated with Titanium Dioxide, Copper Oxide, and Hydroxyapatite-Silver Nanoparticles Against Streptococcus mutans
Bibliographic record
Abstract
Background: The development of dental caries around orthodontic brackets is a common complication of orthodontic treatment. Objectives: This study aimed to compare the antibacterial efficacy of stainless steel (SS) brackets coated with copper oxide nanoparticles (CuO-NPs), titanium dioxide nanoparticles (TiO2-NPs), and hydroxyapatite silver nanoparticles (HA-SNPs) against Streptococcus mutans. Methods: In this in vitro, experimental study, 20 orthodontic brackets were assigned to four groups (n = 5, each group). One group served as the no-intervention control group, and the brackets in the remaining three groups underwent dip coating with CuO-NPs, TiO2-NPs, and HA-SNPs < 100 nm. The number of S. mutans colonies was counted after 24 h, one week, and one and three months. Data were analyzed using the Shapiro Wilk test, repeated measures ANOVA, and Bonferroni multiple comparisons test. Results: All three experimental groups exhibited similar antibacterial properties after 90 days of trial (P > 0.05). Moreover, CuO-NPs had a greater inhibitory effect than TiO2-NPs on S. mutans after 24 h (P = 0.007). However, the inhibitory effect of HA-SNPs was not significantly different from that of TiO2-NPs (P = 0.259) or CuO-NPs (P = 0.224). Conclusions: Considering the similar antibacterial properties of all three coatings in the long term, all three types of nanoparticles can be used to coat orthodontic brackets to prevent caries. However, due to the high cost and difficult preparation of HA-SNPs and the slightly higher efficacy of CuO-NPs in the short-term, the latter may be preferred for this purpose.
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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".