An In Vitro Study Comparing the Antibacterial and Mechanical Properties of Zinc Oxide-Based Nanofillers in Orthodontic Adhesives for White Spot Lesion Prevention in Fixed Orthodontic Therapy
Bibliographic record
Abstract
Introduction Preventing enamel demineralization (white spot lesions or WSLs) around the brackets during and after orthodontic therapy has been a challenging problem. Zinc oxide (ZnO) nanoparticles (NPs) show antibacterial effects against cariogenic bacteria Streptococcus (S.) mutans. Materials and methods In this study, researchers modified Transbond XT adhesive (Sigma Aldrich, St. Louis, Missouri, USA) by adding different concentrations of ZnO nanoparticles, i.e., 0.1% and 0.5wt%, in two experimental groups and a control group. We performed Fourier transform infrared spectroscopy and scanning electron microscopy for physio-structural characterization and investigated antibacterial ability by disc diffusion and colony-forming tests. We conducted shear bond strength and adhesive remnant index to determine the mechanical characteristics. Results The development and size of the inhibition zone were greatly dependent on the concentration of ZnO nanoparticles in the disc agar diffusion test. All ZnO NP-based experimental adhesives reduced the colony numbers for S. mutans. For S. mutans, the composite comprising 0.5wt% ZnO nanoparticles significantly reduced colony counts. The control group exhibited the maximum mean shear bond strength, whereas 0.5wt% nanoparticles composite had the lowest number. Conclusion Adding ZnO as nanofillers imparts antibacterial properties to the orthodontic adhesives. An increase in the concentration of ZnO nanoparticles in orthodontic adhesive increases its antibacterial properties. We found the shear bond strength of the novel composite with up to 0.5wt% ZnO nanoparticles to be in a clinically acceptable range.
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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) | 0.000 | 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".