Assessment of Antimicrobial Photodynamic Therapy With Curcumin on the Shear Bond Strength of Orthodontic Bracket: An In Vitro Study
Bibliographic record
Abstract
Background: Antimicrobial photodynamic therapy (aPDT) is a noninvasive treatment approach that eradicates a broad variety of harmful microorganisms. This study aimed to examine the impact of aPDT with curcumin on the shear bond strength (SBS) of orthodontic bracket to enamel. Materials and Methods: In this in vitro study, 45 intact human premolars were randomly divided into three groups ( N = 15): the control group, aPDT group with curcumin 500 mg/L, and aPDT group with curcumin 1000 mg/L. After performing aPDT, the orthodontic brackets were attached to the surface of the teeth and then samples were thermocycled for 3000 cycles. The brackets were then debonded using a universal testing machine. The SBS and adhesive remnant index (ARI) were calculated. Data were analyzed in SPSS25 software. ANOVA with post hoc tests was used to compare SBS among groups. Results: The findings of this study indicated that the aPDT with curcumin 500 mg/L showed the highest SBS mean, while the control group had the lowest. The average SBS difference between the control group and the curcumin 500 mg/L group as well as the curcumin 1000 mg/L group was statistically significant ( p < 0.05). However, the difference between the average SBS of the two curcumin groups was not statistically significant ( p = 0.184). The experimental groups using curcumin had a considerably higher ARI value compared to the control group ( p ≤ 0.001). However, this index was not statistically different among the C 500 and C 1000 groups ( p > 0.05). Conclusions: aPDT with curcumin increases the ARI and the bond strength of the orthodontic brackets and can be used as an effective method to reduce microbial plaque and inflammation before bonding in orthodontic patients.
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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 | 0.001 | 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 | 0.000 | 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".