Chelating and antibacterial properties of chitosan nanoparticles on dentin
Bibliographic record
Abstract
OBJECTIVES: The use of chitosan nanoparticles (CNPs) in endodontics is of interest due to their antibiofilm properties. This study was to investigate the ability of bioactive CNPs to remove the smear layer and inhibit bacterial recolonization on dentin. MATERIALS AND METHODS: One hundred bovine dentin sections were divided into five groups (n = 20 per group) according to the treatment. The irrigating solutions used were 2.5% sodium hypochlorite (NaOCl) for 20 min, 17% ethylenediaminetetraacetic acid (EDTA) for 3 min and 1.29 mg/mL CNPs for 3 min. The samples were irrigated with either distilled water (control), NaOCl, NaOCl-EDTA, NaOCl-EDTA-CNPs or NaOCl-CNPs. After the treatment, half of the samples (n = 50) were used to assess the chelating effect of the solutions using portable scanning electronic microscopy, while the other half (n = 50) were infected intra-orally to examine the post-treatment bacterial biofilm forming capacity. The biovolume and cellular viability of the biofilms were analysed under confocal laser scanning microscopy. The Kappa test was performed for examiner calibration, and the non-parametric Kruskal-Wallis and Dunn tests (p < 0.05) were used for comparisons among the groups. RESULTS: The smear layer was significantly reduced in all of the groups except the control and NaOCl groups (p < 0.05). The CNPs-treated samples were able to resist biofilm formation significantly better than other treatment groups (p < 0.05). CONCLUSIONS: CNPs could be used as a final irrigant during root canal treatment with the dual benefit of removing the smear layer and inhibiting bacterial recolonization on root dentin.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".