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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".