Electrokinetic transport and distribution of antibacterial nanoparticles for endodontic disinfection
Bibliographic record
Abstract
AIM: To assess a novel, noninvasive intervention capable of mobilizing charged antibacterial nanoparticles to the apical portions of the root canal system, utilizing the principles of electrokinetics. METHODS: Experiments were conducted in three stages. Stage-1: A computer model was generated to predict and visualize the electric field and current density distribution generated by the proposed intervention. Stage-2: Transport of chitosan nanoparticles (CSnp) was evaluated qualitatively using a transparent microfluidic model with fluorescent-labelled CSnp. Stage-3: An ex vivo model was utilized to study the antimicrobial efficacy of the proposed treatment against 3-week-old monospecies E. faecalis biofilms. Scanning electron microscopy (SEM) was also utilized in this stage to confirm the deposition of CSnp. RESULTS: The results of the computer simulations predicted an electric field and current density that reach their maxima at the apical constriction of the root canal. Correspondingly, the microfluidic experiments demonstrated rapid, controlled CSnp transport throughout the simulated root canal anatomy with subsequent distribution and deposition in the apical constriction as well as periapical regions. Infected root canals when subjected to the novel treatment method resulted in a mean bacterial reduction of 2.1 log CFU. SEM analysis revealed electrophoretic deposition of chitosan nanoparticles onto the root canal dentine walls in the apical region. CONCLUSION: The findings from this study demonstrate that the combination of cationic antibacterial nanoparticles with a low-intensity electric field results in particle transportation (electrophoresis) and deposition within the root canal. This results in a synergistic antibiofilm efficacy and has the potential to enhance root canal disinfection.
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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".