Dual effectiveness of a novel all-in-one endodontic irrigating solution in antibiofilm activity and smear layer removal
Bibliographic record
Abstract
The continuous destruction of dental hard tissues increases the risk of bacterial invasion, which leads to pulp infections. Irrigation is critical for successful root canal treatment in terms of infection control. However, no single irrigant covers all of the functions demanded, including antibiofilm and tissue-dissolving activities. The aim of this study was to investigate the antimicrobial properties of Triton, an all-in-one irrigant, on Enterococcus faecalis and multispecies oral biofilms in dentin canals, as well as its ability to remove the smear layer. Dentin blocks (192 specimens) were prepared from single-root human teeth and then assigned to 48 groups (24 groups for each biofilm type). Serial centrifugation was used for bacterial introduction into dentinal tubules. After 3 weeks, half of the specimens were created a uniform smear layer. The following treatments were applied: short time (separate): Triton, 6% NaOCl, 2% NaOCl, and water (all for 3 min); short time (combined): Triton (3 + 1 min), 6% NaOCl +17% EDTA (3 + 1 or 2 + 1 min), and 2% NaOCl +17% EDTA (3 + 1 min); and long time: Triton (3 + 3 min), 6% NaOCl (5 min), 6% NaOCl +17% EDTA (5 + 1 min), and water (3 + 3 min). Confocal laser scanning microscopy and scanning electron microscopy were employed to examine the antimicrobial activity and smear layer removal, respectively. The results revealed that despite the absence or presence of the smear layer, Triton (3 + 3 min) showed the highest killing for both tested biofilms (61.53%–72.22%) among all groups ( p < 0.05). Furthermore, the smear layer was removed by Triton after 3 + 3 min, exposing open dentin canals. These findings demonstrated that Triton can provide dual benefits of antibiofilm and smear layer removal capabilities simultaneously, indicating a simplified and effective strategy for application in root canal treatment.
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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 | 0.001 | 0.001 |
| 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".