Efficacy of the photon‐initiated photoacoustic streaming combined with different solutions on <scp><i>Enterococcus faecalis</i></scp> in the root canals
Bibliographic record
Abstract
This study aimed to evaluate the efficacy of different irrigation solutions used in photon-initiated photoacoustic streaming (PIPS) or conventional needle irrigation (CNI) for eradication of Enterococcus faecalis from artificial root canals. Altogether, 240 artificial root canal samples were included. The models were split and incubated for 2 days to allow formation of E. faecalis biofilm. The models were randomly divided into two groups (n = 120): CNI and laser-activated irrigation (LAI). Each group was divided into six subgroups according to different irrigation solutions: distilled water, 1% sodium hypochlorite (NaOCl), 2% NaOCl, 5.25% NaOCl, MTAD, and chlorhexidine, respectively. After irrigation, half of the samples (n = 10) were assessed immediately, and the other half of the samples (n = 10) were incubated for 6 hr. Bacterial suspensions were obtained from all samples before and after irrigation, and after incubation, and were quantified adenosine 5'-triphosphate (ATP) assay kit. The biofilms were examined using fluorescent microscopy and analyzed by Image Pro Plus software. Significant reduction of ATP, average fluorescence density after irrigation, and growth after incubation was obtained in LAI group than in CNI group (p < .05). LAI can improve bacteriostasis effect of 2% NaOCl (p < .05). PIPS improved the antibacterial effect of the 2% NaOCl used in root canal therapy.
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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 | 0.001 |
| 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".