Laser Assisted Deposition of Silver Nanoparticles into DentinalTubules
Bibliographic record
Abstract
Decontamination of infected root canals and dentinal tubules is a crucial step of endodontic treatment.The aim of this study was to test antibacterial effect of silver nanoparticles, their possibility to penetrate dentinal tubules and to transport an timicrobial activities in deeper parts of root dentin.Silver microparticles and nanoparticles were used for detection of antibacterial activity against the typical endodontic pathogen Enterococcus faecalis (ATTC 29212).Interaction between silver micro-and nanoparticles and root canal walls as well as dentinal tubules was detected using laser confocal microscope and scanning electron microscope.Moreover SEM/Energy Dispersive X-ray Spectrometry was used for depiction of distribution of silver ions in the course of dentinal tubules.Root canals of experimental extracted teeth were pretreated by chemicals and 2780 nm laser.After filling of root canals by micro-and nanoparticles, laser shock waves initiating hydrodynamic effect were used for improvement of penetration of particles into tubules.Antibacterial effect tested on Enterococcus faecalis culture revealed 180 CFU/dish for microparticles and 70 CFU/dish for nanoparticles in comparison with 520 CFU/dish of control cultivation without silver treatment.Laser and chemical pretreatment of root canal showed clean surface without smear layer and very good conditions for penetration of nanoparticles into dentinal tubules.According to the SEM examination, only nanoparticles were able to penetrate into dentinal tubules and they were mostly completely fulfilled by nanomaterial to the depth around 60 µm.Afterwards, EDS point analysis was used to get more details about dentinal tubules infiltration, which was detectable up to the depth of 120 µm.Within the limits of present study, it may be concluded that nanosilver can be succesfully incorporated into antibacterial strategy against E. faecalis and its combination with laser shock waves is promissing method for killing bacteria in deeper parts of dentinal tubules.
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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) | 0.001 | 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".