Curcumin nanoparticles and blue laser irradiation in photothermal inactivation of selected oral pathogens in vitro
Bibliographic record
Abstract
In last years, devices emitting laser beam in blue spectrum has been the subject of substantial research also in dentistry.But improvement of bactericidal effect might be theoretically possible using exogenous chromophore under the terms of photothermal therapy.With respect to the colour, curcumin seems be a suitable chromophore for this method and we decided to use curcumin nanoparticles and evaluate their effect as a photosensitizer for 445 nm laser in effectiveness against selected G + and G -bacteria in vitro.Curcumin nanoparticles were prepared using ultrasound assisted solvent-antisolvent precipitation.Diode laser 445 nm (SIROLaser Blue) was used as a source of laser irradiation (100mW, 1 min.exposition time, energy density 7,68J/cm 2 ).Bacterial strains of Porphyromonas gingivalis, Parvimonas micra and Enterococcus faecalis were cultivated on solid media in marked areas covered by curcumin nanoparticles.After laser irradiation and subsequent anaerobic cultivation, the bacterial growth was evaluated.Irradiated areas without contact with curcumin nanoparticles showed intact bacterial colonies.Completely different quality was detectable in cases of irradiated colonies growing in contact with curcumin nanoparticles.Destruction of bacterial colonies was clearly visible and repeated cultivation of taken material was without positive response confirming presence of non-living cells inside of colonies.Results of our preliminary study showed promising direction of laser photothermal therapy using curcumin nanoparticles in reduction or elimination of oral microflora especially in locations with poor access for classic therapeutic effect.
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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".