Clinical applications of antimicrobial photodynamic therapy in dentistry
Bibliographic record
Abstract
Given the emergence of resistant bacterial strains and novel microorganisms that globally threaten human life, moving toward new treatment modalities for microbial infections has become a priority more than ever. Antimicrobial photodynamic therapy (aPDT) has been introduced as a promising and non-invasive local and adjuvant treatment in several oral infectious diseases. Its efficacy for elimination of bacterial, fungal, and viral infections and key pathogens such as Streptococcus mutans , Porphyromonas gingivalis , Candida albicans, and Enterococcus faecalis have been investigated by many invitro and clinical studies. Researchers have also investigated methods of increasing the efficacy of such treatment modalities by amazing developments in the production of natural, nano based, and targeted photosensitizers. As clinical studies have an important role in paving the way towards evidence-based applications in oral infection treatment by this method, the current review aimed to provide an overall view of potential clinical applications in this field and summarize the data of available randomized controlled clinical studies conducted on the applications of aPDT in dentistry and investigate its future horizons in the dental practice. Four databases including PubMed (Medline), Web of Science, Scopus and Embase were searched up to September 2022 to retrieve related clinical studies. There are several clinical studies reporting aPDT as an effective adjunctive treatment modality capable of reducing pathogenic bacterial loads in periodontal and peri-implant, and persistent endodontic infections. Clinical evidence also reveals a therapeutic potential for aPDT in prevention and reduction of cariogenic organisms and treatment of infections with fungal or viral origins, however, the number of randomized clinical studies in these groups are much less. Altogether, various photosensitizers have been used and it is still not possible to recommend specific irradiation parameters due to heterogenicity among studies. Reaching effective clinical protocols and parameters of this treatment is difficult and requires further high quality randomized controlled trials focusing on specific PS and irradiation parameters that have shown to have clinical efficacy and are able to reduce pathogenic bacterial loads with sufficient follow-up periods.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".