Energy Dose Parameters Affect Antimicrobial Photodynamic Therapy–Mediated Eradication of Periopathogenic Biofilm and Planktonic Cultures
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the in vitro efficacy of a commercially available aPDT system in eradication of the periopathogens Porphyromonas gingivalis, Fusobacterium nucleatum, and Aggregatibacter actinomycetemcomitans in both planktonic and biofilm cultures. BACKGROUND DATA: Antimicrobial photodynamic therapy (aPDT) is an effective antibacterial approach in vitro; however, few data are available regarding effective light-energy parameters. MATERIALS AND METHODS: Planktonic and biofilm cultures of periopathogens were exposed to a methylene blue-based formulation and irradiated with a 670-nm nonthermal diode laser. Energy doses were varied from 2.3 to 9.4 J/cm(2) through adjustments in illumination time and a constant power density. Controls consisted of no treatment, light only, and photosensitizer only. Temperature changes were recorded in experimental samples before and after illumination. RESULTS: aPDT with an energy dose of 9.4 J/cm(2) was effective in eradicating P. gingivalis, F. nucleatum, and A. actinomycetemcomitans in biofilm and planktonic form. Reductions from control in planktonic cultures at this energy dose were 6.8 +/- 0.7, 5.2 +/- 0.6, and 1.9 +/- 0.6 log(10), respectively, whereas biofilm reductions were 4.5 +/- 1.2, 3.4 +/- 1.1, and 4.9 +/- 1.4 log(10). Decreasing the treatment time produced an energy dose-dependent killing effect in both models. Changes in sample temperature did not exceed 3 degrees C under these exposure parameters. CONCLUSION: This study demonstrated that three important periopathogens are susceptible to aPDT-mediated killing, regardless of whether they are present in planktonic or biofilm form. Furthermore, a clear energy dose-dependence exists with this treatment that should to be taken into account when determining optimal treatment times in clinical application.
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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".