5-Aminolevulinic Acid Gel Associated with Light-Emitting Diode Modulates the <i>in Vitro</i> Subgingival Multispecies Oral Biofilm
Bibliographic record
Abstract
Objective: This study assessed the impact of a 5% combination of 5-aminolevulinic acid (5-ALA) (Aladent) in a multispecies in vitro biofilm model subgingival pathogens. Methods: The 33-species biofilm model was established in the Calgary Biofilm Device during a duration of 7 days. The biofilm treatments comprised various groups: control, light-emitting diode (LED), Aladent (ALADA), and Aladent with LED (ALAD+L), administered on day 6. The Aladent was in contact with the biofilm for 45 min before to the 7-min LED treatment. The LED (λ = 630 nm, power ≈ 380 mW/cm 2 ) was placed 2 mm from the biofilm. Subsequently, during 7 days of biofilm formation, the metabolic activity of the biofilms was assessed utilizing triphenyltetrazolium chloride, and the presence of 33 bacterial species was evaluated through DNA–DNA hybridization. Results: The findings indicated that the ALAD+L treatment was the sole intervention demonstrating a statistically significant reduction (∼70%) in the metabolic activity of the biofilms relative to the control group. Moreover, ALAD+L markedly diminished the overall biofilm count and the average counts of five bacterial species: S. intermedius , V. parvula , A. israelii , P. gingivalis , and E. saburreum . Conclusion: The integration of the photosensitizer Aladent with LED application significantly diminished metabolic activity and bacterial species count in the multispecies subgingival biofilm model, indicating substantial promise for the treatment of peri-implantitis.
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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".