An In Vitro Diacetylcurcumin Study for Periodontitis: A New Approach to Controlling Subgingival Biofilms
Bibliographic record
Abstract
Background: Periodontal disease (PD) is a chronic inflammatory condition associated with dysbiotic biofilm, leading to the destruction of bone and periodontal ligament. Scaling and root planing (SRP) is the gold-standard treatment for PD, but some patients may not respond adequately, necessitating adjunctive therapies. This study investigated the antimicrobial activity of diacetylcurcumin (DAC), a modified curcumin, against multispecies subgingival biofilm associated with periodontitis. Methods: The biofilm, containing 40 bacterial species, was cultured for seven days in the Calgary apparatus. Treatments with DAC (200 μg/mL), 0.12% chlorhexidine (CHX), and a vehicle (control) were applied twice daily for 1 min, starting on the third day. On the seventh day, biofilms were analyzed for metabolic activity (MA) and bacterial counts via DNA-DNA hybridization. DAC toxicity was tested on Galleria mellonella larvae. Results: DAC reduced biofilm metabolic activity by 51%, while CHX achieved 88% reduction compared to the vehicle (p < 0.05). DAC also significantly decreased counts of key periodontal pathogens, including P. gingivalis, T. forsythia, P. intermedia, and A. actinomycetemcomitans (p < 0.05). At the tested concentration, DAC showed no toxicity in larvae. Conclusions: These findings suggest that DAC effectively reduces biofilm activity and periodontal pathogen counts, presenting a promising adjunctive therapy for PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".