Evaluation of the antimicrobial effect of cannabidiol (CBD) in a multispecies subgingival biofilm model
Bibliographic record
Abstract
Background This study evaluated the antimicrobial effect of cannabidiol (CBD) on a multi-species subgingival biofilm model.Materials and methods Biofilms were formed using 33 bacterial species on a Calgary device. Two protocols were tested: (A) biofilm in contact with CBD (125, 250 and 500 µg/mL) and chlorhexidine 0.12% (CHX) for the entire period; (B) treatments with CBD (500 and 1000 µg/mL) and CHX started on day 3, twice a day, for 1 minute. The total biofilm counts, the proportion of complexes, and the counts of each species were evaluated by DNA-DNA hybridization (Checkerboard).Results In Experiment A, CBD at concentrations of 250 and 500 µg/mL, as well as CHX, significantly reduced the total biofilm count. At 500 µg/mL, CBD also decreased the proportion of the red complex and reduced the counts of 10 bacterial species, whereas CHX affected 20 species. In Protocol B, both CBD at 1000 µg/mL and CHX reduced the total biofilm count and the proportion of the red complex, while increasing the proportion of the green complex. Both protocols led to a reduction in Porphyromonas gingivalis and Tannerella forsythia.Conclusion CBD reduced the total bacterial count and the red complex, inhibiting known periodontal pathogens. Within the limitations, the results provide exploratory evidence that CBD may reduce the total bacterial count in the proposed polymicrobial biofilm model, including the red complex bacteria, and may thus be postulated as an inhibitor of known periodontal pathogens. However, future in vivo studies with robust sample sizes and standardized CFU-based quantification are required to confirm these findings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".