Antimicrobial Denture Material Synthesized from Poly(methyl methacrylate) Enriched with Cannabidiol Isolates
Bibliographic record
Abstract
Cannabidiol (CBD), derived from the Cannabis plant, has shown potential in dentistry for its antimicrobial properties, particularly against oral bacteria. Denture-associated infections, a common issue among denture wearers, present a challenge in antimicrobial enhancements to poly(methyl methacrylate) (PMMA), the primary material for dentures due to its favorable physical and aesthetic qualities. To address this, researchers developed PMMA denture coatings infused with CBD nanoparticles. The CBD coatings were synthesized using UV curing and characterized via 1H NMR, SEM, and FTIR spectroscopies. Antimicrobial activity was assessed against Staphylococcus aureus, Escherichia coli, and Streptococcus agalactiae. CBD demonstrated significant bactericidal effects on Gram-positive bacteria with a minimum inhibitory concentration (MIC) of 2–2.5 µg/mL and a minimum bactericidal concentration (MBC) of 10–20 µg/mL but was ineffective against planktonic Gram-negative bacteria. However, biofilm studies revealed a 99% reduction in biofilm growth for both Gram-positive and Gram-negative bacteria on CBD-infused PMMA compared to standard PMMA. The CBD disrupted bacterial cell walls, causing lysis. Dissolution studies indicated effective release of CBD molecules, crucial for antimicrobial efficacy. This study highlights CBD’s potential for antibiotic-free denture coatings, reducing dental biofilms and plaque formation, and improving oral health outcomes.
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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) | 0.002 | 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".