Combinations of Cannabinoids with Silver Salts or Silver Nanoparticles for Synergistic Antibiotic Effects against Methicillin-Resistant Staphylococcus aureus
Bibliographic record
Abstract
Silver has been shown to improve the antibiotic effects of other drugs against both Gram- positive and -negative bacteria. In this study, we investigated the antibiotic potential of cannabidiol (CBD), cannabichromene (CBC) and cannabigerol (CBG) and their acidic counterparts (CBDA, CBCA, CBGA) against Gram-positive bacteria and further explored the additive or synergistic effects of silver nitrate or silver nanoparticles using 96-well plate growth assays and viability (CFUs- colony-forming units). All six cannabinoids had strong antibiotic effects against MRSA with minimal inhibitory concentrations (MICs) of 2 mg/L for CBG, CBD and CBCA; 4 mg/L for CBGA; and 8 mg/L for CBC and CBDA. Using 96-well checkerboard assays, CBC, CBG and CBGA showed full or partial synergy with silver nitrate; CBC, CBDA and CBGA were fully synergistic with silver nanoparticles against MRSA. Using CFU assays, combinations of CBC, CBGA and CBG with either silver nitrate or silver nanoparticles, all at half or quarter MICs, demonstrated strong, time-dependent inhibition of bacterial growth (silver nitrate) and bactericidal effects (silver nanoparticles). These data will lead to further investigation into possible biomedical applications of specific cannabinoids in combination with silver salts or nanoparticles against drug-resistant Gram-positive bacteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".