Antimicrobial Studies of Cannabidiol as Biomaterials against superbug MRSA
Bibliographic record
Abstract
Due to its limited treatment options, multi-drug resistant bacteria such as Gram-positive methicillin-resistant Staphylococcus aureus (MRSA) still remains a serious public health threat. The creation of new compelling antimicrobial materials, antibiotics and optional methodologies, which are successful against resistant microbes, is earnestly required. The legalization of cannabis in Canada has provided a new opportunity to investigate the antimicrobial studies of both extracts and individual cannabinoids. This study investigates pure cannabidiol (CBD) isolated from Cannabis sativa by using a methodology of extraction, purification, characterization, and quantification of CBD. The shredded plant material was dissolved in ethanol, with the extract further purified using supercritical fluid chromatography (SFC) to obtain purified CBD. Product purity was confirmed by HPLC and NMR spectroscopy. CBD’s antibacterial activity on MRSA strain USA300 bacteria was studied using dilution series in liquid culture and disk diffusion assays to provide the minimum inhibitory concentration (MIC) and minimal bactericidal concentrations (MBC). The results showed that CBD exhibited a significant bactericidal effect on MRSA with the MIC value of 2.5 µg/mL and MBC of 10 µg/mL. CBD powder form gave a higher antimicrobial activity than its oil form in terms of the inhibition zone. This study shows that CBD exhibits good antimicrobial impact against the MRSA strain showing its utility for enabling a new antibiotic free method for treating MRSA infections. Keywords—CBD, antimicrobial activity, MRSA
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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 | 0.001 | 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 | 0.000 |
| 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".