Effect of cannabidiol (CBD), a cannabis plant derivative, against <i>Candida albicans</i> growth and biofilm formation
Bibliographic record
Abstract
Fungal resistance to available antifungal molecules has emerged as a significant and continued threat. Several plants, including cannabis, have promising antimicrobial activities, potentially including those against C andida albicans. Thus, this study aimed to evaluate the antifungal activities of cannabidiol (CBD) against C. albicans. Yeast cells were treated once or twice with different concentrations (from 0 to 20 µg/mL) of CBD, showing a significant ( p < 0.05) decrease of the growth of C. albicans, with cell concentrations ranging from 5.1 × 106 cells/mL in the control to 1.8 × 106 cells/mL after one exposure to 20 µg/mL CBD. This growth reduction was greater after two exposures to CBD. After two exposures to 20 µg/mL CBD, the cell concentration was only 1.1 × 106 cells/mL. Such a growth decrease in C. albicans was confirmed by a reduced number of colony forming units and a lower MTT value compared to the control. The growth inhibition was supported by a significant ( p < 0.001) decrease in the yeast-to-hyphae transition, ranging from 20 ± 0.2% in the control to 2 ± 0.5% after exposure to 20 µg/mL CBD. Biofilm formation was also significantly reduced following CBD exposure. CBD at 10 and 20 µg/mL promoted the death of C. albicans through an apoptosis/necrotic pathway. Altogether, our results suggest the possible use of CBD, a cannabis derivative, to control C. albicans infection, including oral candidiasis. Highlights Cannabidiol (CBD) decreases the growth of C. albicans. CBD inhibits the yeast-to-hyphae transition. CBD reduces biofilm formation by C. albicans. CBD induces C. albicans death through necrosis.
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) | 0.000 | 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) | 0.001 | 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".