Oxidative Stress and Autophagy Mediate Anti-Cancer Properties of Cannabis Derivatives in Human Oral Cancer Cells
Bibliographic record
Abstract
Cannabinoids, the active components of cannabis exert palliative effects in cancer patients by preventing nausea, vomiting and pain as well as by stimulating appetite. Recent studies indicated that cannabinoids could be helpful in treating certain rare forms of cancer and other inflammatory diseases. The objective of this study was to investigate the cytotoxic effect of a cannabinoid mixture (CM) in oral cells. Thus, normal and cancer gingival cells were treated with different concentrations of CM to evaluate their proliferation by MTT assay, cytotoxicity by using LDH assay, colony formation with crystal violet and migration by the scratch method. In addition, apoptosis, autophagy, oxidative stress, antioxidant level, DNA damage and the mitochondrial membrane potential (ΔΨm) generated by proton pumps were measured by flow cytometry. Furthermore, deactivation of the key signaling pathways involved in cancer progression such as NF-κB, ERK1/2, p38, STAT1, STAT3, STAT5 was also evaluated by this technique. These outcomes indicate that CM, at a concentration higher than 0.1 µg/mL, provokes high cytotoxicity in Ca9-22 oral cancer cells but not in GMSM-K gingival normal cells. Apoptosis, autophagy, antioxidant levels and mitochondrial stress as well as DNA damage in oral cells were increased following exposure to low concentration (1 µg/mL). In addition, major signaling pathways that are involved such as MAPKase, STATs and NF-κB pathways were inhibited by CM as well as cell migration. Our results suggest that cannabinoids could potentially have a beneficial effect on oral cancer therapy.
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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 | 0.000 |
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".