Analysis of Potential Anti-Cancer Effects of Cannabinol and Cannabidiol using HCC1806 and HEK293 Cell Lines
Bibliographic record
Abstract
Humans produce endocannabinoids that act as neuromodulators in the endocannabinoid system. They bind to Gαi protein-coupled cannabinoid receptors to control the release of many neurotransmitters. Cannabinoids receptor 1 (CB1) mediates psychoactive effects through its location mostly in the central nervous system while Cannabinoid receptor 2 (CB2) regulates various immune responses through its location in peripheral tissues. The endocannabinoid system has been used as a molecular target by research to treat diseases such as multiple sclerosis, cardiovascular disorders, obesity and inflammatory pain. Thus, the endocannabinoid system is a potential molecular target to treat cancer. With the proposed legalization of recreational marijuana and with growing number of patients using cannabis for medicinal purpose, there is an urgent need to provide data on potential medicinal value of cannabis and cannabinoids. The Cannabis Sativa plant naturally synthesizes numerous different cannabinoids of which (CBN) and cannabidiol (CBD) have promising properties in cancer treatment. CBD is a phytocannabinoid known for its anticonvulsant and anti-nausea properties. Previous research suggests that CBD can target breast cancer cells while preserving normal cells. CBN is another phyotocannabinoid with anti-inflammatory properties that can potentially aid to reduce inflammation resulting from cancer. This study aims to determine if CBN and CBD have an effect on cancer cells and normal cells. We hypothesize that we may observe an increase in apoptosis of cancer cells treated with the two compounds but no effect or perhaps even a slight increase in normal cell growth. Preliminary data in lab suggests that these compounds have anti-cancer properties and we want to solidify this evidence through repetition of the experiment. Various concentrations of CBN and CBD were administered separately to HCC1806 breast cancer cells and HEK293 normal human kidney cells for 24 hours before conducting apoptosis and cell cycle assays to determine if the cannabinoids had induced cell death or affected proliferation. *Indicates presenter
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".