The Effects of Cannabidiol on Interleukin-2 Production and Viability of Human T Lymphocytes
Bibliographic record
Abstract
The legalization of cannabis in Canada resulted in an increased consumption of cannabis and cannabis-related products by the Canadian population. Cannabidiol (CBD) is one of the active chemicals within the cannabis plant. It is of particular interest because it is thought to suppress T cells, which are an integral part of the adaptive immune system. Suppressing T cells is not the desired effect for most individuals consuming cannabis. One of the problems with commercially available CBD is a lack of information on safe or effective amounts. More research needs to be conducted to determine if CBD at various doses causes health problems or can be therapeutic in patients with autoimmunity. The objectives were: (1) to determine the most effective way to deliver CBD in vitro by testing different solvents, (2) to investigate whether CBD alters the amount of the IL-2 cytokine produced by T cells, and (3) to examine the effects of CBD dose on T cell death. The findings demonstrated (1) glycerol was determined to be a better solvent compared to DMSO, (2) CBD causes cell death and decreased Il-2 production in T cells and PBMCs at higher doses. \n \nThe relevance of these findings is to better understand the interplay between CBD and the immune system by elucidating what a safe and effective dose of CBD might be for suppressing cytokine production in T cells. The results of thesis also pave the ground for future studies on the mechanism of action of CBD on T cells.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".