Cannabinol modulates the endocannabinoid system and shows <scp>TRPV1</scp> ‐mediated anti‐inflammatory properties in human keratinocytes
Bibliographic record
Abstract
Abstract Cannabinol (CBN) is a secondary metabolite of cannabis whose beneficial activity on inflammatory diseases of human skin has attracted increasing attention. Here, we sought to investigate the possible modulation by CBN of the major elements of the endocannabinoid system (ECS), in both normal and lipopolysaccharide‐inflamed human keratinocytes (HaCaT cells). CBN was found to increase the expression of cannabinoid receptor 1 (CB 1 ) at gene level and that of vanilloid receptor 1 (TRPV1) at protein level, as well as their functional activity. In addition, CBN modulated the metabolism of anandamide (AEA) and 2‐arachidonoylglicerol (2‐AG), by increasing the activities of N ‐acyl phosphatidylethanolamines‐specific phospholipase D (NAPE‐PLD) and fatty acid amide hydrolase (FAAH)—the biosynthetic and degradative enzyme of AEA—and that of monoacylglycerol lipase (MAGL), the hydrolytic enzyme of 2‐AG. CBN also affected keratinocyte inflammation by reducing the release of pro‐inflammatory interleukin (IL)‐8, IL‐12, and IL‐31 and increasing the release of anti‐inflammatory IL‐10. Of note, the release of IL‐31 was mediated by TRPV1. Finally, the mitogen‐activated protein kinases (MAPK) signaling pathway was investigated in inflamed keratinocytes, demonstrating a specific modulation of glycogen synthase kinase 3β (GSK3β) upon treatment with CBN, in the presence or not of distinct ECS‐directed drugs. Overall, these results demonstrate that CBN modulates distinct ECS elements and exerts anti‐inflammatory effects—remarkably via TRPV1—in human keratinocytes, thus holding potential for both therapeutic and cosmetic purposes.
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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".