Effects of Rare Phytocannabinoids on the Endocannabinoid System of Human Keratinocytes
Bibliographic record
Abstract
The decriminalization and legalization of cannabis has paved the way for investigations into the potential of the use of phytocannabinoids (pCBs) as natural therapeutics for the treatment of human diseases. This growing interest has recently focused on rare (less abundant) pCBs that are non-psychotropic compounds, such as cannabigerol (CBG), cannabichromene (CBC), Δ9-tetrahydrocannabivarin (THCV) and cannabigerolic acid (CBGA). Notably, pCBs can act via the endocannabinoid system (ECS), which is involved in the regulation of key pathophysiological processes, and also in the skin. In this study, we used human keratinocytes (HaCaT cells) as an in vitro model that expresses all major ECS elements in order to systematically investigate the effects of CBG, CBC, THCV and CBGA. To this end, we analyzed the gene and protein expression of ECS components (receptors: CB1, CB2, GPR55, TRPV1 and PPARα/γ/δ; enzymes: NAPE-PLD, FAAH, DAGLα/β and MAGL) using qRT-PCR and Western blotting, along with assessments of their functionality using radioligand binding and activity assays. In addition, we quantified the content of endocannabinoid(-like) compounds (AEA, 2-AG, PEA, etc.) using UHPLC-MS/MS. Our results demonstrated that rare pCBs modulate the gene and protein expression of distinct ECS elements differently, as well as the content of endocannabinoid(-like) compounds. Notably, they all increased CB1/2 binding, TRPV1 channel stimulation and FAAH and MAGL catalytic activity. These unprecedented observations should be considered when exploring the therapeutic potential of cannabis extracts for the treatment of human skin diseases.
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) | 0.002 | 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".