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NON‐PSYCHOACTIVE CANNABINOIDS (CBD/CBG) ACT VIA CANNABINOID CB2/CB1 RECEPTORS TO REGULATE INTESTINAL MYOFIBROBLAST METABOLIC ACTIVITY AND TO INHIBIT FORSKOLIN‐MEDIATED ELEVATION OF CYCLIC‐AMP

2020· article· en· W3016519186 on OpenAlexaffabout
Vivek Krishna Pulakazhi Venu, Mahmoud Saifeddine, Koichiro Mihara, Brishna Kamal, Daniel Lantela, Morley D. Hollenberg

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsAcuitas Therapeutics (Canada)University of Calgary
Fundersnot available
KeywordsCannabinoid receptor type 2Cannabinoid receptorCannabinoidCell biologyChemistryMyofibroblastSignal transductionReceptorPharmacologyFibrosisInternal medicineBiologyBiochemistryMedicine

Abstract

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Intestinal fibrosis, a common complication of inflammatory bowel disease (IBD), comprises an excessive accumulation of scar tissue in the intestinal wall. Fibrosis is thought to result from an aberrant response to injury resulting in excessive extracellular matrix (ECM) deposition, which can lead to tissue stiffening and intestinal stricture formation. Activation of the cannabinoid‐receptor‐CB2 has been reported to regulate mesenchymal cell function and to dampen fibrogenic signaling. More recently, non‐psychoactive cannabinoids, Cannabdiol (CBD) and Cannabigerol (CBG), have been found to be associated with anti‐inflammatory signalling that dampens inflammation. Here, we tested the hypothesis that CBD and CBG, via cannabinoid CB1/CB2 receptors, could function as potentially negative regulators of intestinal inflammation‐associated fibrosis through modulating intestinal myofibroblast function and triggering CB2/CB2 signalling. The effects of CBD and CBG on TGF‐β induced cell metabolic activity (MTT assay) were examined using cultured myofibroblast cells isolated from mouse and human intestine. Cannabinoid‐regulated signal transduction pathways were assessed using HEK‐293 cells transfected to express human CB2 and CB2 – red‐fluorescent‐protein‐tagged receptors. We found that at 48h and 72h TGF‐β induced cell metabolic activity was attenuated by 5% and 12% compared to TGF‐β‐treatment alone by increasing concentrations of CBG (10μM). Further, CBD (500nM) was able to inhibit TGF‐β induced cell metabolic activity by 12% and 25%, compared to TGF‐β alone at 48h and 72h. In CB1/CB2‐expressing HEK cells, preliminary data showed that both CBD and CBG, acting via CB2, were able to diminish forskolin (0.5 μM)‐increased cyclic‐AMP production, without affecting changes in intracellular calcium, indicating activation of Gi. In cells co‐expressing CB1/CB2 receptors, both CBG (100 nM) and CBD (20 nM) inhibited high forskolin concentration(5–10 μM)‐stimulated cAMP production. Nontransfected HEK cells showed no responses to either CBD or CBG. We conclude that the non‐psychoactive cannabinoids, CBD/CBG can regulate non‐neuronal cells, including intestinal myofibroblasts and kidney HEK cells via both CB2 and CB1 receptors. Support or Funding Information Supported by grants from the Canadian Institutes of Health Research for MDH and Mitacs postdoctoral fellowships for VKPV

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.286
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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