MétaCan
Menu
Back to cohort
Record W4319786932 · doi:10.1016/j.ejphar.2023.175589

A peripherally restricted cannabinoid-1 receptor inverse agonist promotes insulin secretion and protects from cytokine toxicity in human pancreatic islets

2023· article· en· W4319786932 on OpenAlexafffund
Anindya Ghosh, Marie‐Line Peyot, Yat Hei Leung, François Ravenelle, S.R. Murthy Madiraju, Marc Prentki

Bibliographic record

VenueEuropean Journal of Pharmacology · 2023
Typearticle
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsInversago Pharma (Canada)Université de Montréal
FundersMitacs
KeywordsEndocrinologyInternal medicinePancreatic isletsInsulinAgonistInverse agonistIsletBiologyCytokineReceptorMedicine

Abstract

fetched live from OpenAlex

The cannabinoid receptor CB1R is expressed in pancreatic β-cells; CB1R increased activity is associated with diabetes, obesity, cardiovascular disorders as well as decreased insulin secretion and insulin resistance. CB1R was shown to signal through G-protein coupling as well as β-arrestins in β-cells. Peripherally restricted CB1R inverse agonists purportedly have beneficial effects on insulin secretion in β-cells, without the unwanted effects in the central nervous system. Here we show that a peripherally restricted CB1R inverse agonist, MRI-1891, augments glucose stimulated insulin secretion in isolated human pancreatic islets and mouse islets. The insulin secretion enhancing effect of MRI-1891 is comparable to exendin-4, an analogue of the glucagon like peptide-1 (GLP1). Moreover, MRI-1891 treatment protects isolated human islet cells against cytokine-induced apoptosis, similar to exendin-4. Thus, MRI-1891, a new class of CB1R inverse agonist, may be considered a potential therapeutic for both type 1 and type 2 diabetes because of its ability to protect pancreatic β-cells from cytokine toxicity and to promote insulin secretion.

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 score0.002
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 integrity0.0000.001
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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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

Citations8
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueEuropean Journal of PharmacologySame topicCannabis and Cannabinoid ResearchFrench-language works237,207