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Record W3163484416 · doi:10.3390/cells10061279

Role of the Endocannabinoid System in the Adipose Tissue with Focus on Energy Metabolism

2021· review· en· W3163484416 on OpenAlexafffund
Volatiana Rakotoarivelo, Jyoti Sihag, Nicolas Flamand

Bibliographic record

VenueCells · 2021
Typereview
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsEndocannabinoid systemCannabinoid receptorAdipose tissueWhite adipose tissueRimonabantEndocrinologyInternal medicineLeptinGhrelinAppetiteBrown adipose tissueBiologyReceptorHormoneMedicineAntagonistObesity

Abstract

fetched live from OpenAlex

The endocannabinoid system is involved in a wide range of processes including the control of energy acquisition and expenditure. Endocannabinoids and their receptors are present in the central nervous system but also in peripheral tissues, notably the adipose tissues. The endocannabinoid system interacts with two main hormones regulating appetite, namely leptin and ghrelin. The inhibitory effect of the cannabinoid receptor 1 (CB1) antagonist rimonabant on fat mass suggested that the endocannabinoid system can also have a peripheral action in addition to its effect on appetite reduction. Thus, several investigations have focused on the peripheral role of the endocannabinoid system in the regulation of metabolism. The white adipose tissue stores energy as triglycerides while the brown adipose tissue helps to dissipate energy as heat. The endocannabinoid system regulates several functions of the adipose tissues to favor energy accumulation. In this review we will describe the presence of the endocannabinoid system in the adipose tissue. We will survey the role of the endocannabinoid system in the regulation of white and brown adipose tissue metabolism and how the eCB system participates in obesity and metabolic 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.975
Threshold uncertainty score0.836

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.269
Teacher spread0.253 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations47
Published2021
Admission routes2
Has abstractyes

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