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Record W1927558143 · doi:10.1111/bph.12706

Recent advances on the δ opioid receptor: from trafficking to function

2014· review· en· W1927558143 on OpenAlexafffund
Louis Gendron, Nitish Mittal, Hélène Beaudry, Wendy Walwyn

Bibliographic record

VenueBritish Journal of Pharmacology · 2014
Typereview
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsUniversité de Sherbrooke
FundersNational Institute on Drug AbuseFonds de Recherche du Québec - SantéCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsReceptorSignallingG protein-coupled receptorOpioidNeuroscienceOpioid receptorFunctional selectivitySignal transductionFunction (biology)Second messenger systemCell biologyBiologyGenetics

Abstract

fetched live from OpenAlex

UNLABELLED: Within the opioid family of receptors, δ (DOPrs) and μ opioid receptors (MOPrs) are typical GPCRs that activate canonical second-messenger signalling cascades to influence diverse cellular functions in neuronal and non-neuronal cell types. These receptors activate well-known pathways to influence ion channel function and pathways such as the map kinase cascade, AC and PI3K. In addition new information regarding opioid receptor-interacting proteins, downstream signalling pathways and resultant functional effects has recently come to light. In this review, we will examine these novel findings focusing on the DOPr and, in doing so, will contrast and compare DOPrs with MOPrs in terms of differences and similarities in function, signalling pathways, distribution and interactions. We will also discuss and clarify issues that have recently surfaced regarding the expression and function of DOPrs in different cell types and analgesia. LINKED ARTICLES: This article is part of a themed section on Opioids: New Pathways to Functional Selectivity. To view the other articles in this section visit http://dx.doi.org/10.1111/bph.2015.172.issue-2.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.936
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.064
GPT teacher head0.349
Teacher spread0.286 · 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.

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

Citations77
Published2014
Admission routes2
Has abstractyes

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