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Record W2080198258 · doi:10.3390/cells3010152

Molecular Perspectives for mu/delta Opioid Receptor Heteromers as Distinct, Functional Receptors

2014· article· en· W2080198258 on OpenAlexaff
Edmund Ong, Catherine M. Cahill

Bibliographic record

VenueCells · 2014
Typearticle
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsQueen's University
Fundersnot available
KeywordsOpioid receptorOpioidδ-opioid receptorReceptorContext (archaeology)Neuroscienceμ-opioid receptorG protein-coupled receptorPharmacologyChemistryPsychologyBiologyBiochemistry

Abstract

fetched live from OpenAlex

Opioid receptors are the sites of action for morphine and the other opioid drugs. Abundant evidence now demonstrates that different opioid receptor types can physically associate to form heteromers. Understandings of the nature, behavior, and role of these opioid receptor heteromers are developing. Owing to their constituent monomers' involvement in analgesia, mu/delta opioid receptor (M/DOR) heteromers have been a particular focus of attention. There is now considerable evidence demonstrating M/DOR to be an extant and physiologically relevant receptor species. Participating in the cellular environment as a distinct receptor type, M/DOR availability is complexly regulated and M/DOR exhibits unique pharmacology from that of other opioid receptors (ORs), including its constituents. M/DOR appears to have a range of actions that vary in a ligand- (or ligands-) dependent manner. These actions can meaningfully affect the clinical effects of opioid drugs: strategies targeting M/DOR may be therapeutically useful. This review presents and discusses developments in these understandings with a focus on the molecular nature and activity of M/DOR in the context of therapeutic potentials.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

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)0.0010.001

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 head0.243
Teacher spread0.224 · 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 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

Citations28
Published2014
Admission routes1
Has abstractyes

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