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The opioid system in depression

2022· review· en· W4288760145 on OpenAlexfundno aff
Luke A. Jelen, James Stone, Allan H. Young, Mitul A. Mehta

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2022
Typereview
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsnot available
FundersNational Institute of Mental HealthMedical Research CouncilVictoria General Hospital FoundationH. Lundbeck A/SCanadian Institutes of Health ResearchNational Institute for Health and Care ResearchKing's College LondonNational Alliance for Research on Schizophrenia and DepressionMichael Smith Health Research BCUniversity of SussexWellcome Trust
KeywordsOpioidergicNeuroscienceκ-opioid receptorOpioidNeurochemicalMood disordersAntidepressantEndogenous opioidPsychologyOpioid receptorDepression (economics)Animal models of depressionMedicinePharmacologyPsychiatryReceptorInternal medicineHippocampus(+)-NaloxoneAnxiety

Abstract

fetched live from OpenAlex

Opioid receptors are widely distributed throughout the brain and play an essential role in modulating aspects of human mood, reward, and well-being. Accumulating evidence indicates the endogenous opioid system is dysregulated in depression and that pharmacological modulators of mu, delta, and kappa opioid receptors hold potential for the treatment of depression. Here we review animal and clinical data, highlighting evidence to support: dysregulation of the opioid system in depression, evidence for opioidergic modulation of behavioural processes and brain regions associated with depression, and evidence for opioidergic modulation in antidepressant responses. We evaluate clinical trials that have examined the safety and efficacy of opioidergic agents in depression and consider how the opioid system may be involved in the effects of other treatments, including ketamine, that are currently understood to exert antidepressant effects through non-opioidergic actions. Finally, we explore key neurochemical and molecular mechanisms underlying the potential therapeutic effects of opioid system engagement, that together provides a rationale for further investigation into this relevant target in the treatment of depression.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.002

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 head0.261
GPT teacher head0.410
Teacher spread0.149 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
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

Citations95
Published2022
Admission routes1
Has abstractyes

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