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Record W3201322193 · doi:10.3389/fnins.2021.741503

Multiple Sclerosis and the Endogenous Opioid System

2021· review· en· W3201322193 on OpenAlexafffund
Zoë Dworsky-Fried, Caylin I. Chadwick, Bradley J. Kerr, Anna M.W. Taylor

Bibliographic record

VenueFrontiers in Neuroscience · 2021
Typereview
Languageen
FieldNeuroscience
TopicNeuropeptides and Animal Physiology
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsEndogenous opioidMultiple sclerosisExperimental autoimmune encephalomyelitisMedicinePathogenesisCentral nervous systemOpioidEndogenyNeuroscienceOpioidergicOpioid peptideImmunologyEncephalomyelitisReceptorBiologyInternal medicine(+)-Naloxone

Abstract

fetched live from OpenAlex

Multiple sclerosis (MS) is an autoimmune disease characterized by chronic inflammation, neuronal degeneration and demyelinating lesions within the central nervous system. The mechanisms that underlie the pathogenesis and progression of MS are not fully known and current therapies have limited efficacy. Preclinical investigations using the murine experimental autoimmune encephalomyelitis (EAE) model of MS, as well as clinical observations in patients with MS, provide converging lines of evidence implicating the endogenous opioid system in the pathogenesis of this disease. In recent years, it has become increasingly clear that endogenous opioid peptides, binding μ- (MOR), κ- (KOR) and δ-opioid receptors (DOR), function as immunomodulatory molecules within both the immune and nervous systems. The endogenous opioid system is also well known to play a role in the development of chronic pain and negative affect, both of which are common comorbidities in MS. As such, dysregulation of the opioid system may be a mechanism that contributes to the pathogenesis of MS and associated symptoms. Here, we review the evidence for a connection between the endogenous opioid system and MS. We further explore the mechanisms by which opioidergic signaling might contribute to the pathophysiology and symptomatology of MS.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.127
GPT teacher head0.270
Teacher spread0.142 · 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

Citations17
Published2021
Admission routes2
Has abstractyes

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