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Record W2736658723 · doi:10.1111/bph.13957

P2X receptor channels in chronic pain pathways

2017· review· en· W2736658723 on OpenAlexafffund
Louis‐Philippe Bernier, Ariel R. Ase, Philippe Séguéla

Bibliographic record

VenueBritish Journal of Pharmacology · 2017
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdenosine and Purinergic Signaling
Canadian institutionsMontreal Neurological Institute and HospitalMcGill UniversityUniversity of British ColumbiaVancouver Coastal Health
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchMichael Smith Health Research BC
KeywordsNeuroscienceChronic painMedicineBiology

Abstract

fetched live from OpenAlex

Chronic pain is a highly prevalent debilitating condition for which treatment options remain limited for many patients. Ionotropic ATP signalling through excitatory and calcium-permeable P2X receptor channels is now rightfully considered as a critical player in pathological pain generation and maintenance; therefore, their selective targeting represents a therapeutic opportunity with promising yet untapped potential. Recent advances in the structural, functional and pharmacological characterization of rodent and human ATP-gated P2X receptor channels have shed brighter light on the role of specific subtypes in the pathophysiology of chronic inflammatory, neuropathic or cancer pain. Here, we will review the contribution of P2X3, P2X4 and P2X7 receptors to chronic pain and discuss the opportunities and challenges associated with the pharmacological manipulation of their function. LINKED ARTICLES: This article is part of a themed section on Recent Advances in Targeting Ion Channels to Treat Chronic Pain. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v175.12/issuetoc.

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 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 score0.006
Threshold uncertainty score0.020

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 studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.063
GPT teacher head0.365
Teacher spread0.302 · 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 source (direct Gemma or distilled Codex), 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

Citations195
Published2017
Admission routes2
Has abstractyes

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