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Record W4308801518 · doi:10.3389/fphys.2022.1066455

Non-psychotropic phytocannabinoid interactions with voltage-gated sodium channels: An update on cannabidiol and cannabigerol

2022· review· en· W4308801518 on OpenAlexafffund
Mohammad‐Reza Ghovanloo, Sulayman D. Dib‐Hajj, Samuel J. Goodchild, Peter C. Ruben, Stephen G. Waxman

Bibliographic record

VenueFrontiers in Physiology · 2022
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsSimon Fraser UniversityXenon Pharmaceuticals (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchParalyzed Veterans of AmericaYale University
KeywordsCannabidiolSodium channelIon channelPharmacologyChemistryMedicineNeuroscienceReceptorCannabisBiologyBiochemistrySodium

Abstract

fetched live from OpenAlex

Phytocannabinoids, found in the plant, Cannabis sativa, are an important class of natural compounds with physiological effects. These compounds can be generally divided into two classes: psychoactive and non-psychoactive. Those which do not impart psychoactivity are assumed to predominantly function via endocannabinoid receptor (CB) -independent pathways and molecular targets, including other receptors and ion channels. Among these targets, the voltage-gated sodium (Nav) channels are particularly interesting due to their well-established role in electrical signalling in the nervous system. The interactions between the main non-psychoactive phytocannabinoid, cannabidiol (CBD), and Nav channels were studied in detail. In addition to CBD, cannabigerol (CBG), is another non-psychoactive molecule implicated as a potential therapeutic for several conditions, including pain via interactions with Nav channels. In this mini review, we provide an update on the interactions of Nav channels with CBD and CBG.

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)0.001
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 score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.028
GPT teacher head0.326
Teacher spread0.298 · 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

Citations12
Published2022
Admission routes2
Has abstractyes

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