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Record W2946708385 · doi:10.1080/19336950.2019.1615824

Cannabinoid interactions with ion channels and receptors

2019· review· en· W2946708385 on OpenAlexaff
Abeline R. Watkins

Bibliographic record

VenueChannels · 2019
Typereview
Languageen
FieldMedicine
TopicCannabis and Cannabinoid Research
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCannabidiolSodium channelEpilepsyIon channelCannabinoid receptorNeuroscienceCannabinoidCannabis sativaDrug discoveryBioinformaticsPharmacologyMedicineChemistryBiologyCannabisReceptorSodiumBiochemistryPsychiatry

Abstract

fetched live from OpenAlex

Cannabidiol (CBD), the non-psychoactive component of Cannabis sativa, acts on a diverse selection of membrane proteins with promising therapeutic potential in epilepsy and chronic pain. One such protein is the voltage-gated sodium channel (Nav). CBD shows a lack of specificity for sodium channels; however, the method of interaction is still unknown. In this review, we will outline the studies that report reproducible results of CBD and other cannabinoids changing membrane channel function, with particular interest on Nav. Nav are implicated in fatal forms of epilepsy and are also associated with chronic pain. This makes Nav potential targets for CBD interaction since it has been reported to reduce pain and seizures. One potential method of interaction that is of interest in this review is whether CBD affects channel function by altering lipid bilayer properties, independent of any possible direct interaction with membrane channels. CBD’s ability to interact with its targets is a novel and important discovery. This discovery will not only prompt further research towards CBD’s characterization, but also promotes the application of cannabinoids as potentially therapeutic compounds for diseases like epilepsy and pain.

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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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)0.0040.002

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 head0.065
GPT teacher head0.363
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

Citations87
Published2019
Admission routes1
Has abstractyes

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