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Record W3106178930 · doi:10.1038/s41467-020-19510-5

Organized cannabinoid receptor distribution in neurons revealed by super-resolution fluorescence imaging

2020· article· en· W3106178930 on OpenAlexaff
Hui Li, Jie Yang, Cuiping Tian, Min Diao, Quan Wang, Simeng Zhao, Shanshan Li, Fangzhi Tan, Tian Hua, Ya Qin, Chao‐Po Lin, Dylan Deska‐Gauthier, Garth J. Thompson, Ying Zhang, Wenqing Shui, Zhi‐Jie Liu, Tong Wang, Guisheng Zhong

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsDalhousie University
FundersNational Key Research and Development Program of ChinaAustralian Research CouncilPeking UniversityNational Natural Science Foundation of China
KeywordsG protein-coupled receptorReceptorCannabinoid receptorCannabinoidAgonistCell biologyIntracellularCannabinoid Receptor AgonistsG proteinBiologyNeuroscienceChemistrySignal transductionBiophysicsBiochemistry

Abstract

fetched live from OpenAlex

Abstract G-protein-coupled receptors (GPCRs) play important roles in cellular functions. However, their intracellular organization is largely unknown. Through investigation of the cannabinoid receptor 1 (CB 1 ), we discovered periodically repeating clusters of CB 1 hotspots within the axons of neurons. We observed these CB 1 hotspots interact with the membrane-associated periodic skeleton (MPS) forming a complex crucial in the regulation of CB 1 signaling. Furthermore, we found that CB 1 hotspot periodicity increased upon CB 1 agonist application, and these activated CB 1 displayed less dynamic movement compared to non-activated CB 1 . Our results suggest that CB 1 forms periodic hotspots organized by the MPS as a mechanism to increase signaling efficacy upon activation.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations33
Published2020
Admission routes1
Has abstractyes

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