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Record W2797278894 · doi:10.1139/cjpp-2018-0021

Imidazolines increase the levels of the autophagosomal marker LC3-II in macrophage-like RAW264.7 cells

2018· article· en· W2797278894 on OpenAlexvenueno aff
Shiori Nakagawa, Takayuki Ueno, Takayuki Manabe, Kiyoshi Kawasaki

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2018
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsIdazoxanBafilomycinAutophagyAutophagosomeImidazoline receptorChemistryLysosomeCell biologyPharmacologyBiologyBiochemistryReceptorAntagonistEnzyme

Abstract

fetched live from OpenAlex

This study evaluated whether imidazolines can induce autophagy in the murine macrophage-like cell line RAW264.7. Idazoxan increased the content of LC3-II, an autophagosomal marker, in RAW264.7 cells. To determine whether this effect was due to the induction of its synthesis or inhibition of its degradation, idazoxan treatment was performed in the presence of bafilomycin A1, which blocks autophagosome-lysosome fusion, as well as Pepstatin A and E-64d, both of which block protein degradation in autolysosomes. An increased content of LC3-II was observed in the presence of bafilomycin A1 as well as the protease inhibitors. Furthermore, an increased number of autophagosomes was observed following idazoxan treatment using an autophagosome-specific dye. This indicated that idazoxan induced autophagy. Other imidazolines, such as efaroxan, clonidine, and 2-(2-benzofuranyl)-2-imidazoline, also increased the LC3-II content in RAW264.7 cells in the presence of bafilomycin A1. Taken together, these results indicate that some imidazolines, including idazoxan, can induce autophagy in RAW264.7 cells.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.278
Teacher spread0.265 · 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 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

Citations9
Published2018
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicAutophagy in Disease and TherapyFrench-language works237,207