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Record W2266819956 · doi:10.1139/cjpp-2015-0394

Nerve growth factor enhances the CRE-dependent transcriptional activity activated by nobiletin in PC12 cells

2016· article· en· W2266819956 on OpenAlexvenueno aff
Jiro Takito, Junko Kimura, Koji Kajima, Nobuyuki Uozumi, Makoto Watanabe, Akihito Yokosuka, Yoshihiro Mimaki, Masanori Nakamura, Yasushi Ohizumi

Bibliographic record

VenueCanadian Journal of Physiology and Pharmacology · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioactive Compounds in Plants
Canadian institutionsnot available
Fundersnot available
KeywordsNobiletinNerve growth factorTropomyosin receptor kinase ACREBMAPK/ERK pathwayTranscription factorTrk receptorCell biologyChemistrySignal transductionInternal medicineBiologyEndocrinologyReceptorMedicineBiochemistryFlavonoid

Abstract

fetched live from OpenAlex

Prevention and treatment of Alzheimer disease are urgent problems for elderly people in developed countries. We previously reported that nobiletin, a poly-methoxylated flavone from the citrus peel, improved the symptoms in various types of animal models of memory loss and activated the cAMP responsive element (CRE)-dependent transcription in PC12 cells. Nobiletin activated the cAMP/PKA/MEK/Erk/MAPK signaling pathway without using the TrkA signaling activated by nerve growth factor (NGF). Here, we examined the effect of combination of nobiletin and NGF on the CRE-dependent transcription in PC12 cells. Although NGF alone had little effect on the CRE-dependent transcription, NGF markedly enhanced the CRE-dependent transcription induced by nobiletin. The NGF-induced enhancement was neutralized by a TrkA antagonist, K252a. This effect of NGF was effective on the early signaling event elicited by nobiletin. These results suggested that there was crosstalk between NGF and nobiletin signaling in activating the CRE-dependent transcription in PC12 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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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

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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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
Published2016
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Physiology and PharmacologySame topicBioactive Compounds in PlantsFrench-language works237,207