MétaCan
Menu
Back to cohort
Record W3110751566 · doi:10.1111/1440-1681.13443

Chrysophanol increases osteoblast differentiation via AMPK/Smad1/5/9 phosphorylation in vitro and in vivo

2020· article· en· W3110751566 on OpenAlexaff
Young‐Ju Lim, Kyeong‐Min Kim, Won‐Gu Jang

Bibliographic record

VenueClinical and Experimental Pharmacology and Physiology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBone Metabolism and Diseases
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsAMPKOsteoblastChemistryAlkaline phosphataseCell biologyAdipogenesisIn vivoPhosphorylationProtein kinase AIn vitroSignal transductionZebrafishBiochemistryBiologyGeneEnzyme

Abstract

fetched live from OpenAlex

Chrysophanol (Chrysophanic acid; CA) is a natural anthraquinone found in Senna tora and rhubarb that has various characteristic features, including the ability to suppress adipogenesis. However, its effects on osteoblast differentiation have not been investigated. Herein, this study aimed to demonstrate the mechanism by which CA induces the osteoblast differentiation. CA increased the expression of osteogenic genes. The staining levels Alkaline phosphatase (ALP) and Alizarin Red S (ARS) were increased by chrysophanol. CA induced osteoblast differentiation through AMP-activated protein kinase (AMPK)/Small mothers against decapentaplegic (Smad1/5/9) activation in MC3T3-E1 cells. In addition, compound C, AMPK inhibitor (Comp. C)-induced cells suppressed osteogenic genes expression and AMPK/Smad1/5/9 activation. Interestingly, AMPK in the CA-induced AMPK/Smad1/5/9 signalling pathway was an upstream regulator of Smad1/5/9. In order to further dissect in bone development, we used a zebrafish model to investigate the effect of CA on bone development. These results suggest that CA stimulated bone development via AMPK/Smad1/5/9. Overall, our results demonstrate that CA promotes osteoblast differentiation via AMPK/Smad1/5/9 expression in vitro and in vivo.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.536

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 science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.309
Teacher spread0.294 · 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

Citations14
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueClinical and Experimental Pharmacology and PhysiologySame topicBone Metabolism and DiseasesFrench-language works237,207