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Curcumaromin C


  • Brand : BIOFRON

  • Catalogue Number : AV-B03040

  • Specification : 96%

  • CAS number : 1810034-40-2

  • Formula : C29H32O4

  • Molecular Weight : 444.56

  • PUBCHEM ID : 125181871

  • Volume : 5mg

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Catalogue Number


Analysis Method






Molecular Weight




Botanical Source

Structure Type



Standards;Natural Pytochemical;API




(1E,6E)-1,7-Bis(4-hydroxyphenyl)-4-[(1R,6R)-6-isopropyl-3-methyl-2-cyclohexen-1-yl]-1,6-heptadiene-3,5-dione/1,6-Heptadiene-3,5-dione, 1,7-bis(4-hydroxyphenyl)-4-[(1R,6R)-3-methyl-6-(1-methylethyl)-2-cyclohexen-1-yl]-, (1E,6E)-





1.2±0.1 g/cm3


Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.

Flash Point

349.9±26.6 °C

Boiling Point

631.8±50.0 °C at 760 mmHg

Melting Point



InChl Key


WGK Germany


HS Code Reference

Personal Projective Equipment

Correct Usage

For Reference Standard and R&D, Not for Human Use Directly.

Meta Tag

provides coniferyl ferulate(CAS#:1810034-40-2) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

No Technical Documents Available For This Product.




The rapidly growing availability of multigene sequence data during the past decade has enabled phylogeny estimation at phylogenomic scales. However, dealing with evolutionary process heterogeneity across the genome becomes increasingly challenging. Here we develop a mixture model approach that uses reversible jump Markov chain Monte Carlo (MCMC) estimation to permit as many distinct models as the data require. Each additional model considered may be a fully parametrized general time-reversible model or any of its special cases. Furthermore, we expand the usual proposal mechanisms for topology changes to permit hard polytomies (i.e., zero-length internal branches). This new approach is implemented in the Crux software toolkit. We demonstrate the feasibility of using reversible jump MCMC on mixture models by reexamining a well-known 44-taxon mammalian data set comprising 22 concatenated genes. We are able to reproduce the results of the original analysis (with respect to bipartition support) when we make identical assumptions, but when we allow for polytomies and/or use data-driven mixture model estimation, we infer much lower bipartition support values for several key bipartitions.


Bayesian phylogenetic inference, mixture models, model selection, polytomous trees, reversible jump Markov chain Monte-Carlo


Generalized Mixture Models for Molecular Phylogenetic Estimation


Jason Evans* and Jack Sullivan

Publish date

2012 Jan;




Muscle atrophy can result from inactivity or unloading on one hand or the induction of a catabolic state on the other. Muscle-specific ring finger 1 (MuRF1), a member of the tripartite motif family of E3 ubiquitin ligases, is an essential mediator of multiple conditions inducing muscle atrophy. While most studies have focused on the role of MuRF1 in protein degradation, the protein may have other roles in regulating skeletal muscle mass and metabolism. We therefore systematically evaluated the effect of MuRF1 on gene expression during denervation and dexamethasone-induced atrophy. We find that the lack of MuRF1 leads to few differences in control animals, but there were several significant differences in specific sets of genes upon denervation- and dexamethasone-induced atrophy. For example, during denervation, MuRF1 knockout mice showed delayed repression of metabolic and structural genes and blunted induction of genes associated with the neuromuscular junction. In the latter case, this pattern correlates with blunted HDAC4 and myogenin upregulation. Lack of MuRF1 caused fewer changes in the dexamethasone-induced atrophy program, but certain genes involved in fat metabolism and intracellular signaling were affected. Our results demonstrate a new role for MuRF1 in influencing gene expression in two important models of muscle atrophy.


denervation, glucocorticoid, microarray, skeletal muscle, gene expression


Altered gene expression patterns in muscle ring finger 1 null mice during denervation- and dexamethasone-induced muscle atrophy


J. David Furlow,corresponding author1 Monica L. Watson,1 David S. Waddell,1 Eric S. Neff,1 Leslie M. Baehr,1 Adam P. Ross,1,2 and Sue C. Bodine1,2

Publish date

2013 Dec 1;




The efficacy of erlotinib treatment for advanced non-small cell lung cancer (NSCLC) is due to its action as an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI). Patients treated with erlotinib experience different drug responses. The effect of germline mutations on therapeutic responses and adverse drug responses (ADRs) to erlotinib in Chinese patients requires elucidation. Sixty Han Chinese advanced non-small cell lung cancer patients received erlotinib monotherapy and, for each participant, 76 candidate genes (related to EGFR signaling, drug metabolism and drug transport pathways) were sequenced and analyzed. The single-nucleotide polymorphisms (SNPs) rs1042640 in UGT1A10, rs1060463, and rs1064796 in CYP4F11, and rs2074900 in CYP4F2 were significantly associated with therapeutic responses to erlotinib. Rs1064796 in CYP4F11 and rs10045685 in UGT3A1 were significantly associated with adverse drug reaction. Moreover, analysis of a validation cohort confirmed the significant association between rs10045685 in UGT3A1 and erlotinib adverse drug response(unadjusted p = 0.015). This study provides comprehensive, systematic analyses of genetic variants associated with responses to erlotinib in Chinese advanced non-small cell lung cancer patients. Newly-identified SNPs may serve as promising markers to predict responses and safety in erlotinib-treated advanced non-small cell lung cancer patients after chemotherapy doublet.


non-small cell lung cancer, erlotinib, single-nucleotide polymorphism (SNP), therapeutic responses, adverse drug responses


Genetic Association of Drug Response to Erlotinib in Chinese Advanced Non-small Cell Lung Cancer Patients


Cong Wang,1,2,† Fang Chen,1,2,† Yichen Liu,2,† Qingqing Xu,2 Liang Guo,3 Xiaoqing Zhang,4 Yunfeng Ruan,2 Ye Shi,2 Lu Shen,2 Mo Li,2 Huihui Du,2 Xiaofang Sun,5 Jingsong Ma,2 Lin He,2,* and Shengying Qin2,5,6,*

Publish date