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5-Methyl-7-methoxyisoflavone

$78

  • Brand : BIOFRON

  • Catalogue Number : BF-M2001

  • Specification : 98%

  • CAS number : 82517-12-2

  • Formula : C17H14O3

  • Molecular Weight : 266.29

  • PUBCHEM ID : 2734290

  • Volume : 20mg

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Quantity
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Catalogue Number

BF-M2001

Analysis Method

HPLC,NMR,MS

Specification

98%

Storage

2-8°C

Molecular Weight

266.29

Appearance

White crystalline powder

Botanical Source

Geranium

Structure Type

Flavonoids

Category

Standards;Natural Pytochemical;API

SMILES

CC1=CC(=CC2=C1C(=O)C(=CO2)C3=CC=CC=C3)OC

Synonyms

7-methoxy-5-methyl-3-phenylchromen-4-one/5-Methyl-7-methoxyisoflavone/7-Methoxy-5-methyl-3-phenyl-4H-chromen-4-one/4H-1-Benzopyran-4-one, 7-methoxy-5-methyl-3-phenyl-

IUPAC Name

7-methoxy-5-methyl-3-phenylchromen-4-one

Density

1.2±0.1 g/cm3

Solubility

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

Flash Point

217.5±15.1 °C

Boiling Point

449.3±45.0 °C at 760 mmHg

Melting Point

116-120 °C

InChl

InChI=1S/C17H14O3/c1-11-8-13(19-2)9-15-16(11)17(18)14(10-20-15)12-6-4-3-5-7-12/h3-10H,1-2H3

InChl Key

WGOUYULOZZRTFS-UHFFFAOYSA-N

WGK Germany

RID/ADR

HS Code Reference

2932990000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:82517-12-2) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

PMID

29609200

Abstract

Objective
To determine whether systemic juvenile idiopathic arthritis (sJIA) susceptibility loci identified by candidate gene studies demonstrated association with sJIA in the largest study population assembled to date.

Methods
Single nucleotide polymorphisms (SNPs) from 11 previously reported sJIA risk loci were examined for association in 9 populations, including 770 sJIA cases and 6947 control subjects. The effect of sJIA-associated SNPs on gene expression was evaluated in silico in paired whole genome and RNA sequencing data from lymphoblastoid cell lines (LCL) of 373 European 1000 Genomes Project subjects. The relationship between sJIA-associated SNPs and response to anakinra treatment was evaluated in 38 US patients for whom treatment response data were available.

Results
We found no association of the 26 SNPs previously reported as sJIA-associated. Expanded analysis of the regions containing the 26 SNPs revealed only one significant association, the promoter region of IL1RN (p<1E-4). sJIA-associated SNPs correlated with IL1RN expression in LCLs, with an inverse correlation between sJIA risk and IL1RN expression. The presence of homozygous IL1RN high expression alleles correlated strongly with non-response to anakinra therapy (OR 28.7 [3.2, 255.8]).

Conclusion
IL1RN was the only candidate locus associated with sJIA in our study. The implicated SNPs are among the strongest known determinants of IL1RN and IL1RA levels, linking low expression with increased sJIA risk. Homozygous high expression alleles predicted non-response to anakinra therapy, nominating them as candidate biomarkers to guide sJIA treatment. This is an important first step towards the personalized treatment of sJIA.

KEYWORDS

systemic juvenile idiopathic arthritis, genetics, IL1RN, anakinra, biomarker, personalized medicine

Title

IL1RN Variation Influences both Disease Susceptibility and Response to Human Recombinant IL-1RA Therapy in Systemic Juvenile Idiopathic Arthritis

Author

Victoria L. Arthur, BA,1,* Emily Shuldiner, BA,1,* Elaine F. Remmers, PhD,2 Anne Hinks, PhD,3 Alexei A. Grom, MD,4,5 Dirk Foell, MD,6 Alberto Martini, MD,7,8 Marco Gattorno, MD,8 Seza ozen, MD,9 Sampath Prahalad, MD,10,11 Andrew S. Zeft, MD,12 John F. Bohnsack, MD,13 Norman T. Ilowite, MD,14 Elizabeth D. Mellins, MD PhD,15 Ricardo Russo, MD,16 Claudio Len, MD,17 Sheila Oliveira, MD PhD,18 Rae S. M. Yeung, MD PhD,19,20,21 Alan M. Rosenberg, MD,22 Lucy R. Wedderburn, MD PhD,23,24,25 Jordi Anton, MD PhD,26 Johannes-Peter Haas, MD,27 Angela Rosen-Wolff, MD PhD,28 Kirsten Minden, MD PhD,29,30 Ann Marie Szymanski, MD,1 INCHARGE Consortium,31 Wendy Thomson, PhD,3,32 Daniel L. Kastner, MD PhD,2 Patricia Woo, MBBS PhD,23 and Michael J. Ombrello, MD1

Publish date

2018 Jun 28

PMID

30807586

Abstract

Background
Joint inflammation is the common feature underlying juvenile idiopathic arthritis (JIA). Clinicians recognize patterns of joint involvement currently not part of the International League of Associations for Rheumatology (ILAR) classification. Using unsupervised machine learning, we sought to uncover data-driven joint patterns that predict clinical phenotype and disease trajectories.

Methods and findings
We analyzed prospectively collected clinical data, including joint involvement using a standard 71-joint homunculus, for 640 discovery patients with newly diagnosed JIA enrolled in a Canada-wide study who were followed serially for five years, treatment-naïve except for nonsteroidal anti-inflammatory drugs (NSAIDs) and diagnosed within one year of symptom onset. Twenty-one patients had systemic arthritis, 300 oligoarthritis, 125 rheumatoid factor (RF)-negative polyarthritis, 16 RF-positive polyarthritis, 37 psoriatic arthritis, 78 enthesitis-related arthritis (ERA), and 63 undifferentiated arthritis. At diagnosis, we observed global hierarchical groups of co-involved joints.

To characterize these patterns, we developed sparse multilayer non-negative matrix factorization (NMF). Model selection by internal bi-cross-validation identified seven joint patterns at presentation, to which all 640 discovery patients were assigned: pelvic girdle (57 patients), fingers (25), wrists (114), toes (48), ankles (106), knees (283), and indistinct (7). Patterns were distinct from clinical subtypes (P < 0.001 by χ2 test) and reproducible through external data set validation on a 119-patient, prospectively collected independent validation cohort (reconstruction accuracy Q2 = 0.55 for patterns; 0.35 for groups).

Some patients matched multiple patterns. To determine whether their disease outcomes differed, we further subdivided the 640 discovery patients into three subgroups by degree of localization—the percentage of their active joints aligning with their assigned pattern: localized (≥90%; 359 patients), partially localized (60%-90%; 124), or extended (<60%; 157). Localized patients more often maintained their baseline patterns (P < 0.05 for five groups by permutation test) than nonlocalized patients (P < 0.05 for three groups by permutation test) over a five-year follow-up period.

We modelled time to zero joints in the discovery cohort using a multivariate Cox proportional hazards model considering joint pattern, degree of localization, and ILAR subtype. Despite receiving more intense treatment, 50% of nonlocalized patients had zero joints at one year compared to six months for localized patients. Overall, localized patients required less time to reach zero joints (partial: P = 0.0018 versus localized by log-rank test; extended: P = 0.0057).

Potential limitations include the requirement for patients to be treatment naïve (except NSAIDs), which may skew the patient cohorts towards milder disease, and the validation cohort size precluded multivariate analyses of disease trajectories.

Conclusions
Multilayer NMF identified patterns of joint involvement that predicted disease trajectory in children with arthritis. Our hierarchical unsupervised approach identified a new clinical feature, degree of localization, which predicted outcomes in both cohorts. Detailed assessment of every joint is already part of every musculoskeletal exam for children with arthritis. Our study supports both the continued collection of detailed joint involvement and the inclusion of patterns and degrees of localization to stratify patients and inform treatment decisions. This will advance pediatric rheumatology from counting joints to realizing the potential of using data available from uncovering patterns of joint involvement.

Title

Patterns of joint involvement in juvenile idiopathic arthritis and prediction of disease course: A prospective study with multilayer non-negative matrix factorization

Author

Simon W. M. Eng, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing,1,2,3,‡ Florence A. Aeschlimann, Investigation, Writing - original draft, Writing - review & editing,1,‡ Mira van Veenendaal, Conceptualization, Data curation, Investigation, Writing - original draft, Writing - review & editing,1,‡ Roberta A. Berard, Data curation, Resources, Writing - review & editing,4,5,6 Alan M. Rosenberg, Data curation, Resources, Writing - review & editing,7 Quaid Morris, Conceptualization, Formal analysis, Methodology, Resources, Supervision, Writing - original draft, Writing - review & editing,3,7,8,9,10,11,* Rae S. M. Yeung, Conceptualization, Data curation, Funding acquisition, Project administration, Resources, Supervision, Writing - original draft, Writing - review & editing,1,2,4,* and on behalf of the ReACCh-Out Research Consortium¶

Publish date

2019 Feb 26

PMID

27244050

Abstract

Gene expression-based signatures help identify pathways relevant to diseases and treatments, but are challenging to construct when there is a diversity of disease mechanisms and treatments in patients with complex diseases. To overcome this challenge, we present a new application of an in silico gene expression deconvolution method, ISOpure-S1, and apply it to identify a common gene expression signature corresponding to response to treatment in 33 juvenile idiopathic arthritis (JIA) patients. Using pre- and post-treatment gene expression profiles only, we found a gene expression signature that significantly correlated with a reduction in the number of joints with active arthritis, a measure of clinical outcome (Spearman rho = 0.44, p = 0.040, Bonferroni correction). This signature may be associated with a decrease in T-cells, monocytes, neutrophils and platelets. The products of most differentially expressed genes include known biomarkers for JIA such as major histocompatibility complexes and interleukins, as well as novel biomarkers including α-defensins. This method is readily applicable to expression datasets of other complex diseases to uncover shared mechanistic patterns in heterogeneous samples.

Title

Gene Expression Deconvolution for Uncovering Molecular Signatures in Response to Therapy in Juvenile Idiopathic Arthritis

Author

Ang Cui,#1,¤a Gerald Quon,#2,¤b Alan M. Rosenberg,3 Rae S. M. Yeung,4,5,* Quaid Morris,2,6,7,* and BBOP Study Consortium¶

Publish date

2016 May 31


Description :

5-Methyl-7-methoxyisoflavone is a sensational, non-steroidal anabolic isoflavone. 5-Methyl-7-methoxyisoflavone shows potency increasing muscle mass and endurance[1].