Shipping to United States We Offer Worldwide Shipping
Login Wishlist

Kuwanon A

$504

  • Brand : BIOFRON

  • Catalogue Number : BD-P0157

  • Specification : 95.0%(HPLC)

  • CAS number : 62949-77-3

  • Formula : C25H24O6

  • Molecular Weight : 420.45

  • PUBCHEM ID : 44258296

  • Volume : 10mg

Available on backorder

Quantity
Checkout Bulk Order?

Catalogue Number

BD-P0157

Analysis Method

HPLC,NMR,MS

Specification

95.0%(HPLC)

Storage

2-8°C

Molecular Weight

420.45

Appearance

Yellow powder

Botanical Source

Structure Type

Flavonoids

Category

SMILES

CC(=CCC1=C(OC2=CC(=CC(=C2C1=O)O)O)C3=C4C(=C(C=C3)O)C=CC(O4)(C)C)C

Synonyms

5,7-dihydroxy-2-(5-hydroxy-2,2-dimethylchromen-8-yl)-3-(3-methylbut-2-enyl)chromen-4-one

IUPAC Name

5,7-dihydroxy-2-(5-hydroxy-2,2-dimethylchromen-8-yl)-3-(3-methylbut-2-enyl)chromen-4-one

Applications

Density

Solubility

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

Flash Point

Boiling Point

Melting Point

InChl

InChI=1S/C25H24O6/c1-13(2)5-6-16-22(29)21-19(28)11-14(26)12-20(21)30-23(16)17-7-8-18(27)15-9-10-25(3,4)31-24(15)17/h5,7-12,26-28H,6H2,1-4H3

InChl Key

DBUNRZUFILGKHP-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#:62949-77-3) 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.

PMID

21202666

Abstract

The title compound, C20H15BrO3S, was prepared by the oxidation of 7-bromo-2-methyl-1-(4-tolyl­sulfan­yl)naph­tho[2,1-b]furan with 3-chloro­peroxy­benzoic acid. The 4-tolyl ring makes a dihedral angle of 70.96 (6)° with the plane of the naphthofuran fragment. The crystal structure is stabilized by aromatic π-π stacking inter­actions, with centroid-centroid distances of 3.672 (3) and 3.858 (3) a between the central benzene and furan rings, and between the brominated benzene and central benzene rings of the naphthofuran system of neighbouring mol­ecules, respectively. In addition, the stacked mol­ecules exhibit C—H⋯π and inter- and intra­molecular C—H⋯O inter­actions.

Title

7-Bromo-2-methyl-1-tosyl­naphtho[2,1-b]furan

Author

Hong Dae Choi,a Pil Ja Seo,a Byeng Wha Son,b and Uk Leeb,*

Publish date

2008 Jun 1;

PMID

21193244

Abstract

One in five older adults in Taiwan have been diagnosed with diabetes. This study drew on disability data for 5,121 nationally representative middle-aged and older adults from the 1996-2003 Survey of Health and Living Status of the Elderly in Taiwan (SHLSET). By employing cohort sequential design and multilevel models, it combined cross-sectional and longitudinal evidence to characterize the age trajectory of physical disability from midlife to older adulthood and to discern the extent to which diabetes contributes to the variation in that trajectory. The main effects of diabetes and diabetes × age interaction in the fully controlled model provide evidence that diabetes independently and consistently changes physical functioning over and above natural aging processes in Taiwanese adults. In addition, while adding diabetes in the age trajectory of physical disability explained 3.2% and 1.6% of the variance in levels of and linear changes in physical disability trajectory, respectively, further adding follow-up status, sociodemographic factors and comorbidities altogether explained 40.5% and 29.1% of the variance in levels of and linear changes in that trajectory. These results imply that preventing the incidence of diabetes-related comorbidities may reduce the deterioration in both levels of and rates of change in physical disability.

KEYWORDS

Cohort-sequential design (accelerated longitudinal design), Multilevel modeling, Taiwan elders, Diabetes

Title

Diabetes-Related Change in Physical Disability from Midlife to Older Adulthood: Evidence from 1996-2003 Survey of Health and Living Status of the Elderly in Taiwan

Author

Ching-Ju Chiu, PhD,1 Linda A. Wray, PhD,1 and Mary Beth Ofstedal, PhD2

Publish date

2012 Mar 1.

PMID

27239230

Abstract

Background
Bioassay data analysis continues to be an essential, routine, yet challenging task in modern drug discovery and chemical biology research. The challenge is to infer reliable knowledge from big and noisy data. Some aspects of this problem are general with solutions informed by existing and emerging data science best practices. Some aspects are domain specific, and rely on expertise in bioassay methodology and chemical biology. Testing compounds for biological activity requires complex and innovative methodology, producing results varying widely in accuracy, precision, and information content. Hit selection criteria involve optimizing such that the overall probability of success in a project is maximized, and resource-wasteful “false trails” are avoided. This “fail-early” approach is embraced both in pharmaceutical and academic drug discovery, since follow-up capacity is resource-limited. Thus, early identification of likely promiscuous compounds has practical value.

Results
Here we describe an algorithm for identifying likely promiscuous compounds via associated scaffolds which combines general and domain-specific features to assist and accelerate drug discovery informatics, called Badapple: bioassay-data associative promiscuity pattern learning engine. Results are described from an analysis using data from MLP assays via the BioAssay Research Database (BARD) http://bard.nih.gov. Specific examples are analyzed in the context of medicinal chemistry, to illustrate associations with mechanisms of promiscuity. Badapple has been developed at UNM, released and deployed for public use two ways: (1) BARD plugin, integrated into the public BARD REST API and BARD web client; and (2) public web app hosted at UNM.

Conclusions
Badapple is a method for rapidly identifying likely promiscuous compounds via associated scaffolds. Badapple generates a score associated with a pragmatic, empirical definition of promiscuity, with the overall goal to identify “false trails” and streamline workflows. Unlike methods reliant on expert curation of chemical substructure patterns, Badapple is fully evidence-driven, automated, self-improving via integration of additional data, and focused on scaffolds. Badapple is robust with respect to noise and errors, and skeptical of scanty evidence.

Electronic supplementary material
The online version of this article (doi:10.1186/s13321-016-0137-3) contains supplementary material, which is available to authorized users.

KEYWORDS

Drug discovery informatics, High-throughput screening (HTS), Compound promiscuity, Molecular scaffolds, Statistical learning

Title

Badapple: promiscuity patterns from noisy evidence

Author

Jeremy J. Yang, Oleg Ursu, Christopher A. Lipinski, Larry A. Sklar, Tudor I. Oprea, and Cristian G. Bologacorresponding author

Publish date

2016;