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3-epi-Isocucurbitacin B

$1,152

  • Brand : BIOFRON

  • Catalogue Number : BN-O0982

  • Specification : 95%(HPLC)

  • CAS number : 89647-62-1

  • Formula : C32H46O8

  • Molecular Weight : 558.7

  • PUBCHEM ID : 434841

  • Volume : 5mg

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Catalogue Number

BN-O0982

Analysis Method

HPLC,NMR,MS

Specification

95%(HPLC)

Storage

2-8°C

Molecular Weight

558.7

Appearance

Oil

Botanical Source

Structure Type

Triterpenoids

Category

Standards;Natural Pytochemical;API

SMILES

CC(=O)OC(C)(C)C=CC(=O)C(C)(C1C(CC2(C1(CC(=O)C3(C2CC=C4C3CC(=O)C(C4(C)C)O)C)C)C)O)O

Synonyms

(23E)-1,16,20-Trihydroxy-9,10,14-trimethyl-2,11,22-trioxo-4,9-cyclo-9,10-secocholesta-5,23-dien-25-yl acetate/Estr-5-ene-2,11-dione, 17-[(3E)-5-(acetyloxy)-1-hydroxy-1,5-dimethyl-2-oxo-3-hexen-1-yl]-3,16-dihydroxy-4,4,9,14-tetramethyl-

IUPAC Name

[6-(3,16-dihydroxy-4,4,9,13,14-pentamethyl-2,11-dioxo-3,7,8,10,12,15,16,17-octahydro-1H-cyclopenta[a]phenanthren-17-yl)-6-hydroxy-2-methyl-5-oxohept-3-en-2-yl] acetate

Density

1.2±0.1 g/cm3

Solubility

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

Flash Point

219.9±26.4 °C

Boiling Point

702.4±60.0 °C at 760 mmHg

Melting Point

InChl

InChI=1S/C32H46O8/c1-17(33)40-27(2,3)13-12-23(36)32(9,39)25-21(35)15-29(6)22-11-10-18-19(14-20(34)26(38)28(18,4)5)31(22,8)24(37)16-30(25,29)7/h10,12-13,19,21-22,25-26,35,38-39H,11,14-16H2,1-9H3/b13-12+/t19?,21-,22?,25?,26-,29?,30?,31?,32?/m1/s1

InChl Key

WTBZNVRBNJWSPF-SLKCYTCGSA-N

WGK Germany

RID/ADR

HS Code Reference

2933990000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:89647-62-1) 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

29489839

Abstract

Background
Since the site of human subjects research has public health, regulatory, ethical, economic, and social implications, we sought to determine the global distribution and migration of clinical research using an open-access trial registry.

Methods
We obtained individual clinical trial data including location of trial sites, dates of operation, funding source (United States government, pharmaceutical industry, or organization), and clinical study phase (1, 1/2, 2, 2/3, or 3) from ClinicalTrials.gov. We used the World Bank’s classification of each country’s economic development status [“High Income and a Member of the Organization for Economic Co-operation and Development (OECD)”, “High Income and Non-Member of the OECD”, “Upper-Middle Income”, “Lower-Middle Income”, or “Low Income”] and United Nations Populations Division data for country-specific population estimates. We analyzed data from calendar year 2006 through 2012 by number of clinical trial sites, cumulative trial site-years, trial density (trial site-years/106 population), and annual growth rate (%) for each country, and by development category, funding source, and clinical study phase.

Results
Over a 7-year period, 89,647 clinical trials operated 784,585 trial sites in 175 countries, contributing 2,443,850 trial site-years. Among those, 652,200 trial sites (83%) were in 25 high-income OECD countries, while 37,195 sites (5%) were in 91 lower-middle or low-income countries. Trial density (trial site-years/106 population) was 540 in the United States, 202 among other high-income OECD countries (excluding the United States), 81 among high-income non-OECD countries, 41 among upper-middle income countries, 5 among lower-middle income countries, and 2 among low-income countries. Annual compound growth rate was positive (ranging from 0.8% among low-income countries to 14.7% among lower-middle income countries) among all economic groups, except the United States (-0.5%). Overall, 29,191 trials (33%) were funded by industry, 4,059 (5%) were funded by the United States government, and 56,397 (63%) were funded by organizations. Countries with emerging economies (low- and middle-income) operated 19% of phase 3 trial sites, as compared to only 6% of phase 1 trial sites.

Conclusion
Human clinical research remains concentrated in high-income countries, but operational clinical trial sites, particularly for phase 3 trials, may be migrating to low- and middle-income countries with emerging economies.

Title

Global migration of clinical research during the era of trial registration

Author

Paul K. Drain, Conceptualization, Data curation, Funding acquisition, Investigation, Methodology, Writing - original draft,1,2,3,* Robert A. Parker, Data curation, Formal analysis, Investigation, Writing - review & editing,4,5,6 Marion Robine, Data curation, Investigation, Project administration, Writing - review & editing,4 and King K. Holmes, Conceptualization, Investigation, Writing - review & editing1,2 Srinivas Murthy, Editor

Publish date

2018;

PMID

21754126

Abstract

The asymmetric unit of the title compound, C21H15NO3S, contains two crystallographically independent mol­ecules. As a result of the electron-withdrawing character of the phenyl­sulfonyl groups, the N—Csp 2 bond lengths are slightly longer than the anti­cipated value of approximately 1.35 a for N atoms with planar configurations. Both unique S atoms have a distorted tetra­hedral configuration. In each mol­ecule, the indole ring system is essentially planar (r.m.s. deviations for all non-H atoms of 0.020 and 0.023 a). In one mol­ecule, the indole ring system makes dihedral angles of 65.7 (8) and 73.4 (8)°, respectively, with the benzene and phenyl rings [62.2 (7) and 72.1 (7)°, respectively, in the other mol­ecule].

Title

Phen­yl(1-phenyl­sulfonyl-1H-indol-2-yl)methanone

Author

S. Ranjith,a A. SubbiahPandi,a,* E. Govindan,a V. Dhayalan,b and A. K. MohanaKrishnanb

Publish date

2011 Apr 1;

PMID

30283492

Abstract

Detection of coding/functional SNPs that change the biological function of a gene may lead to identification of putative causative alleles within QTL regions and discovery of genetic markers with large effects on phenotypes. This study has two-fold objectives, first to develop, and validate a 50K transcribed gene SNP-chip using RNA-Seq data. To achieve this objective, two bioinformatics pipelines, GATK and SAMtools, were used to identify ~21K transcribed SNPs with allelic imbalances associated with important aquaculture production traits including body weight, muscle yield, muscle fat content, shear force, and whiteness in addition to resistance/susceptibility to bacterial cold-water disease (BCWD). SNPs ere identified from pooled RNA-Seq data collected from ~620 fish, representing 98 families from growth- and 54 families from BCWD-selected lines with divergent phenotypes. In addition, ~29K transcribed SNPs without allelic-imbalances were strategically added to build a 50K Affymetrix SNP-chip. SNPs selected included two SNPs per gene from 14K genes and ~5K non-synonymous SNPs. The SNP-chip was used to genotype 1728 fish. The average SNP calling-rate for samples passing quality control (QC; 1,641 fish) was ≥ 98.5%. The second objective of this study was to test the feasibility of using the new SNP-chip in GWA (Genome-wide association) analysis to identify QTL explaining muscle yield variance. GWA study on 878 fish (representing 197 families from 2 consecutive generations) with muscle yield phenotypes and genotyped for 35K polymorphic markers (passing QC) identified several QTL regions explaining together up to 28.40% of the additive genetic variance for muscle yield in this rainbow trout population. The most significant QTLs were on chromosomes 14 and 16 with 12.71 and 10.49% of the genetic variance, respectively. Many of the annotated genes in the QTL regions were previously reported as important regulators of muscle development and cell signaling. No major QTLs were identified in a previous GWA study using a 57K genomic SNP chip on the same fish population. These results indicate improved detection power of the transcribed gene SNP-chip in the target trait and population, allowing identification of large-effect QTLs for important traits in rainbow trout.

KEYWORDS

GWAS, SNP-chip, muscle, trout, fillet yield

Title

Genome-Wide Association Analysis With a 50K Transcribed Gene SNP-Chip Identifies QTL Affecting Muscle Yield in Rainbow Trout

Author

Mohamed Salem,1,2,* Rafet Al-Tobasei,2,3 Ali Ali,1 Daniela Lourenco,4 Guangtu Gao,5 Yniv Palti,5 Brett Kenney,6 and Timothy D. Leeds5

Publish date

2018


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