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cis-Tiliroside

$672

  • Brand : BIOFRON

  • Catalogue Number : BD-P0328

  • Specification : 98.0%(HPLC)

  • CAS number : 163956-16-9

  • Formula : C30H26O13

  • Molecular Weight : 594.52

  • PUBCHEM ID : 10175330

  • Volume : 10mg

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Catalogue Number

BD-P0328

Analysis Method

HPLC,NMR,MS

Specification

98.0%(HPLC)

Storage

2-8°C

Molecular Weight

594.52

Appearance

Yellow powder

Botanical Source

Structure Type

Flavonoids

Category

SMILES

C1=CC(=CC=C1C=CC(=O)OCC2C(C(C(C(O2)OC3=C(OC4=CC(=CC(=C4C3=O)O)O)C5=CC=C(C=C5)O)O)O)O)O

Synonyms

[(2R,3S,4S,5R,6S)-6-[5,7-dihydroxy-2-(4-hydroxyphenyl)-4-oxochromen-3-yl]oxy-3,4,5-trihydroxyoxan-2-yl]methyl (Z)-3-(4-hydroxyphenyl)prop-2-enoate

IUPAC Name

[(2R,3S,4S,5R,6S)-6-[5,7-dihydroxy-2-(4-hydroxyphenyl)-4-oxochromen-3-yl]oxy-3,4,5-trihydroxyoxan-2-yl]methyl (Z)-3-(4-hydroxyphenyl)prop-2-enoate

Applications

Density

1.7±0.1 g/cm3

Solubility

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

Flash Point

311.9±27.8 °C

Boiling Point

943.9±65.0 °C at 760 mmHg

Melting Point

InChl

InChI=1S/C30H26O13/c31-16-6-1-14(2-7-16)3-10-22(35)40-13-21-24(36)26(38)27(39)30(42-21)43-29-25(37)23-19(34)11-18(33)12-20(23)41-28(29)15-4-8-17(32)9-5-15/h1-12,21,24,26-27,30-34,36,38-39H,13H2/b10-3-/t21-,24-,26+,27-,30+/m1/s1

InChl Key

DVGGLGXQSFURLP-PYFXTMFGSA-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#:163956-16-9) 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

30234160

Abstract

Performing accurate diagnosis using computed tomography (CT) in emergency medicine may reduce mortality rates in various diseases. In this observational, correlational and cross-sectional study, we conducted multiple regression analyses to investigate the relationship between CT utilization rates and mortality. In addition, we estimated the annual net profits from CT to show the profitability of introducing a CT system in each Japanese prefecture.

We conducted a multiple regression analysis to investigate correlations between CT utilization rates and mortality from each disease adjusted for the population density, number of doctors, as well as transportation time to the medical institution.

The results of multiple regression analysis showed that traffic accident mortality was related to CT utilization rate and population density. Extrinsic death such as mortality due to falling, drowning and asphyxia was related to CT utilization, indicating that CT in emergency medicine reduced mortality. Moreover, the annual net profit from multi-slice CT (MSCT) was estimated as positive.

Our study clearly demonstrates that CT utilization rates relate to a reduction in mortality from accidents, indicating that screening patients with CT in the emergency room has a beneficial effect and reduces mortality. Therefore, CT equipment has a beneficial effect in both emergency medicine and hospital management.

KEYWORDS

Computed tomography, Mortality, Emergency medicine, Traffic accident, Hospital management

Title

Benefits of Computed Tomography in Reducing Mortality in Emergency Medicine

Author

Shinya Imai,*,1,2,3 Manabu Akahane,2 Yuto Konishi,1 and Tomoaki Imamura2

Publish date

2018

PMID

9358174

Abstract

The genome sequences from increasing numbers of organisms allow for rapid and organized examination of gene expression. Yet current computational-based paradigms for gene recognition are limited and likely to miss genes expressing non-coding RNAs or mRNAs with small open reading frames (ORFs). We have utilized two strategies to determine if there are additional transcripts in the yeast Saccharomyces cerevisiae that were not identified in previous analyses of the genome. In one approach, we identified strong consensus polymerase III promoters based on sequence, and determined experimentally if these promoters drive the expression of an RNA polymerase III transcript. This approach led to the identification of a new, non-essential 170 nt non-coding RNA. An alternative strategy analyzed RNA expression from large sequence gaps>2 kb between predicted ORFs. Fifteen unique RNA transcripts ranging in size from 161 to 1200 nt were identified from a total of 59 sequence gaps. Several of these RNAs contain unusually small potential ORFs, while one is clearly non-coding and appears to be a small nucleolar RNA. These results suggest that there are likely to be additional previously unidentified non-coding RNAs in yeast, and that new paradigms for gene recognition will be required to identify all expressed genes from an organism.

Title

Analysis of the yeast genome: identification of new non-coding and small ORF-containing RNAs.

Author

W M Olivas, D Muhlrad, and R Parker

Publish date

1997 Nov 15