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Kaempferol-7-O-α-L-rhamnoside

$1,076

  • Brand : BIOFRON

  • Catalogue Number : BD-P0851

  • Specification : 98.0%(HPLC&TLC)

  • CAS number : 20196-89-8

  • Formula : C21H20O10

  • Molecular Weight : 432.4

  • PUBCHEM ID : 25079965

  • Volume : 25mg

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Quantity
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Catalogue Number

BD-P0851

Analysis Method

HPLC,NMR,MS

Specification

98.0%(HPLC&TLC)

Storage

2-8°C

Molecular Weight

432.4

Appearance

Yellow powder

Botanical Source

Structure Type

Flavonoids

Category

SMILES

CC1C(C(C(C(O1)OC2=CC(=C3C(=C2)OC(=C(C3=O)O)C4=CC=C(C=C4)O)O)O)O)O

Synonyms

3,5-dihydroxy-2-(4-hydroxyphenyl)-7-[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxychromen-4-one

IUPAC Name

3,5-dihydroxy-2-(4-hydroxyphenyl)-7-[(2S,3R,4R,5R,6S)-3,4,5-trihydroxy-6-methyloxan-2-yl]oxychromen-4-one

Applications

Density

1.7±0.1 g/cm3

Solubility

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

Flash Point

267.9±26.4 °C

Boiling Point

753.2±60.0 °C at 760 mmHg

Melting Point

231-234℃

InChl

InChI=1S/C21H20O10/c1-8-15(24)17(26)19(28)21(29-8)30-11-6-12(23)14-13(7-11)31-20(18(27)16(14)25)9-2-4-10(22)5-3-9/h2-8,15,17,19,21-24,26-28H,1H3/t8-,15-,17+,19+,21-/m0/s1

InChl Key

HQNOUCSPWAGQND-GKLNBGJFSA-N

WGK Germany

RID/ADR

HS Code Reference

2942000000

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:20196-89-8) 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

27566436

Abstract

Transmembrane proteins (TMPs) are important drug targets because they are essential for signaling, regulation, and transport. Despite important breakthroughs, experimental structure determination remains challenging for TMPs. Various methods have bridged the gap by predicting transmembrane helices (TMHs), but room for improvement remains. Here, we present TMSEG, a novel method identifying TMPs and accurately predicting their TMHs and their topology. The method combines machine learning with empirical filters. Testing it on a non-redundant dataset of 41 TMPs and 285 soluble proteins, and applying strict performance measures, TMSEG outperformed the state-of-the-art in our hands. TMSEG correctly distinguished helical TMPs from other proteins with a sensitivity of 98±2% and a false positive rate as low as 3±1%. Individual TMHs were predicted with a precision of 87±3% and recall of 84±3%. Furthermore, in 63±6% of helical TMPs the placement of all TMHs and their inside/outside topology was correctly predicted. There are two main features that distinguish TMSEG from other methods. First, the errors in finding all helical TMPs in an organism are significantly reduced. For example, in human this leads to 200 and 1600 fewer misclassifications compared to the 2nd and 3rd best method available, and 4400 fewer mistakes than by a simple hydrophobicity-based method. Second, TMSEG provides an add-on improvement for any existing method to benefit from.

KEYWORDS

membrane protein, protein structure prediction, transmembrane helices, α-helical integral membrane protein, transmembrane protein prediction, transmembrane helix prediction

Title

TMSEG: novel prediction of transmembrane helices

Author

Michael Bernhofer,1,* Edda Kloppmann,1,2 Jonas Reeb,1 and Burkhard Rost1,2,3,4

Publish date

2017 Nov 1.

PMID

32649039

Title

Abstract Supplement Oral Abstracts from the 23rd International AIDS Conference, 6‐10 July 2020

Publish date

2020 Jul;

PMID

29922093

Abstract

Background
Before embarking on administrative research, validated case ascertainment algorithms must be developed. We aimed at developing algorithms for identifying inflammatory bowel disease (IBD) patients, date of disease onset, and IBD type (Crohn’s disease [CD] vs ulcerative colitis [UC]) in the databases of the four Israeli Health Maintenance Organizations (HMOs) covering 98% of the population.

Methods
Algorithms were developed on 5,131 IBD patients and 2,072 controls, following independent chart review (60% CD and 39% UC). We reviewed 942 different combinations of clinical parameters aided by mathematical modeling. The algorithms were validated on an independent cohort of 160,000 random subjects.

Results
The combination of the following variables achieved the highest diagnostic accuracy: IBD-related codes, alone if more than five to six codes or combined with purchases of IBD-related medications (at least three purchases or ≥3 months from the first to last purchase) (sensitivity 89%, specificity 99%, positive predictive value [PPV] 92%, negative predictive value [NPV] 99%). A look-back period of 2-5 years (depending on the HMO) without IBD-related codes or medications best determined the date of diagnosis (sensitivity 83%, specificity 68%, PPV 82%, NPV 70%). IBD type was determined by the majority of CD/UC codes of the three recent contacts or the most recent when less than three contacts were recorded (sensitivity 92%, specificity 97%, PPV 97%, NPV 92%). Applying these algorithms, a total of 38,291 IBD patients were residing in Israel, corresponding to a prevalence rate of 459/100,000 (0.46%).

Conclusion
The application of the validated algorithms to Israel’s administrative databases will now create a large and accurate ongoing population-based cohort of IBD patients for future administrative studies.

KEYWORDS

inflammatory bowel diseases, Crohn’s disease, ulcerative colitis, search algorithms, validation, case ascertainment, Israel, administrative database research

Title

Development and validation of novel algorithms to identify patients with inflammatory bowel diseases in Israel: an epi-IIRN group study

Author

Mira Y Friedman,1,2 Maya Leventer-Roberts,3 Joseph Rosenblum,4 Nir Zigman,4 Iris Goren,4 Vered Mourad,4 Natan Lederman,5 Nurit Cohen,5 Eran Matz,6 Doron Z Dushnitzky,6 Nirit Borovsky,6 Moshe B Hoshen,3 Gili Focht,1 Malka Avitzour,1 Yael Shachar,1 Yehuda Chowers,7 Rami Eliakim,8 Shomron Ben-Horin,8 Shmuel Odes,9 Doron Schwartz,9 Iris Dotan,10 Eran Israeli,11 Zohar Levi,10 Eric I Benchimol,12,13,14 Ran D Balicer,3 and Dan Turner1, On behalf of the Israeli IBD Research Nucleus (IIRN)

Publish date

2018;