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Mogrol

$512

  • Brand : BIOFRON

  • Catalogue Number : BD-D1124

  • Specification : HPLC≥98%

  • CAS number : 88930-15-8

  • Formula : C30H52O4

  • Molecular Weight : 476.742

  • PUBCHEM ID : 14525327

  • Volume : 20mg

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Catalogue Number

BD-D1124

Analysis Method

HPLC,NMR,MS

Specification

HPLC≥98%

Storage

-20℃

Molecular Weight

476.742

Appearance

Powder

Botanical Source

Siraitiae fructus

Structure Type

Triterpenoids

Category

Standards;Natural Pytochemical;API

SMILES

CC(CCC(C(C)(C)O)O)C1CCC2(C1(CC(C3(C2CC=C4C3CCC(C4(C)C)O)C)O)C)C

Synonyms

Estr-5-ene-3,11-diol, 17-[(1R,4R)-4,5-dihydroxy-1,5-dimethylhexyl]-4,4,9,14-tetramethyl-, (3β,9β,10α,11α,17β)-/(1S,4R,9β,11α,24R)-9,10,14-Trimethyl-4,9-cyclo-9,10-secocholest-5-ene-1,11,24,25-tetrol

IUPAC Name

(3S,8S,9R,10R,11R,13R,14S,17R)-17-[(2R,5R)-5,6-dihydroxy-6-methylheptan-2-yl]-4,4,9,13,14-pentamethyl-2,3,7,8,10,11,12,15,16,17-decahydro-1H-cyclopenta[a]phenanthrene-3,11-diol

Applications

Mogrol is a biometabolite of mogrosides, and acts via inhibition of the ERK1/2 and STAT3 pathways, or reducing CREB activation and activating AMPK signaling.

Density

1.1±0.1 g/cm3

Solubility

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

Flash Point

242.9±24.7 °C

Boiling Point

595.6±50.0 °C at 760 mmH

Melting Point

InChl

InChI=1S/C30H52O4/c1-18(9-13-24(32)27(4,5)34)19-15-16-28(6)22-12-10-20-21(11-14-23(31)26(20,2)3)30(22,8)25(33)17-29(19,28)7/h10,18-19,21-25,31-34H,9,11-17H2,1-8H3/t18-,19-,21-,22+,23+,24-,25-,28+,29-,30+/m1/s1

InChl Key

JLYBBRAAICDTIS-AYEHCKLZSA-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#:88930-15-8) 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

27555947

Abstract

We report the syntheses and characterization of three solution-processable phen­oxy silicon phthalocyanines (SiPcs), namely bis­(3-methyl­phen­oxy)(phthalocyanine)silicon [(3MP)2-SiPc], C46H30N8O2Si, bis­(2-sec-butyl­phen­oxy)(phthalocyanine)silicon [(2secBP)2-SiPc], C44H24I2N8O2Si, and bis­(3-iodo­phen­oxy)(phthalocyanine)silicon [(3IP)2-SiPc], C52H42N8O2Si. Crystals grown of these compounds were characterized by single-crystal X-ray diffraction and the π-π inter­actions between the aromatic SiPc cores were studied. It was determined that (3MP)2-SiPc has similar inter­actions to previously reported bis­(3,4,5-tri­fluoro­phen­oxy)silicon phthalocyanines [(345 F)2-SiPc] with significant π-π inter­actions between the SiPc groups. (3IP)2-SiPc and (2secBP)2-SiPc both experienced a parallel stacking of two of the peripheral aromatic groups. In all three cases, the solubility of these mol­ecules was increased by the addition of phen­oxy groups while maintaining π-π inter­actions between the aromatic SiPc groups. The solubility of (2secBP)2-SiPc was significantly higher than other bis-phen­oxy-SiPcs and this was exemplified by the higher observed disorder within the crystal structure.

KEYWORDS

crystal structure, silicon, phthalocyanine, phenol, phen­oxy, phen­oxy­lation, inter­actions, halogen, bonds

Title

Crystal structures of bis­(phen­oxy)silicon phthalocyanines: increasing π-π inter­actions, solubility and disorder and no halogen bonding observed

Author

Benoît H. Lessard,a,b Alan J. Lough,c and Timothy P. Bendera,d,c,*

Publish date

2016 Jul 1

PMID

29953556

Abstract

The propagation of concepts in a population of agents is a form of influence spread, which can be modelled as a cascade from a set of initially activated individuals. The study of such influence cascades, in particular the identification of influential individuals, has a wide range of applications including epidemic control, viral marketing and the study of social norms. In real-world environments there may be many concepts spreading and interacting. These interactions can affect the spread of a given concept, either boosting it and allowing it to spread further, or inhibiting it and limiting its capability to spread. Previous work does not consider how the interactions between concepts affect concept spread. Taking concept interactions into consideration allows for indirect concept manipulation, meaning that we can affect concepts we are not able to directly control. In this paper, we consider the problem of indirect concept manipulation, and propose heuristics for indirectly boosting or inhibiting concept spread in environments where concepts interact. We define a framework that allows for the interactions between any number of concepts to be represented, and present a heuristic that aims to identify important influence paths for a given target concept in order to manipulate its spread. We compare the performance of this heuristic, called maximum probable gain, against established heuristics for manipulating influence spread.

Title

Manipulating concept spread using concept relationships

Author

James Archbold, Conceptualization, Formal analysis, Investigation, Methodology, Software, Writing - original draft, Writing - review & editing#* and Nathan Griffiths, Conceptualization, Investigation, Methodology, Project administration, Supervision, Writing - review & editing#

Publish date

2018

PMID

30997489

Abstract

Background
The use of environmental DNA for species detection via metabarcoding is growing rapidly. We present a co-designed lab workflow and bioinformatic pipeline to mitigate the 2 most important risks of environmental DNA use: sample contamination and taxonomic misassignment. These risks arise from the need for polymerase chain reaction (PCR) amplification to detect the trace amounts of DNA combined with the necessity of using short target regions due to DNA degradation.

Findings
Our high-throughput workflow minimizes these risks via a 4-step strategy: (i) technical replication with 2 PCR replicates and 2 extraction replicates; (ii) using multi-markers (12S,16S,CytB); (iii) a “twin-tagging,” 2-step PCR protocol; and (iv) use of the probabilistic taxonomic assignment method PROTAX, which can account for incomplete reference databases. Because annotation errors in the reference sequences can result in taxonomic misassignment, we supply a protocol for curating sequence datasets. For some taxonomic groups and some markers, curation resulted in >50% of sequences being deleted from public reference databases, owing to (i) limited overlap between our target amplicon and reference sequences, (ii) mislabelling of reference sequences, and (iii) redundancy. Finally, we provide a bioinformatic pipeline to process amplicons and conduct PROTAX assignment and tested it on an invertebrate-derived DNA dataset from 1,532 leeches from Sabah, Malaysia. Twin-tagging allowed us to detect and exclude sequences with non-matching tags. The smallest DNA fragment (16S) amplified most frequently for all samples but was less powerful for discriminating at species rank. Using a stringent and lax acceptance criterion we found 162 (stringent) and 190 (lax) vertebrate detections of 95 (stringent) and 109 (lax) leech samples.

Conclusions
Our metabarcoding workflow should help research groups increase the robustness of their results and therefore facilitate wider use of environmental and invertebrate-derived DNA, which is turning into a valuable source of ecological and conservation information on tetrapods.

KEYWORDS

metabarcoding, invertebrate-derived -DNA, environmental DNA, leeches

Title

An efficient and robust laboratory workflow and tetrapod database for larger scale environmental DNA studies

Author

Jan Axtner,1 Alex Crampton-Platt,1 Lisa A Horig,1 Azlan Mohamed,1 Charles C Y Xu,2,3,4 Douglas W Yu,2,5 and Andreas Wilting1

Publish date

2019 Apr