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8alpha-Hydroxylabda-13(16),14-dien-19-yl p-hydroxycinnamate

$830

  • Brand : BIOFRON

  • Catalogue Number : AV-B02355

  • Specification : 95%

  • CAS number : 117254-98-5

  • Formula : C29H40O4

  • Molecular Weight : 452.63

  • PUBCHEM ID : 73554042

  • Volume : 5mg

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Catalogue Number

AV-B02355

Analysis Method

HPLC,NMR,MS

Specification

95%

Storage

2-8°C

Molecular Weight

452.63

Appearance

Powder

Botanical Source

Structure Type

Category

Standards;Natural Pytochemical;API

SMILES

CC1(CCCC2(C1CCC(C2CCC(=C)C=C)(C)O)C)COC(=O)C=CC3=CC=C(C=C3)O

Synonyms

2-Propenoic acid, 3-(4-hydroxyphenyl)-, [(1S,4aS,5R,6R,8aR)-decahydro-6-hydroxy-1,4a,6-trimethyl-5-(3-methylene-4-penten-1-yl)-1-naphthalenyl]methyl ester, (2E)-/[(1S,4aS,5R,6R,8aR)-6-Hydroxy-1,4a,6-trimethyl-5-(3-methylene-4-penten-1-yl)decahydro-1-naphthalenyl]methyl (2E)-3-(4-hydroxyphenyl)acrylate

IUPAC Name

[(4aS,5R,6S,8aR)-6-hydroxy-1,4a,6-trimethyl-5-(3-methylidenepent-4-enyl)-3,4,5,7,8,8a-hexahydro-2H-naphthalen-1-yl]methyl (E)-3-(4-hydroxyphenyl)prop-2-enoate

Applications

Density

1.1±0.1 g/cm3

Solubility

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

Flash Point

180.0±16.7 °C

Boiling Point

571.1±25.0 °C at 760 mmHg

Melting Point

InChl

InChI=1S/C29H40O4/c1-6-21(2)8-14-25-28(4)18-7-17-27(3,24(28)16-19-29(25,5)32)20-33-26(31)15-11-22-9-12-23(30)13-10-22/h6,9-13,15,24-25,30,32H,1-2,7-8,14,16-20H2,3-5H3/b15-11+/t24-,25+,27?,28-,29-/m0/s1

InChl Key

LXORINFASUBZBQ-WCZLRCKKSA-N

WGK Germany

RID/ADR

HS Code Reference

Personal Projective Equipment

Correct Usage

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

Meta Tag

provides coniferyl ferulate(CAS#:117254-98-5) 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

28700366

Abstract

Objectives:
The study examined sex differences in trend and clinical characteristics of cannabis use disorder (CUD) diagnosis involved hospitalizations among adult patients.

Methods:
We analyzed hospitalization data from the 2007-2011 Nationwide Inpatient Samples for patients aged 18-64 years (N?=?15,114,930). Descriptive statistics were used to characterize demographic variables and to compare the proportions of CUD diagnosis and comorbid patterns between male and female hospitalizations. Logistic regressions were performed to examine the association of sex and other demographic variables with CUD diagnosis.

Results:
During the study period, 3.3% of male and 1.5% of female hospitalizations had any-listed CUD diagnoses, and both sexes presented an upward trend in the number, rate, and proportion of CUD diagnosis. Among hospitalizations for patients aged 18-25 years, about 1 in 10 males and 1 in 20 females included a CUD diagnosis, and this proportion decreased with age strata. Mental disorders accounted for the highest proportion of CUD involved inpatient hospitalizations, and female CUD involved hospitalizations included a higher proportion of mental disorders that required hospitalized care compared with male hospitalizations (41% vs 36%). In each sex group, younger age, black race, lower household income, large metropolitan residence, non-private insurance, substance use diagnosis, and mental disorders were associated with elevated odds of having CUD diagnosis.

Conclusion:
The large sample of clinical hospitalization data suggest an increased trend in CUD diagnosis and sex differences in several comorbidities with CUD-involved hospital admissions. Prevention and treatment for CUD should consider sex differences in clinical comorbidities.

KEYWORDS

cannabis use disorder, hospitalization, mental disorder, sex

Title

Sex Differences in Cannabis Use Disorder Diagnosis Involved Hospitalizations in the United States

Author

He Zhu, PhD and Li-Tzy Wu, ScD, RN, MA

Publish date

2017 Sep;

PMID

25083866

Abstract

Adaptation of the endoplasmic reticulum (ER) pathway for MHC class I (MHC-I) presentation in dendritic cells enables cross-presentation of peptides derived from phagocytosed microbes, infected cells, or tumor cells to CD8 T cells. How these peptides intersect with MHC-I molecules remains poorly understood. Here, we show that MHC-I selectively accumulate within phagosomes carrying microbial components, which engage Toll-like receptor (TLR) signaling. Although cross-presentation requires Sec22b-mediated phagosomal recruitment of the peptide loading complex from the ER-Golgi intermediate compartment (ERGIC), this step is independent of TLR signaling and does not deliver MHC-I. Instead, MHC-I are recruited from an endosomal recycling compartment (ERC), which is marked by Rab11a, VAMP3/cellubrevin, and VAMP8/endobrevin and holds large reserves of MHC-I. While Rab11a activity stocks ERC stores with MHC-I, MyD88-dependent TLR signals drive IκB-kinase (IKK)2-mediated phosphorylation of phagosome-associated SNAP23. Phospho-SNAP23 stabilizes SNARE complexes orchestrating ERC-phagosome fusion, enrichment of phagosomes with ERC-derived MHC-I, and subsequent cross-presentation during infection.

Title

TLR Signals Induce Phagosomal MHC-I Delivery from the Endosomal Recycling Compartment to Allow Cross-Presentation

Author

Priyanka Nair-Gupta,1,3,5 Alessia Baccarini,1,2,4 Navpreet Tung,1,3,5 Fabian Seyffer,6 Oliver Florey,7 Yunjie Huang,8 Meenakshi Huang,8 Michael Overholtzer,7 Paul A. Roche,9 Robert Tampe,6 Brian D. Brown,1,2,4 Derk Amsen,10 Sidney W. Whiteheart,8 and J. Magarian Blander1,2,3,*

Publish date

2015 Jul 31.

PMID

20140251

Abstract

For more than four decades the cause of most type A influenza virus infections of humans has been attributed to only two viral subtypes, A/H1N1 or A/H3N2. In contrast, avian and other vertebrate species are a reservoir of type A influenza virus genome diversity, hosting strains representing at least 120 of 144 combinations of 16 viral hemagglutinin and 9 viral neuraminidase subtypes. Viral genome segment reassortments and mutations emerging within this reservoir may spawn new influenza virus strains as imminent epidemic or pandemic threats to human health and poultry production. Traditional methods to detect and differentiate influenza virus subtypes are either time-consuming and labor-intensive (culture-based) or remarkably insensitive (antibody-based). Molecular diagnostic assays based upon reverse transcriptase-polymerase chain reaction (RT-PCR) have short assay cycle time, and high analytical sensitivity and specificity. However, none of these diagnostic tests determine viral gene nucleotide sequences to distinguish strains and variants of a detected pathogen from one specimen to the next. Decision-quality, strain- and variant-specific pathogen gene sequence information may be critical for public health, infection control, surveillance, epidemiology, or medical/veterinary treatment planning. The Resequencing Pathogen Microarray (RPM-Flu) is a robust, highly multiplexed and target gene sequencing-based alternative to both traditional culture- or biomarker-based diagnostic tests. RPM-Flu is a single, simultaneous differential diagnostic assay for all subtype combinations of type A influenza viruses and for 30 other viral and bacterial pathogens that may cause influenza-like illness. These other pathogen targets of RPM-Flu may co-infect and compound the morbidity and/or mortality of patients with influenza. The informative specificity of a single RPM-Flu test represents specimen-specific viral gene sequences as determinants of virus type, A/HN subtype, virulence, host-range, and resistance to antiviral agents.

Title

Single Assay for Simultaneous Detection and Differential Identification of Human and Avian Influenza Virus Types, Subtypes, and Emergent Variants

Author

David Metzgar, 1 Christopher A. Myers, 1 Kevin L. Russell, 1 , 2 Dennis Faix, 1 Patrick J. Blair, 1 Jason Brown, 1 Scott Vo, 1 David E. Swayne, 3 Colleen Thomas, 3 David A. Stenger, 4 Baochuan Lin, 4 Anthony P. Malanoski, 4 Zheng Wang, 4 Kate M. Blaney, 3 Nina C. Long, 4 Joel M. Schnur, 4 , 5 Magdi D. Saad, 6 Lisa A. Borsuk, 7 Agnieszka M. Lichanska, 7 Matthew C. Lorence, 7 Brian Weslowski, 7 Klaus O. Schafer, 7 and Clark Tibbetts 7 , *

Publish date

2010;