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Periplocogenin

$1,376

  • Brand : BIOFRON

  • Catalogue Number : BN-O0951

  • Specification : 98%(HPLC)

  • CAS number : 112899-63-5

  • Formula : C28H42O6

  • Molecular Weight : 474.63

  • Volume : 5mg

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Catalogue Number

BN-O0951

Analysis Method

HPLC,NMR,MS

Specification

98%(HPLC)

Storage

2-8°C

Molecular Weight

474.63

Appearance

Powder

Botanical Source

Structure Type

Steroids

Category

Standards;Natural Pytochemical;API

SMILES

CC1C=C(C(=O)C(O1)OC2CCC3(C4CCC5(C(C4CC=C3C2)CCC5(C(C)OC6CC(C(C(O6)C)O)OCOC)O)C)C)OC

Synonyms

periplocogenin

IUPAC Name

Density

Solubility

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

Flash Point

Boiling Point

Melting Point

InChl

InChI=1S/C46H57NO14/c1-8-9-12-21-33(51)47-35(28-17-13-10-14-18-28)36(52)42(55)59-30-23-46(56)40(60-41(54)29-19-15-11-16-20-29)38-44(7,31(50)22-32-45(38,24-57-32)61-27(4)49)39(53)37(58-26(3)48)34(25(30)2)43(46,5)6/h10-11,13-20,30-32,35-38,40,50,52,56H,8-9,12,21-24H2,1-7H3,(H,47,51)/t30-,31-,32+,35-,36+,37+,38-,40-,44+,45-,46+/m0/s1

InChl Key

OPDVJOHDZLWTNG-YIQCCZLZSA-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#:112899-63-5) 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

31138300

Abstract

Background
Environmental exposures are related to the risk of some types of cancer, and children are the most vulnerable group of people. This study seeks to present the methodological approaches used in the papers of our group about risk of childhood cancers in the vicinity of pollution sources (industrial and urban sites). A population-based case-control study of incident childhood cancers in Spain and their relationship with residential proximity to industrial and urban areas was designed. Two methodological approaches using mixed multiple unconditional logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals (95% CIs) were developed: (a) “near vs. far” analysis, where possible excess risks of cancers in children living near (“near”) versus those living far (“far”) from industrial and urban areas were assessed; and (b) “risk gradient” analysis, where the risk gradient in the vicinity of industries was assessed. For each one of the two approaches, three strategies of analysis were implemented: “joint”, “stratified”, and “individualized” analysis. Incident cases were obtained from the Spanish Registry of Childhood Cancer (between 1996 and 2011).

Results
Applying this methodology, associations between proximity (≤ 2 km) to specific industrial and urban zones and risk (OR; 95% CI) of leukemias (1.31; 1.04-1.65 for industrial areas, and 1.28; 1.00-1.53 for urban areas), neuroblastoma (2.12; 1.18-3.83 for both industrial and urban areas), and renal (2.02; 1.16-3.52 for industrial areas) and bone (4.02; 1.73-9.34 for urban areas) tumors have been suggested.

Conclusions
The two methodological approaches were used as a very useful and flexible tool to analyze the excess risk of childhood cancers in the vicinity of industrial and urban areas, which can be extrapolated and generalized to other cancers and chronic diseases, and adapted to other types of pollution sources.

KEYWORDS

Cancer risk, Childhood cancer, Methodology, Industrial pollution, Urban pollution, Case-control study

Title

Methodological approaches to the study of cancer risk in the vicinity of pollution sources: the experience of a population-based case-control study of childhood cancer

Author

Javier Garcia-Perez,corresponding author1,2 Diana Gomez-Barroso,2,3 Ibon Tamayo-Uria,4 and Rebeca Ramis1,2

Publish date

2019

PMID

24653211

Abstract

The largest genus in the conifer family Pinaceae is Pinus, with over 100 species. The size and complexity of their genomes (∼20-40 Gb, 2n = 24) have delayed the arrival of a well-annotated reference sequence. In this study, we present the annotation of the first whole-genome shotgun assembly of loblolly pine (Pinus taeda L.), which comprises 20.1 Gb of sequence. The MAKER-P annotation pipeline combined evidence-based alignments and ab initio predictions to generate 50,172 gene models, of which 15,653 are classified as high confidence. Clustering these gene models with 13 other plant species resulted in 20,646 gene families, of which 1554 are predicted to be unique to conifers. Among the conifer gene families, 159 are composed exclusively of loblolly pine members. The gene models for loblolly pine have the highest median and mean intron lengths of 24 fully sequenced plant genomes. Conifer genomes are full of repetitive DNA, with the most significant contributions from long-terminal-repeat retrotransposons. In depth analysis of the tandem and interspersed repetitive content yielded a combined estimate of 82%.

KEYWORDS

introns, gene family, repeats, retrotransposons, conifer

Title

Unique Features of the Loblolly Pine (Pinus taeda L.) Megagenome Revealed Through Sequence Annotation

Author

Jill L. Wegrzyn,*,1 John D. Liechty,* Kristian A. Stevens,† Le-Shin Wu,‡ Carol A. Loopstra,§ Hans A. Vasquez-Gross,* William M. Dougherty,† Brian Y. Lin,* Jacob J. Zieve,* Pedro J. Martinez-Garcia,* Carson Holt,** Mark Yandell,** Aleksey V. Zimin,†† James A. Yorke,††‡‡ Marc W. Crepeau,† Daniela Puiu,§§ Steven L. Salzberg,§§ Pieter J. de Jong,*** Keithanne Mockaitis,††† Doreen Main,§§§ Charles H. Langley,† and David B. Neale*

Publish date

2014 Mar;

PMID

26502879

Abstract

Background
Chronic diseases and multimorbidity are common in western countries and associated with increased breast cancer mortality. This study aims to investigate non-participation in breast cancer screening among women with chronic diseases and multimorbidity and the role of time in this association.

Method
This population-based cohort study used regional and national registries. Women who were invited to the first breast cancer screening round in the Central Denmark Region in 2008-09 were included (n = 149,234). Selected chronic diseases and multimorbidity were assessed up to 10 years before the screening date. Prevalence ratios (PR) were used as an association measure.

Results
The results indicated that women with at least one chronic condition were significantly more likely not to participate in breast cancer screening. In adjusted analysis, a significantly higher likelihood of non-participation was found for women with cancer (PR = 1.50, 95 % CI: 1.40-1.60), mental illness (PR = 1.51, 95 % CI: 1.42-1.60), chronic obstructive pulmonary disease (PR = 1.51, 95 % CI: 1.42-1.62), neurological disorders (PR = 1.24, 95 % CI: 1.12-1.37) and kidney disease (PR = 1.70, 95 % CI 1.49-1.94), whereas women with chronic bowel disease (PR = 0.75, 95 % CI 0.65-0.88) were more likely to participate than women without these disease. Multimorbidity was associated with increased non-participation likelihood. E.g. having 3 or more diseases was associated with 58 % increased non-participation likelihood (95 % CI: 27-96 %). Higher non-participation was also observed for women with severe multimorbidity (PR = 1.53, 95 % CI: 1.23-1.90) and mental-physical multimorbidity (PR = 1.54, 95 % CI: 1.36-1.75).

Conclusion
In conclusion, we found a strong association between non-participation in breast cancer screening for some chronic diseases and for multimorbidity. The highest propensity not to participate was observed for women with hospital contacts related to the chronic disease in the period closest to the screening date.

Electronic supplementary material
The online version of this article (doi:10.1186/s12885-015-1829-1) contains supplementary material, which is available to authorized users.

KEYWORDS

Chronic disease, Multimorbidity, Breast cancer screening, Mammography screening, Participation, Non-attendance, Denmark

Title

Non-participation in breast cancer screening for women with chronic diseases and multimorbidity: a population-based cohort study

Author

L. F. Jensen, A. F. Pedersen, B. Andersen, M. Vestergaard, P. Vedsted

Publish date

2015;


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