Catalogue Number
BD-P0073
Analysis Method
HPLC,NMR,MS
Specification
95.0%(HPLC)
Storage
2-8°C
Molecular Weight
436.67
Appearance
White cryst.
Botanical Source
Structure Type
Triterpenoids
Category
SMILES
CC(CCC=C(C)C=O)C1CCC2(C1(CC=C3C2=CCC4C3(CCC(=O)C4(C)C)C)C)C
Synonyms
(E,6R)-2-methyl-6-[(5R,10S,13R,14R,17R)-4,4,10,13,14-pentamethyl-3-oxo-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]hept-2-enal
IUPAC Name
(E,6R)-2-methyl-6-[(5R,10S,13R,14R,17R)-4,4,10,13,14-pentamethyl-3-oxo-1,2,5,6,12,15,16,17-octahydrocyclopenta[a]phenanthren-17-yl]hept-2-enal
Density
1.0±0.1 g/cm3
Solubility
Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.
Flash Point
202.0±27.1 °C
Boiling Point
551.0±50.0 °C at 760 mmHg
Melting Point
127-128℃
InChl
InChI=1S/C30H44O2/c1-20(19-31)9-8-10-21(2)22-13-17-30(7)24-11-12-25-27(3,4)26(32)15-16-28(25,5)23(24)14-18-29(22,30)6/h9,11,14,19,21-22,25H,8,10,12-13,15-18H2,1-7H3/b20-9+/t21-,22-,25+,28-,29-,30+/m1/s1
InChl Key
RHNFCIPJKSUUES-SPFFTVLFSA-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#:104700-98-3) 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.
15613248
Background
Cluster sample study designs are cost effective, however cluster samples violate the simple random sample assumption of independence of observations. Failure to account for the intra-cluster correlation of observations when sampling through clusters may lead to an under-powered study. Researchers therefore need estimates of intra-cluster correlation for a range of outcomes to calculate sample size. We report intra-cluster correlation coefficients observed within a large-scale cross-sectional study of general practice in Australia, where the general practitioner (GP) was the primary sampling unit and the patient encounter was the unit of inference.
Methods
Each year the Bettering the Evaluation and Care of Health (BEACH) study recruits a random sample of approximately 1,000 GPs across Australia. Each GP completes details of 100 consecutive patient encounters. Intra-cluster correlation coefficients were estimated for patient demographics, morbidity managed and treatments received. Intra-cluster correlation coefficients were estimated for descriptive outcomes and for associations between outcomes and predictors and were compared across two independent samples of GPs drawn three years apart.
Results
Between April 1999 and March 2000, a random sample of 1,047 Australian general practitioners recorded details of 104,700 patient encounters. Intra-cluster correlation coefficients for patient demographics ranged from 0.055 for patient sex to 0.451 for language spoken at home. Intra-cluster correlations for morbidity variables ranged from 0.005 for the management of eye problems to 0.059 for management of psychological problems. Intra-cluster correlation for the association between two variables was smaller than the descriptive intra-cluster correlation of each variable. When compared with the April 2002 to March 2003 sample (1,008 GPs) the estimated intra-cluster correlation coefficients were found to be consistent across samples.
Conclusions
The demonstrated precision and reliability of the estimated intra-cluster correlations indicate that these coefficients will be useful for calculating sample sizes in future general practice surveys that use the GP as the primary sampling unit.
Observed intra-cluster correlation coefficients in a cluster survey sample of patient encounters in general practice in Australia
Stephanie A Knoxcorresponding author1 and Patty Chondros2
2004
31601202
Background
Approximately 2.9 million deaths are attributed to ambient fine particle air pollution around the world each year (PM2.5). In general, cohort studies of mortality and outdoor PM2.5 concentrations have limited information on individuals exposed to low levels of PM2.5 as well as covariates such as smoking behaviours, alcohol consumption, and diet which may confound relationships with mortality. This study provides an updated and extended analysis of the Canadian Community Health Survey-Mortality cohort: a population-based cohort with detailed PM2.5 exposure data and information on a number of important individual-level behavioural risk factors. We also used this rich dataset to provide insight into the shape of the concentration-response curve for mortality at low levels of PM2.5.
Methods
Respondents to the Canadian Community Health Survey from 2000 to 2012 were linked by postal code history from 1981 to 2016 to high resolution PM2.5 exposure estimates, and mortality incidence to 2016. Cox proportional hazard models were used to estimate the relationship between non-accidental mortality and ambient PM2.5 concentrations (measured as a three-year average with a one-year lag) adjusted for socio-economic, behavioural, and time-varying contextual covariates.
Results
In total, 50,700 deaths from non-accidental causes occurred in the cohort over the follow-up period. Annual average ambient PM2.5 concentrations were low (i.e. 5.9 μg/m3, s.d. 2.0) and each 10 μg/m3 increase in exposure was associated with an increase in non-accidental mortality (HR = 1.11; 95% CI 1.04-1.18). Adjustment for behavioural covariates did not materially change this relationship. We estimated a supra-linear concentration-response curve extending to concentrations below 2 μg/m3 using a shape constrained health impact function. Mortality risks associated with exposure to PM2.5 were increased for males, those under age 65, and non-immigrants. Hazard ratios for PM2.5 and mortality were attenuated when gaseous pollutants were included in models.
Conclusions
Outdoor PM2.5 concentrations were associated with non-accidental mortality and adjusting for individual-level behavioural covariates did not materially change this relationship. The concentration-response curve was supra-linear with increased mortality risks extending to low outdoor PM2.5 concentrations.
PM2.5, Air pollution, Canada, Cohort study, Fine particulate matter, Mortality, Fine particle air pollution
Low concentrations of fine particle air pollution and mortality in the Canadian Community Health Survey cohort
Tanya Christidis,corresponding author1 Anders C. Erickson,2 Amanda J. Pappin,1,13 Daniel L. Crouse,3 Lauren L. Pinault,1 Scott A. Weichenthal,4,5 Jeffrey R. Brook,6,12 Aaron van Donkelaar,7,11 Perry Hystad,8 Randall V. Martin,7,9,11 Michael Tjepkema,1 Richard T. Burnett,10 and Michael Brauer2
2019
31354303
Circular RNAs (circRNAs) are a newly discovered class of endogenous non-coding RNAs. Owing to the development of high-throughput sequencing, researchers have identified thousands of circRNAs. Emerging evidence suggests that circRNAs are involved in various tumor cell processes, including proliferation, apoptosis, invasion and migration. Because of their high stability and abundance, tissue-specific expression, and easy detection, circRNAs are considered ideal biomarkers for cancer diagnosis and prognosis. An increasing number of studies have recently demonstrated that circRNAs are closely associated with colorectal cancer (CRC). CRC is the third most common cancer and the second leading cause of cancer-related death globally. Thus, understanding the molecular mechanisms involved in the development and progression of CRC is vital. In this review, we summarize the current literature regarding human circRNAs related to CRC and present an overview of the potential clinical implications of circRNAs with respect to CRC.
circRNA, biomarker, diagnosis, prognosis
Emerging roles of circular RNAs in colorectal cancer
Shuhong Hao,1 Liang Cong,2 Rongfeng Qu,1 Rui Liu,1 Guizhen Zhang,3 and Yarong Li1
2019;
