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  • Brand : BIOFRON

  • Catalogue Number : BD-P0534

  • Specification : 98.0%(HPLC)

  • CAS number : 28178-92-9

  • Formula : C21H22O5

  • Molecular Weight : 354.4

  • PUBCHEM ID : 5281817

  • Volume : 25mg

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Catalogue Number


Analysis Method






Molecular Weight




Botanical Source

Structure Type



Standards;Natural Pytochemical;API




2-Allyl-4-[(1E)-1-(1,3-benzodioxol-5-yl)-1-propen-2-yl]-4,5-dimethoxy-2,5-cyclohexadien-1-one/2-Allyl-4-[(E)-2-(1,3-benzodioxol-5-yl)-1-methylethenyl]-4,5-dimethoxy-2,5-cyclohexadien-1-one/(E)-2-Allyl-4-(2-(1,3-benzodioxol-5-yl)-1-methylvinyl)-4,5-dimethoxy-2,5-cyclohexadien-1-one/2,5-Cyclohexadien-1-one, 4-[(E)-2-(1,3-benzodioxol-5-yl)-1-methylethenyl]-4,5-dimethoxy-2-(2-propen-1-yl)-/Futoquinol





1.2±0.1 g/cm3


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

Flash Point

225.5±30.2 °C

Boiling Point

515.0±50.0 °C at 760 mmHg

Melting Point



InChl Key


WGK Germany


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#:28178-92-9) MSDS, density, melting point, boiling point, structure, formula, molecular weight etc. Articles of coniferyl ferulate are included as well.>> amp version: coniferyl ferulate

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Background and objectives
Hypophosphatemia is common in the first year after kidney transplantation, but its clinical implications are unclear. We investigated the relationship between the severity of post-transplant hypophosphatemia and mortality or death-censored graft failure in a large cohort of renal transplant recipients with long-term follow-up.

Design, setting, participants, & measurements
We performed a longitudinal cohort study in 957 renal transplant recipients who were transplanted between 1993 and 2008 at a single center. We used a large real-life dataset containing 28,178 phosphate measurements (median of 27; first to third quartiles, 23-34) serial measurements per patient) and selected the lowest intraindividual phosphate level during the first year after transplantation. The primary outcomes were all-cause mortality, cardiovascular mortality, and death-censored graft failure.

The median (interquartile range) intraindividual lowest phosphate level was 1.58 (1.30-1.95) mg/dl, and it was reached at 33 (21-51) days post-transplant. eGFR was the main correlate of the lowest serum phosphate level (model R2 =0.32). During 9 (5-12) years of follow-up, 181 (19%) patients developed graft failure, and 295 (35%) patients died, of which 94 (32%) deaths were due to cardiovascular disease. In multivariable Cox regression analysis, more severe hypophosphatemia was associated with a lower risk of death-censored graft failure (fully adjusted hazard ratio, 0.61; 95% confidence interval, 0.43 to 0.88 per 1 mg/dl lower serum phosphate) and cardiovascular mortality (fully adjusted hazard ratio, 0.37; 95% confidence interval, 0.22 to 0.62) but not noncardiovascular mortality (fully adjusted hazard ratio, 1.33; 95% confidence interval, 0.9 to 1.96) or all-cause mortality (fully adjusted hazard ratio, 1.15; 95% confidence interval, 0.81 to 1.61).

Post-transplant hypophosphatemia develops early after transplantation. These data connect post-transplant hypophosphatemia with favorable long-term graft and patient outcomes.


hypophosphatemia, graft survival, mortality, transplantation, chronic kidney disease-mineral and bone disorder, phosphate, Cardiovascular Diseases, Confidence Intervals, Follow-Up Studies, Humans, Hypophosphatemia, kidney transplantation, Longitudinal Studies, Phosphates, Proportional Hazards Models, Regression Analysis, Renal Insufficiency, Chronic, Risk


Post-Transplant Hypophosphatemia and the Risk of Death-Censored Graft Failure and Mortality after Kidney Transplantation


Marco van Londen, Brigitte M. Aarts, Petronella E. Deetman, Jessica van der Weijden, Michele F. Eisenga, Gerjan Navis, Stephan J. L. Bakker, Martin H. de Borst

Publish date

2017 Aug 7;




CS1 is one of a limited number of serologically distinct pili found in enterotoxigenic Escherichia coli (ETEC) strains associated with disease in people. The genes for the CS1 pilus are on a large plasmid, pCoo. We show that pCoo is not self-transmissible, although our sequence determination for part of pCoo shows regions almost identical to those in the conjugative drug resistance plasmid R64. When we introduced R64 into a strain containing pCoo, we found that pCoo was transferred to a recipient strain in mating. Most of the transconjugant pCoo plasmids result from recombination with R64, leading to acquisition of functional copies of all of the R64 transfer genes. Temporary coresidence of the drug resistance plasmid R64 with pCoo leads to a permanent change in pCoo so that it is now self-transmissible. We conclude that when R64-like plasmids are transmitted to an ETEC strain containing pCoo, their recombination may allow for spread of the pCoo plasmid to other enteric bacteria.


Horizontal Transfer of CS1 Pilin Genes of Enterotoxigenic Escherichia coli


Barbara Froehlich,1 Erik Holtzapple,2,† Timothy D. Read,2,‡ and June R. Scott1,*

Publish date

2004 May;




Data on age-sequenced trajectories of individuals’ attributes are used for a growing number of research purposes. However, there is no consensus about which method to use to identify the number of discrete trajectories in a population or to assign individuals to a specific trajectory group. We modeled real and simulated trajectory data using “naïve” methods, optimal matching, grade of membership models, and three types of finite mixture models. We found that these methods produced inferences about the number of trajectories that frequently differ (1) from one another and (2) from the truth as represented by simulation parameters. We also found that they differed in the assignment of individuals to trajectory groups. In light of these findings, we argue that researchers should interpret results based on these methods cautiously, neither reifying point estimates about the number of trajectories nor treating individuals’ trajectory group assignments as certain.

Investigators across a wide range of disciplines are concerned with characteristics and processes that vary systematically with subjects’ age. In recent years, researchers have studied age-sequenced patterns of delinquent behaviors (e.g., Miller, Malone and Dodge 2010), family and work roles (e.g., Rindfuss et al. 2010), chronic disabilities (e.g., Gill et al. 2010; Stallard et al. 2010), popularity (e.g., Moody et al. 2011), research productivity (e.g., Hunter and Leahey 2010), income (e.g., Rippeyoung and Noonan 2012), and academic achievement (e.g., Pianta et al. 2009), to name just a few substantive topics. In these and other areas of inquiry, scholars utilize ideas about trajectories of individual-level attributes, where trajectories are age-sequenced observations that reflect career, life course, or developmental patterns of stability and change for a particular focal attribute.1

Despite this widespread interest in trajectories, and despite the growing availability of rich age-graded data, there is no consensus about how to describe and model trajectories in the social sciences. When faced with similar data structures—repeated observation of some individual-level attribute(s) across ages—different researchers employ different assumptions and empirical strategies. In some cases, they assume there is a single underlying trajectory (around which individuals’ observations deviate) and estimate hierarchical or growth curve models. In other cases, researchers assume there are a small number of underlying latent trajectories that describe individuals’ experiences. In the latter case, analysts face two specific challenges: How should they identify and enumerate those latent trajectories? How should they determine which latent trajectory best describes each individual’s age-sequenced observations? As reviewed below, a variety of statistical approaches are used to meet these challenges. In practice, researchers typically choose just one of these approaches without considering the consequences of this choice for the validity and reliability of substantive conclusions.

In this article, we are concerned with applications in which researchers aim to identify and characterize a discrete set of latent trajectories and to classify individuals according to which trajectory best describes their age-graded observations. Our goal is to compare the relative validity and reliability of several alternative methods for identifying and describing trajectories and for assigning individuals to them. To this end, we pose four questions: (1) Do these methods lead to similar inferences about the number of latent trajectories that exist in a particular population? (2) Do they yield similar descriptions of the characteristics or qualities of those trajectories? (3) Do they lead to similar decisions about which trajectory best describes each individual’s age-graded observations? (4) Under what circumstances do these alternative methods yield the same conclusions and—perhaps most importantly—under what circumstances do they yield correct conclusions?

On one hand, it would be reassuring if the various methods all yielded valid and equivalent conclusions about the number and qualities of trajectories or about which trajectory best describes each individual’s age-graded observations. We would have greater confidence in the soundness of any number of substantive scientific projects that have (more or less uncritically) elected to use just one of these various methods. On the other hand, if the choice of method does matter for substantive conclusions, and especially if some methods yield more accurate conclusions than others, then it will be imperative to understand (1) whether there are situations in which the choice of methods is particularly consequential and (2) whether certain methods produce more valid results under particular empirical conditions. Our research is designed to address both of these issues.


Do Different Methods for Modeling Age-Graded Trajectories Yield Consistent and Valid Results?


John R. Warren, Liying Luo, Andrew Halpern-Manners, James M. Raymo, Alberto Palloni

Publish date

2017 May 15.