Catalogue Number
BD-P0120
Analysis Method
HPLC,NMR,MS
Specification
99.0%(HPLC)
Storage
2-8°C
Molecular Weight
408.5
Appearance
Cryst.
Botanical Source
Structure Type
Flavonoids
Category
SMILES
CC(=CCC(CC1=C(C=C(C2=C1OC(CC2=O)C3=CC=CC=C3O)O)O)C(=C)C)C
Synonyms
(2S)-5,7-dihydroxy-2-(2-hydroxyphenyl)-8-[(2R)-5-methyl-2-prop-1-en-2-ylhex-4-enyl]-2,3-dihydrochromen-4-one
IUPAC Name
(2S)-5,7-dihydroxy-2-(2-hydroxyphenyl)-8-[(2R)-5-methyl-2-prop-1-en-2-ylhex-4-enyl]-2,3-dihydrochromen-4-one
Density
1.2±0.1 g/cm3
Solubility
Soluble in Chloroform,Dichloromethane,Ethyl Acetate,DMSO,Acetone,etc.
Flash Point
195.8±23.6 °C
Boiling Point
581.7±50.0 °C at 760 mmHg
Melting Point
InChl
InChI=1S/C25H28O5/c1-14(2)9-10-16(15(3)4)11-18-20(27)12-21(28)24-22(29)13-23(30-25(18)24)17-7-5-6-8-19(17)26/h5-9,12,16,23,26-28H,3,10-11,13H2,1-2,4H3/t16-,23+/m1/s1
InChl Key
OGBMVWVBHWHRGD-MWTRTKDXSA-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#:99217-63-7) 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.
29793962
Background Hospitalizations and 30-day readmissions are common in the hemodialysis population. Actionable clinical markers for near-term hospital encounters are needed to identify individuals who require swift intervention to avoid hospitalization. Aspects of volume management, such as failed target weight (i.e, estimated dry weight) achievement, are plausible modifiable indicators of impending adverse events. The short-term consequences of failed target weight achievement are not well established.
Methods Statistically deidentified data were taken from a cohort of Medicare-enrolled, prevalent hemodialysis patients treated at a large dialysis organization from 2010 to 2012. We used a retrospective cohort design with repeated intervals, each consisting of 180-day baseline, 30-day exposure assessment, and 30-day follow-up period, to estimate the associations between failed target weight achievement and the risk of 30-day emergency department visits and hospitalizations. We estimated adjusted risk differences using inverse probability of exposure weighted Kaplan-Meier methods.
Results A total of 113,561 patients on hemodialysis contributed 788,722 study intervals to analyses. Patients who had a postdialysis weight >1.0 kg above the prescribed target weight in ≥30% (versus <30%) of exposure period treatments had a higher absolute risk (risk difference) of 30-day: emergency department visits (2.13%; 95% confidence interval, 2.00% to 2.32%); and all-cause (1.47%; 95% confidence interval, 1.34% to 1.62%), cardiovascular (0.31%; 95% confidence interval, 0.24% to 0.40%), and volume-related (0.15%; 95% confidence interval, 0.11% to 0.21%) hospitalizations. Conclusions In the absence of objective measures of volume status, recurrent failure to achieve target weight is an easily identifiable clinical risk marker for impending hospital encounters among patients on hemodialysis.
ED visits, hemodialysis, hospitalizations, target weight
Failed Target Weight Achievement Associates with Short-Term Hospital Encounters among Individuals Receiving Maintenance Hemodialysis
Magdalene M. Assimon,1 Lily Wang,2 and Jennifer E. Flythecorresponding author1,3
2018 Aug;
21481264
Background
Strategies to accurately identify the occurrence of specific health care events in administrative data is central to many quality improvement and research efforts. Many health care quality measures have treatment identification strategies based on diagnosis and procedure codes – an approach that is inexpensive and feasible but usually of unknown validity. In this study, we examined if the diagnosis/procedure code combinations used in the 2006 HEDIS Initiation and Engagement quality measures to identify instances of addiction treatment have high concordance with documentation of addiction treatment in clinical progress notes.
Methods
Four type of records were randomly sampled from VHA electronic medical data: (a) Outpatient records from a substance use disorder (SUD) specialty clinic with a HEDIS-qualified substance use disorder (SUD) diagnosis/CPT code combination (n = 700), (b) Outpatient records from a non-SUD setting with a HEDIS-qualified SUD diagnosis/CPT code combination (n = 592), (c) Specialty SUD Inpatient/residential records that included a SUD diagnosis (n = 700), and (d) Non-SUD specialty Inpatient/residential records that included a SUD diagnosis (n = 700). Clinical progress notes for the sampled records were extracted and two raters classified each as documenting or not documenting addiction treatment. Rates of concordance between the HEDIS addiction treatment identification strategy and the raters’ judgments were calculated for each record type.
Results
Within SUD outpatient clinics and SUD inpatient specialty units, 92% and 98% of sampled records had chart evidence of addiction treatment. Of outpatient encounters with a qualifying diagnosis/procedure code combination outside of SUD clinics, 63% had chart evidence of addiction treatment. Within non-SUD specialty inpatient units, only 46% of sampled records had chart evidence of addiction treatment.
Conclusions
For records generated in SUD specialty settings, the HEDIS strategy of identifying SUD treatment with diagnosis and procedure codes has a high concordance with chart review. The concordance rate outside of SUD specialty settings is much lower and highly variable between facilities. Therefore, some patients may be counted as meeting the 2006 HEDIS Initiation and Engagement criteria without having received the specified amount (or any) addiction treatment.
Validation of the treatment identification strategy of the HEDIS addiction quality measures: concordance with medical record review
Alex HS Harris,corresponding author1 Rachelle N Reeder,1 Laura S Ellerbe,1 and Thomas R Bowe1
2011
25422107
The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,677 children enrolled from birth who carry HLA-susceptibility genotypes for development of islet autoantibodies (IA) and type 1 diabetes (T1D). During the median follow-up time of 57 months, 350 children developed at least one persistent IA (GAD antibody, IA-2A, or micro insulin autoantibodies) and 84 of them progressed to T1D. We genotyped 5,164 Caucasian children for 41 non-HLA single nucleotide polymorphisms (SNPs) that achieved genome-wide significance for association with T1D in the genome-wide association scan meta-analysis conducted by the Type 1 Diabetes Genetics Consortium. In TEDDY participants carrying high-risk HLA genotypes, eight SNPs achieved significant association to development of IA using time-to-event analysis (P < 0.05), whereof four were significant after adjustment for multiple testing (P < 0.0012): rs2476601 in PTPN22 (hazard ratio [HR] 1.54 [95% CI 1.27-1.88]), rs2292239 in ERBB3 (HR 1.33 [95% CI 1.14-1.55]), rs3184504 in SH2B3 (HR 1.38 [95% CI 1.19-1.61]), and rs1004446 in INS (HR 0.77 [0.66-0.90]). These SNPs were also significantly associated with T1D in particular: rs2476601 (HR 2.42 [95% CI 1.70-3.44]). Although genes in the HLA region remain the most important genetic risk factors for T1D, other non-HLA genetic factors contribute to IA, a first step in the pathogenesis of T1D, and the progression of the disease.
Role of Type 1 Diabetes-Associated SNPs on Risk of Autoantibody Positivity in the TEDDY Study
Carina Torn,corresponding author1 David Hadley,2,3 Hye-Seung Lee,2 William Hagopian,4 ake Lernmark,1 Olli Simell,5 Marian Rewers,6 Anette Ziegler,7 Desmond Schatz,8 Beena Akolkar,9 Suna Onengut-Gumuscu,10 Wei-Min Chen,10 Jorma Toppari,5 Juha Mykkanen,5 Jorma Ilonen,11,12 Stephen S. Rich,10 Jin-Xiong She,13 Andrea K. Steck,6 Jeffrey Krischer,2 and the TEDDY Study Group*
2015 May;
