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Dronedarone hydrochloride

$64

  • Brand : BIOFRON

  • Catalogue Number : BN-O1031

  • Specification : 98%(HPLC)

  • CAS number : 141625-93-6

  • Formula : C31H45ClN2O5S

  • Molecular Weight : 593.22

  • PUBCHEM ID : 219025

  • Volume : 5mg

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Catalogue Number

BN-O1031

Analysis Method

Specification

98%(HPLC)

Storage

-20℃

Molecular Weight

593.22

Appearance

Powder

Botanical Source

Structure Type

Category

SMILES

CCCCC1=C(C2=C(O1)C=CC(=C2)NS(=O)(=O)C)C(=O)C3=CC=C(C=C3)OCCCN(CCCC)CCCC.Cl

Synonyms

n-(2-butyl-3-(4-(3-(dibutylamino)propoxy)benzoyl)-5-benzofuranyl)methanesulfonamide monohydrochloride/N-(2-Butyl-3-{4-[3-(dibutylamino)propoxy]benzoyl}-1-benzofuran-5-yl)methanesulfonamide hydrochloride (1:1)/Methanesulfonamide, N-[2-butyl-3-[4-[3-(dibutylamino)propoxy]benzoyl]-5-benzofuranyl]-, hydrochloride (1:1)/Multaq Hydrochloride./Methanesulfonamide, N-(2-butyl-3-(4-(3-(dibutylamino)propoxy)benzoyl)-5-benzofuranyl)-, monohydrochloride/Multaq/Dronedarone (Hydrochloride)/Dronedarone hydrochloride/Dronedarone HCl

IUPAC Name

Density

Solubility

Flash Point

367.4ºC

Boiling Point

683.9ºC at 760mmHg

Melting Point

InChl

InChl Key

DWKVCQXJYURSIQ-UHFFFAOYSA-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#:141625-93-6) 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

30340884

Abstract

Introduction:
During an influenza epidemic, where early vaccination is crucial, pharmacies may be a resource to increase vaccine distribution reach and capacity.

Methods:
We utilized an agent-based model of the US and a clinical and economics outcomes model to simulate the impact of different influenza epidemics and the impact of utilizing pharmacies in addition to traditional (hospitals, clinic/physician offices, and urgent care centers) locations for vaccination for the year 2017.

Results:
For an epidemic with a reproductive rate (R0) of 1.30, adding pharmacies with typical business hours averted 11.9 million symptomatic influenza cases, 23,577 to 94,307 deaths, $1.0 billion in direct (vaccine administration and healthcare) costs, $4.2-44.4 billion in productivity losses, and $5.2-45.3 billion in overall costs (varying with mortality rate). Increasing the epidemic severity (R0 of 1.63), averted 16.0 million symptomatic influenza cases, 35,407 to 141,625 deaths, $1.9 billion in direct costs, $6.0-65.5 billion in productivity losses, and $7.8-67.3 billion in overall costs (varying with mortality rate). Extending pharmacy hours averted up to 16.5 million symptomatic influenza cases, 145,278 deaths, $1.9 billion direct costs, $4.1 billion in productivity loss, and $69.5 billion in overall costs. Adding pharmacies resulted in a cost-benefit of $4.1 to $11.5 billion, varying epidemic severity, mortality rate, pharmacy hours, location vaccination rate, and delay in the availability of the vaccine.

Conclusions:
Administering vaccines through pharmacies in addition to traditional locations in the event of an epidemic can increase vaccination coverage, mitigating up to 23.7 million symptomatic influenza cases, providing cost-savings up to $2.8 billion to third-party payers and $99.8 billion to society. Pharmacies should be considered as points of dispensing epidemic vaccines in addition to traditional settings as soon as vaccines become available.

KEYWORDS

Influenza, Epidemic, Pharmacies, Vaccination, Economic

Title

Epidemiologic and economic impact of pharmacies as vaccination locations during an influenza epidemic

Author

Sarah M. Bartsch,a,b Michael S. Taitel,c Jay V. DePasse,d Sarah N. Cox,a,b Renae L. Smith-Ray,c Patrick Wedlock,a,b Tanya G. Singh,c Susan Carr,e Sheryl S. Siegmund,a,b and Bruce Y. Leea,b,*

Publish date

2019 Nov 12.

PMID

23160806

Abstract

Protein folding and disaggregation are crucial processes for survival of cells under unfavorable conditions. A network of molecular chaperones supports these processes. Collaborative action of Hsp70 and Hsp100 proteins is an important component of this network. J-proteins/DnaJ members as co-chaperones assist Hsp70. As against 22 DnaJ sequences noted in yeast, rice genome contains 104 J-genes. Rice J-genes were systematically classified into type A (12 sequences), type B (9 sequences), and type C (83 sequences) classes and a scheme of nomenclature of these proteins is proposed. Transcript expression profiles revealed that J-proteins are possibly involved in basal cellular activities, developmental programs, and in stress. Ydj1 is the most abundant J-protein in yeast. Ydj1 deleted yeast cells are nonviable at 37 °C. Two rice ortholog proteins of yeast Ydj1 protein namely OsDjA4 and OsDjA5 successfully rescued the growth defect in mutant yeast. As Hsp70 and J-proteins work in conjunction, it emerges that rice J-proteins can partner with yeast Hsp70 proteins in functioning. It is thus shown that J-protein machine is highly conserved.

Electronic supplementary material
The online version of this article (doi:10.1007/s12192-012-0384-9) contains supplementary material, which is available to authorized users.

KEYWORDS

J-proteins, Hsp70, Rice, Transcript expression, Yeast complementation

Title

Functional relevance of J-protein family of rice (Oryza sativa)

Author

Neelam K Sarkar, Upasna Thapar, Preeti Kundnani, Priyankar Panwar, and Anil Grover

Publish date

2013 May

PMID

23619916

Abstract

This study attempts to find a prediction method of death risk in patients with acute mediastinitis (AM). There is no such tool described in available literature for this serious disease. The study comprised 37 consecutive cases of iatrogenic AM. General anamnesis and biochemical data were included. Factor analysis was used to extract the risk characteristic for the patients. The most valuable results were obtained for eight parameters, which were selected for further statistical analysis (all collected during a few hours after admission). Three factors reached eigenvalue > 1. Clinical explanations for these combined statistical factors are as follows: Factor 1—proteinic status (serum total protein, albumin, and hemoglobin level), Factor 2—inflammatory status (white blood cells, C-reactive protein, and procalcitonin), and Factor 3—general risk (age and number of coexisting diseases). Threshold values of prediction factors were estimated using statistical analysis (factor analysis, Statgraphics Centurion XVI). The final prediction result for the patients is constructed as simultaneous evaluation of all factor scores. High probability of death should be predicted if factor 1 value decreases with simultaneous increase of factors 2 and 3. The diagnostic power of the proposed method was revealed to be high [sensitivity = 100 %, specificity = 69.2 %]: Factor 1 [SNC = 95.8 %, SPC = 76.9 %]; Factor 2 [SNC = 100 %, SPC = 53.8 %]; and Factor 3 [SNC = 75 %, SPC = 76.9 %]. The described method may turn out to be a valuable prognostic tool for patients with AM.

KEYWORDS

acute mediastinitis, prediction method, inflammatory status, proteinic status

Title

Evaluation of Recovery in Iatrogenic Evoked Acute Mediatinitis

Author

Sławomir Jabłońskicorresponding author and Marcin Kozakiewicz

Publish date

2013;


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