The study aimed to find an association between Type two diabetes mellitus (T2DM) patients, obesity and the rate of nasal carriage of Staphylococcus aureus (NCSA) producer of TSST-1 in patients with T2DM compared with non-diabetic control groups. T2DM patients and control subjects were selected from outpatient of "The Specialist Center for Diseases of Endocrine and Diabetes" in Baghdad. The subjects were divided into 4 groups: Group I included 21 obese T2DM patients; Group II included 20 lean T2DM patients; Group III included 20 obese as control group and Group IV included 21 lean as control group. The study included sample with size (n= 82), male and female, with the ages ranged from 35 to 75 years, and the patients were not on any kind of anti-diabetic treatment. A total number of the nasal carriage S. aureus isolates were 38, of them 23 S. aureus (56.1 %) were isolated from the groups of patients with T2DM and 15 S.aureus isolates (38.46 %) were isolated from the control groups. Molecular method was used to detect the presence of tstH gene in S. aureus isolates indicating that the presnce of toxic shock syndrome toxin-1. The results revealed the presence of this gene in 12 (63.16%) S. aureus isolates collected from T2DM patients and 7 (36.84 %) isolates collected from control groups.
Building numerical reservoir simulation model with a view to model actual case requires enormous amount of data and information. Such modeling and simulation processes normally require lengthy time and different sets of field data and experimental tests that are usually very expensive. In addition, the availability, quality and accessibility of all necessary data are very limited, especially for the green field. The degree of complexities of such modelling increases significantly especially in the case of heterogeneous nature typically inherited in unconventional reservoirs. In this perspective, this study focuses on exploring the possibility of simplifying the numerical simulation pr
The comparison of double informative priors which are assumed for the reliability function of Pareto type I distribution. To estimate the reliability function of Pareto type I distribution by using Bayes estimation, will be used two different kind of information in the Bayes estimation; two different priors have been selected for the parameter of Pareto type I distribution . Assuming distribution of three double prior’s chi- gamma squared distribution, gamma - erlang distribution, and erlang- exponential distribution as double priors. The results of the derivaties of these estimators under the squared error loss function with two different double priors. Using the simulation technique, to compare the performance for
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