Type 2 diabetes mellitus (DM) is a group of metabolic disorder disease. The inflammatory markers act as a new risk factor for development of type 2 diabetes with a possible association with ABO/Rh blood groups. Human ABO genes are located on chromosome 9q34.1-q34.2. The aim of this study was to investigate the possible association between inflammatory markers, interleukin (IL) -18 and IL-33 in type 2DM and ABO blood groups. Sixty four patients with newly diagnosed type2 DM and control group consist of twenty healthy Iraqi individual. Laboratory test were include ABO blood groups using standard serological procedures and detection IL-18 and IL-33 in serum by ELISA kits. The Present data showed a significant increase in the serum level of IL-18 between type 2 DM patients and control, while there was no significant difference in the serum level of IL-33. At the same time both study blood groups O patients & control showed lowest level of serum IL-18, while blood group A with allele A showed less concentration of IL-33 in patients & control. Blood group O showed the highest percentage in patients & control, also Rh positive showed higher percentage. In conclusion, positive relation between IL-18 concentration and risk of type 2 DM, thus may be a predictor for newly diagnostic diabetic patient, while Serum levels of IL-33 might be a predictor marker of disease progression. No associations were found between ABO & Rh groups with type 2 DM.
The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of t
... Show MoreBackground: Pneumonia is the common lower respiratory tract infection among pediatrics, especially under five; it is a common cause of under-five children morbidity and mortality. Objectives of study: To identify nurses' perceptions toward therapeutic strategies for children with pneumonia and to find the association between their perceptions and their demographic variables. Methods: A Convenient sample of 46 nurses in Baghdad city from three hospitals) Kadhimiya Hospital for Children, Central Teaching Hospital of Pediatrics, and Child Welfare Teaching Hospital) included in the study to identify their perceptions regarding pneumonia in children. Results: The results of the study present that most of the nurses' participants in the a
... Show MoreThis paper presents the results of investigating the vibrational characteristics of oblate dish with and without framed structure . A finite element method, was applied to the dynamic analysis of oblate spheroidal shell. Different types of elements were considered in one dimension and two dimensions. It was found that the natural frequencies of oblate shells had two types of behavior against increasing the shell thickness and eccentricity, which are the membrane mode and bending mode –Since – the membrane modes natural frequencies tend to increase with the increasing the eccentricity of oblate, while the bending modes natural frequencies decrease with the increasing the eccentricity till reach the optimum eccentricity.
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In this paper the centralizing and commuting concerning skew left -derivations and skew left -derivations associated with antiautomorphism on prime and semiprime rings were studied and the commutativity of Lie ideal under certain conditions were proved.
A new distribution, the Epsilon Skew Gamma (ESΓ ) distribution, which was first introduced by Abdulah [1], is used on a near Gamma data. We first redefine the ESΓ distribution, its properties, and characteristics, and then we estimate its parameters using the maximum likelihood and moment estimators. We finally use these estimators to fit the data with the ESΓ distribution