It is becoming a public health issue to predict which expectant women will develop gestational diabetes mellitus (GDM). The goal of this case control research is to investigate the role of maternal oxidative stress levels in the first, second, and third trimesters, as well as other factors, in the development of gestational diabetes mellitus (GDM).
Methods
Between October and December 2021, 142 women participated in this research. The 101 GDM patients were split into three groups based on their gestation (T1, T2, and T3), and 41 healthy pregnant women were chosen as the comparison group. TAS and TOS levels of oxidative stress and XO were calculated using a Spectrophotometer for colorimetric techniques; fasting and random sugar levels, as well as HbA1c, were evaluated.
Results
Afamin levels were significantly greater in women with GDM in all trimester (T1=51.63 ± 3.86, T2=57.39 ± 3.17, T3=64.22 ± 3.67 ng/mL) in comparison with control (45.93 ± 2.26) with statistical significance (P ≤ 0.01). Afamin levels were higher significantly in T3>T2>T1.The levels of oxidative stress were higher significantly in women with GDM in all trimester (T1=73.79 ± 18.43, T2=81.28 ± 18.06, T3=96.70 ± 20.69) in comparison with control (37.28 ± 18.95) with statistical significance (P = 0.000). Oxidative stress levels were higher significantly in T3>T2>T1. The measured TOS and XO levels were  higher in all GDM groups compared with control (p=0.000) and high significant between patient groups (T3>T2>T1), while high significant of TAS in GDM than control but (T3
The aim of this research is to estimate the parameters of the linear regression model with errors following ARFIMA model by using wavelet method depending on maximum likelihood and approaching general least square as well as ordinary least square. We use the estimators in practical application on real data, which were the monthly data of Inflation and Dollar exchange rate obtained from the (CSO) Central Statistical organization for the period from 1/2005 to 12/2015. The results proved that (WML) was the most reliable and efficient from the other estimators, also the results provide that the changing of fractional difference parameter (d) doesn’t effect on the results.
The molluscum contagiosum virus (MCV) is a dermatotropic poxvirus. The causative agent of molluscum contagiosum (MC) is nonlethal, common and worldwide. Additionally, little inflammation is associated with MC papules. The present study aims to evaluate the immune status of MC patients by measuring the level of immunoglobulins IgG and IgM by using the radial immune diffusion assay (RIA) and the level of interleukin 18 receptor 1 (IL-18R1) by the Enzyme-linked immunosorbent assay (ELISA).The study is conducted during November 2013 to April, 2014 in outpatient clinic of Baquba Teaching Hospital. There are 75 patients, diagnosed with clinical lesions of MCV on different areas of the body, whose age is ranged between 2-50 years including 40(53.
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atrogenic atrial septal defect (IASD), post Catheter ablation during electrophysiological study simply can be assess with Echocardiography nowadays ablation consider the main line in the managements of patients with various type of arrhythmia. This study aims to de-termine the outcomes of Iatrogenic Atrial Septal Defect (IASD) six months post radiofrequency ablation (RF) procedure of left atrial arrhythmia using non-invasive Transtho-racic Echocardiography (TTE) parameters (LVEF, E/e` and ASD size) with sheath size as predictors of atrial septal defect closure. Patients and methods: A prospective study was con-ducted in Iraqi Centre for Heart Diseases included 47 patients post Electrophysiology procedure and ablation of left atrial SVT were
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... Show MoreA geographic information system (GIS) is a very effective management and analysis tool. Geographic locations rely on data. The use of artificial neural networks (ANNs) for the interpretation of natural resource data has been shown to be beneficial. Back-propagation neural networks are one of the most widespread and prevalent designs. The combination of geographic information systems with artificial neural networks provides a method for decreasing the cost of landscape change studies by shortening the time required to evaluate data. Numerous designs and kinds of ANNs have been created; the majority of them are PC-based service domains. Using the ArcGIS Network Analyst add-on, you can locate service regions around any network
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