The aim of this study is to find a relationship between oxidative stress and adiponectin in Iraqi patients with acromegaly. The present study included 30 patients with acromegaly disease attending at Al-Yarmuk teaching hospital , and 30 healthy individuals as a control group.The two groups with ages ranging (30-55) years. The results revealed a highly significant elevation in all parameters (GH,IGF-1 , adiponectin , malondialdehyde , and peroxynitrite ) levels in sera of patients when compared with healthy control .It can be concluded that oxidative stress (malondialdehyde and peroxynitrite ) may be valuable in detecting of endocrine diseases like acromegaly .

The aim of this study is to find a relationship between oxidative stress and adiponectin in Iraqi patients with acromegaly. The present study included 30 patients with acromegaly disease attending at Al-Yarmuk teaching hospital , and 30 healthy individuals as a control group.The two groups with ages ranging (30-55) years. The results revealed a highly significant elevation in all parameters (GH,IGF-1 , adiponectin , malondialdehyde , and peroxynitrite ) levels in sera of patients when compared with healthy control .It can be concluded that oxidative stress (malondialdehyde and peroxynitrite ) may be valuable in detecting of endocrine diseases like acromegaly .

To determine the relationship between Helicobacter pylori infection and skin disorders, sixty six patients who suffering from skin diseases include chronic urticarial (CU) and atopic dermatitis (AD) who attended at Dermatological Clinic/ Al-Numan Teaching Hospital from the beginning of October 2015 to the end of January 2016 with age (6-62) have been investigated and compared to twenty two samples of apparently healthy individuals were studied as control group. All the studied groups were subjected to measurement of antiHelicobacter pylori IgG antibodies by enzyme linked immuno sorbent assay (ELISA) and detection of 16S rRNA and CagA genes by using singleplex and multiplex PCR methods. The results of current study revealed that there was a

Transition metal complexes of Co(II) and Ni(II) with azo dye 3,5-dimethyl-2-(4-nitrophenylazo)-phenol derived from 4-nitoaniline and3,5-dimethylphenol were synthesized. Characterization of these compounds has been done on the basis of elemental analysis,electronic data, FT-IR,UV-Vis and 1 HNMR, as well as magnetic susceptibility and conductivity measurements. The nature of thecomplexes formed were studies following the mole ratio and continuous variation methods, Beer ' s law obeyed over a concentrationrange (1x10 -4 - 3x10 -4 M). High molar absorbtivity of the complex solutions were observed. From the analytical data, thestoichiomerty of the complexes has been found to be 1:2 (Metal:ligand). On the basis of physicochemical data tetrahedral

This study investigates the potential of biogas recovery from used engine oil (UEO) by co-digestion with animals’ manure, including cow dung (CD), poultry manure (PM), and cattle manure (CM). The experimental work was carried out in anaerobic biodigesters at mesophilic conditions (37°C). Two groups of biodigesters were prepared. Each group consisted of 4 digesters. UEO was the main component in the first group of biodigesters with and without inoculum, whereby a mix of UEO and petroleum refinery oily sludge (ROS) was the component in the second group of biodigesters. The results revealed that for UEO-based biodigesters, maximum biogas production was 0.98, 1.23, 1.93, and 0 ml/g VS from UEO±CD, UEO±CM, UEO±PM, and U

New complexes of the [M(Ura)(Phen)(OH2)Cl2]Cl.2H2O type, where (Ura) uracil ; (Phen) 1,10-phenanthroline hydrate; M (Cr+3 , Fe+3 and La+3) were synthesized from mix ligand and characterized . These complexes have been characterized by the elemental micro analysis, spectral (FT-IR., UV-Vis, 1HNMR, 13CNMR and Mass) and magnetic susceptibility as well the molar conductive mensuration. Cr+3, Fe+3 and La+3- complexes of six–coordinated were proposed for the insulated for three metal(III) complexes for molecular formulas following into uracil property and 1,10-phenanthroline hydrate present . The proposed molecular structure for all metal (III) complexes is octahedral geometries .The biological activity was tested of metal(III) salts, liga

New complexes of the [M(Ura)(Phen)(OH2)Cl2]Cl.2H2O type, where (Ura) uracil ; (Phen) 1,10-phenanthroline hydrate; M (Cr+3 , Fe+3 and La+3) were synthesized from mix ligand and characterized . These complexes have been characterized by the elemental micro analysis, spectral (FT-IR., UV-Vis, 1HNMR, 13CNMR and Mass) and magnetic susceptibility as well the molar conductive mensuration. Cr+3, Fe+3 and La+3- complexes of six–coordinated were proposed for the insulated for three metal(III) complexes for molecular formulas following into uracil property and 1,10-phenanthroline hydrate present . The proposed molecular structure for all metal (III) complexes is octahedral geometries .The biological activity was tested of metal(III) salts, ligands

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class