Metalloendo peptidase is a neutral endopeptidase that cleaves peptides at the amino side of hydrophobic residues and inactivates several peptide hormones, including atrial natriuretic factor, giucagon, enkephalin, substance p, neurotensin, oxytocin, and bradykinin. It is also a major enzyme for the degradation of beta-amyloid. This study aimed to measure enzyme activity and compare it with other biochemical changes in sera patients with diabetic nephropathy. The study included 35 pathological samples of people with diabetic nephropathy, 24 samples from males and 11 samples from females, as well as the same number of healthy people as a comparison group of 15 males, 20 females, with the ages of both groups of patients with diabetic nephropathy and the healthy ranging between (60-35) years. When compared with healthy controls, a significant change (P 0.001) was seen in each of Age, SBP, DBP, FBS, HbA1c, HOMA, LDL, VLDL, TAG, TC, urea, creatinine, and uric acid over the period of September 2020 - December 2020. While there was a substantial drop (p 0.001) in both insulin and HDL when compared with the control group, there was no significant change (P 0.331) in BMI. As for the enzyme and cystatin-C, a significant change (P <0.001) was observed in the serum of patients with diabetic nephropathy compared with healthy subjects. Also, there was a positive correlation relationships between the enzyme and each of cystatin - C, Age, SBP, insulin, HOMA, HbA1c, FBS, DBP, creatinine, urea, and uric acid while a negative correlation between the enzyme and BMI, DBP, TC, TAG, VLDL, HDL, and LDL was observed. We can conclude from the results that the enzyme activity increases significantly in the serum of patients with diabetic nephropathy compared with healthy controls. This is due to damage to the kidney's tissues and cells, which results in the enzyme's release into the bloodstream and increased activity there. Because the enzyme is prevalent in renal tissues. It should be used as a clinical variable in patient diagnosis as an early predictor of disease.
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An experimental and numerical study has been carried out to investigate the forced convection heat transfer by clean or dusty air in a two dimensional annulus enclosure filled with porous media (glass beads) between two vertical concentric cylinders. The outer cylinder is of (82 mm) outside diameters and the inner cylinder of (27 mm) outside diameter. Under steady state condition; the inner cylinder surface is maintained at a high temperature by applying a uniform heat flux and the outer cylinder surface at an ambient temperature. The investigation covered values of input power of (6.3, 4.884, 4.04 and 3.26 W), Reynolds number values of (300, 700, 1000, 1500, and 2000) and dust ratio values (density number N) of (2, 4, 6 and 8). A comput
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Background Molluscum contagiosum is skin disease caused by the molluscum contagiosum virus (MCV) usually causing one or more small dome shaped umbilicated papules with symptoms that maybe self-resolve. MCV was once a disease primarily of children, but it has evolved to become a sexually transmitted disease in adults. It is believed to be a member of the pox virus family. In addition to the classic presentation of the disease; it can also come in different clinical forms that simulate large number of dermatolological disease.
Objective: To study different clinical forms of Molluscum contagiosum presentation in different age groups of Iraqi patients.
Method:This clinical descriptive study was performed in the outpatient department of
Objective :.
1-Find out the prevalence of alcohol and drugs addiction in two different years before and after the last
war i.e. in 2002 and in 200. 2-Study the association between the addiction
and some variables. 3-Identify the prescribed drugs and other substances that
have been abused
Methodology : A retrospective study has been conducted involving the in-patients at addiction unit-IbnRushd
psychiatric hospital in Baghdad by applying the semi-structured form based on ICD-10 criteria
of addiction and dependency with the confirmation of the specialist psychiatrist diagnosis of
dependency. Data concerning each patient admitted in the hospital was gathered to have an idea about
the problem of addiction (drugs an
The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita
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Permeability is one of the essential petrophysical properties of rocks, reflecting the rock's ability to pass fluids. It is considered the basis for building any model to predict well deliverability. Yamama formation carbonate rocks are distinguished by sedimentary cycles that separate formation into reservoir units and insulating layers, a very complex porous system caused by secondary porosity due to substitute and dissolution processes. Those factors create permeability variables and vary significantly. Three ways used for permeability calculation, the firstly was the classical method, which only related the permeability to the porosity, resulting in a weak relationship. Secondly, the flow zone indicator (FZI) was divided reservoir into
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Objective Thalassemic patients present with multiple immune abnormalities that may predispose them to oral Candida, however this has not been investigated. The aim of this study was to assess oral candidal colonization in a group of patients with β-thalassemia major both qualitatively and quantitatively. Study design The oral mycologic flora of 50 β-thalassemia major patients and 50 age- and sex-matched control subjects was assessed using the concentrated oral rinse technique. Candida species were identified using the germ tube test and the Vitek yeast identification system. Results Oral Candida was isolated from 37 patients (74%) and 28 healthy subjects (56%; P = .04). The mean candidal count was significantly higher in thalassemic patie
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Software Defined Network (SDN) is a new technology that separate the control plane from the data plane. SDN provides a choice in automation and programmability faster than traditional network. It supports the Quality of Service (QoS) for video surveillance application. One of most significant issues in video surveillance is how to find the best path for routing the packets between the source (IP cameras) and destination (monitoring center). The video surveillance system requires fast transmission and reliable delivery and high QoS. To improve the QoS and to achieve the optimal path, the SDN architecture is used in this paper. In addition, different routing algorithms are used with different steps. First, we eva
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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
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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
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