The immune infertility caused by anti-sperm antibodies (ASAs) represented about 10–20% of infertility among the couples. The ASAs interfere with sperm parameters such as sperm motility and sperm ability to penetrate cervical mucus, sperm-oocyte binding, and fertilization and embryo development. Objectives: The present study designed to assess semen analysis, presence of ASAs and DNA fragmentation index as well as correlation within these parameters in normzoospermic Iraqi subjects Patients, Materials, and Methods: A total number of Iraqi subjects (116) with range of age (20-51) years and their mean duration of infertility (4.70 ± 2.77). Seminal fluid for macroscopic and microscopic assessments done according to WHO 2010 criteria. The mixed agglutination reaction (MAR) test was used to assess their ASAs in semen (direct method), seminal plasma, and serum (indirect method) for both IgG and IgA classes of antibodies and their distribution among different parts of spermatozoa as a percentage. Results: The mean of IgG using the direct method was (23.88± 1.75) and for the indirect detection method the mean was (27.41± 2.41) with no significant difference (p > 0.05) between the two methods. While the direct method for IgA detection had a mean of 14.46 ± 1.76 and the indirect method of detection had a mean of 6.86 ± 0.39, there was a significant difference (P<0.05) between the two methods. In addition to that, the distribution of IgG and IgA detected by the direct method on sperm parts showed no significant difference except for the sperm tail, while the indirect method for IgA and IgG showed a significant difference in distribution on the sperm mid-piece. Conclusion: The correlation among sperm parameters and immunoglobulin using direct and indirect methods shows variations in significance also in the correlation between the direct and indirect methods according to distributions on the sperm surface.
The current study aims to compare between the assessments of the Rush model’s parameters to the missing and completed data in various ways of processing the missing data. To achieve the aim of the present study, the researcher followed the following steps: preparing Philip Carter test for the spatial capacity which consists of (20) items on a group of (250) sixth scientific stage students in the directorates of Baghdad Education at Al–Rusafa (1st, 2nd and 3rd) for the academic year (2018-2019). Then, the researcher relied on a single-parameter model to analyze the data. The researcher used Bilog-mg3 model to check the hypotheses, data and match them with the model. In addition
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The grey system model GM(1,1) is the model of the prediction of the time series and the basis of the grey theory. This research presents the methods for estimating parameters of the grey model GM(1,1) is the accumulative method (ACC), the exponential method (EXP), modified exponential method (Mod EXP) and the Particle Swarm Optimization method (PSO). These methods were compared based on the Mean square error (MSE) and the Mean Absolute percentage error (MAPE) as a basis comparator and the simulation method was adopted for the best of the four methods, The best method was obtained and then applied to real data. This data represents the consumption rate of two types of oils a he
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Background: Pregnancy is considered a major risk factor for development and progression of periodontal disease. There are hormonal changes in both estrogen and progesterone hormones in addition to bacterial effect and poor oral hygiene that will enhance development of periodontal disease in pregnant women. Materials and methods: Seventy subjects were enrolled in the study, the subjects with an age range (20-35) years old without any history of systemic disease. The subjects were divided into 20 non-pregnant women they represent the control group (G I), 30 pregnant women with gingivitis (GII) and 20 pregnant women with periodontitis (GIII).All periodontal parameters (plaque index, gingival index, bleeding on probing, probing pocket depth an
... Show MoreThis paper shews how to estimate the parameter of generalized exponential Rayleigh (GER) distribution by three estimation methods. The first one is maximum likelihood estimator method the second one is moment employing estimation method (MEM), the third one is rank set sampling estimator method (RSSEM)The simulation technique is used for all these estimation methods to find the parameters for generalized exponential Rayleigh distribution. Finally using the mean squares error criterion to compare between these estimation methods to find which of these methods are best to the others
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Been investigating a Pfyrus antigen hepatitis pattern J and Virus-positive rubella viral hepatitis antibodies pattern J. declaration followed by the percentage rate-positive patients of measles Allmana antibodies
Abstract
Magnetic abrasive finishing (MAF) process is one of non-traditional or advanced finishing methods which is suitable for different materials and produces high quality level of surface finish where it uses magnetic force as a machining pressure. A set of experimental tests was planned according to Taguchi orthogonal array (OA) L27 (36) with three levels and six input parameters. Experimental estimation and optimization of input parameters for MAF process for stainless steel type 316 plate work piece, six input parameters including amplitude of tooth pole, and number of cycle between teeth, current, cutting speed, working gap, and finishing time, were performed by design of experiment
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BACKGROUND: Carcinoma of urinary bladder is one of the most common malignancies worldwide and constitutes a major health problem. Multiple risk factors are associated with this tumor and its prognosis will depend on different clinicopathological parameters. Over expression of P53 protein and mutant Rb gene is associated with more aggressive clinical and histopathological features of the tumor such as advanced stage and higher grade. AIM: The immunohistochemical expression of Rb gene and P53 gene will be assessed through their protein products in transitional cell carcinoma (TCC) of the urinary bladder and then will be correlated with other well-known risk factors and prognostic parameters of bladder TCC, such as grading, tumor size, smoking
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