Background: DVT is a very common problem with a very serious complications like pulmonary embolism (PE) which carries a high mortality,and many other chronic and annoying complications ( like chronic DVT, post-phlebitic syndrome, and chronic venous insufficiency) ,and it has many risk factors that affect its course, severity ,and response to treatment. Objectives: Most of those risk factors are modifiable, and a better understanding of the relationships between them can be beneficial for better assessment for liable pfatients , prevention of disease, and the effectiveness of our treatment modalities. Male to female ratio was nearly equal , so we didn’t discuss the gender among other risk factors. Type of the study:A cross- sectional study.Methods: Data taken from 114 patients with DVT were analyzed by association rules mining.Immobility was the most important risk factor. Results: Smoking add more risk to immobile, post operative patient. Age per se has no effect.100% of patients with long bone fracture, were immobile. Fever occurred in one third of post operative patients who develop DVT. Conclusions: Association rules mining allow better and faster analysis of more data with an interactive powerful system, which saves time and effort, and discovers the relations among many factors to one or more than one factors. So, we use this method for analysis in this study, and we get the above mentioned relations, which are important for the future management of DVT.
Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a
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The intelligent buildings provided various incentives to get highly inefficient energy-saving caused by the non-stationary building environments. In the presence of such dynamic excitation with higher levels of nonlinearity and coupling effect of temperature and humidity, the HVAC system transitions from underdamped to overdamped indoor conditions. This led to the promotion of highly inefficient energy use and fluctuating indoor thermal comfort. To address these concerns, this study develops a novel framework based on deep clustering of lagrangian trajectories for multi-task learning (DCLTML) and adding a pre-cooling coil in the air handling unit (AHU) to alleviate a coupling issue. The proposed DCLTML exhibits great overall control and is
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Oxidative stress markers are of important diagnostic parameters for many disorders including cholelithiasis. This present study has aimed to assess the state of oxidative stress in symptomatic radiographically confirmed (Cholelithiasis) patients by measuring two parameters used as oxidative stress parameters which are serum myeloperoxidase (MPO) and superoxide dismutase (SOD). This study was carried out on 100 patient diagnosed as (Cholelithiasis) patients with 30 age and sex matched healthy controls by measuring serum (MPO) and (SOD) by ELIZA technique .Results showed significantly decrease in antioxidant enzyme(SOD) and increase in serum level of (MPO) comparing with controls.
Keywords: Cholelithiasis , Oxidative stress
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Background: Genetic factors play an important role in susceptibility to Guillain Barre' syndrome. Human leukocyte antigen (HLA) as part of immune system has a role in the disease process.Aim of the study: to assess the relationship between HLA-A alleles with Guillain Barre' syndrome (GBS) compared with a healthy control group using PCR-SSOP method.Type of the study: Cross-sectional study.Patients and methods:Patient's group consisted of 30 Iraqi Arab Muslims patients with Guillain Barre' syndrome that consulted the Neurological department in Neurosciences Hospital between January-2013 to January- 2014 were genotyped for HLA-A alleles. A control group consisted of 30 healthy volunteers among the staff of AL-Kindi College of Medicine that
... Show MoreBackground: Obesity and diabetes mellitus are the common health problems,and obesity is common cause of the insulin resistance. Aim of studv: Aim of the study is to find any correlation between obesity (insulin resistance) and type I diabetes in children. Patients and methods: This study included (40) children with type I diabetes, in addition to (40) children as control.The age of all studied groups ranged from (8-18) years.This study was attemted from Ibn AlBalady Hospital during from 20 August to 9 Novembar,2008. The subjects wrer divided into (4) groups according to their BMI:- * Obese children,diabetes,n=2O,BMI>30. * Non obese children, diabetes, n=20,BMI<25. Obese children, non diabetes, n=20,BMI>30. * Non obese children,non diabetes
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In this study, the results of x-ray diffraction methods were used to determine the Crystallite size and Lattice strain of Cu2O nanoparticles then to compare the results obtained by using variance analysis method, Scherrer method and Williamson-Hall method. The results of these methods of the same powder which is cuprous oxide, using equations during the determination the crystallite size and lattice strain, It was found that the results obtained the values of the crystallite size (28.302nm) and the lattice strain (0.03541) of the variance analysis method respectively and for the Williamson-Hall method were the results of the crystallite size (21.678nm) and lattice strain (0.00317) respectively, and Scherrer method which gives the value of c
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Abstract
Social media has thrived recently and public organizations at Thi-Qar governorate across different levels are experimenting with launching government social media (GSM) to facilitating two-way interactions between the government and its citizens. Both scholars and practitioners are focusing on understanding the key success factors related to the create of GSM. This study aimed to identify the key success factors by exploring the formation mechanism of individuals’ continuous usage intention. Through the theoretical perspective of the uses and gratifications theory. We identify the gratification factors that stimulate users’ continuance intention toward GSM. Furthermore, we draw upon the stimulus–organism–
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The two most popular models inwell-known count regression models are Poisson and negative binomial regression models. Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes the response variable Y has a Poisson distribution, and assumes the logarithm of its expected value can be modeled by a linear combination of unknown parameters. Negative binomial regression is similar to regular multiple regression except that the dependent (Y) variables an observed count that follows the negative binomial distribution. This research studies some factors affecting divorce using Poisson and negative binomial regression models. The factors are unemplo
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