The aim of the study was comparative between oxidative stress in diabetes mellitus using the measurement of some biophysical and biochemical parameters on two groups of diabetic patients, were conducted in the Al-Yarmuk Teaching Hospital,30 patients insulin dependent diabetes mellitus (IDDM) or type 1 ,their ages ranged between (15-45) and30 patients non- insulin dependent diabetes mellitus (NIDDM) or type 2,their ages ranged between (42-65).This study has been compared with 30 healthy subjects. The present study was demonstrated to evaluate the alteration in oxidative stress as measured by plasma and red blood cells Malondialdehyde (MDA) andchanges in antioxidant mechanism as measured by plasma and red blood cells Glutathione (GSH)

The infection with H. Pylori stimulates a signaling cascade that causes the generation of Cytokines and provokes Oxidative stress that is involved in the chronic inflammatory response leads to Gastric cancers. Reactive oxygen species (ROS) produce 8-Hydroxydeoxyguanosine (8-OHdG), the persistent oxidative DNA damage product. The study objective was to assess if there was a link between inflammatory cytokine levels and the presence of Oxidative DNA damage in Gastric tumor patients. In addition, evaluation of the diagnostic and prognostic value of Oxidative DNA damage and inflammatory cytokine biomarkers for Stomach cancers is being conducted. The study was accomplished on medically diagnosed Stomach cancer patients before any form of trea

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

Objective: to identify the effect of the Instruction program on the knowledge of pregnant women who suffering anemia.

Methodology: A quasi-experimental design was carried out with the application of pre- post test for the study and the control group. Purposive sample, consists of (60) pregnant women diagnosed with anemia attending four health care centers in Baquba city.

Result: The findings indicate that the level of hemoglobin is increasing post instructional program among women in the study group, in which (46.7%) of women are reveal a level of (8.1-9) g/dl that is less than normal pre instructional program and the level is increased to normal level post instructional

Background: Tuberculosis infection represents a global health problem and a great risk to Health Care Workers. Identifying individuals, particularly Health Care Workers with latent tuberculosis infection will support tuberculosis control through chemoprophylaxis and prevent cross-infection. The aim of this study was to assess the prevalence and some risk factors for latent tuberculosis infection among Health Care Workers in three tuberculosis health institutions in Baghdad.
Methods: A cross-sectional study was conducted in 2013 in Baghdad- Iraq on Health Care Workers working in the Specialized Respiratory & Chest Diseases Center (Tuberculosis institute), Ibn Zuhur Hospital for Chest Diseases and

The aim of this paper is to design suitable neural network (ANN) as an alternative accurate tool to evaluate concentration of Copper in contaminated soils. First, sixteen (4x4) soil samples were harvested from a phytoremediated contaminated site located in Baghdad city in Iraq. Second, a series of measurements were performed on the soil samples. Third, design an ANN and its performance was evaluated using a test data set and then applied to estimate the concentration of Copper. The performance of the ANN technique was compared with the traditional laboratory inspecting using the training and test data sets. The results of this study show that the ANN technique trained on experimental measurements can be successfully applied to the rapid est

This paper proposes a new structure of the hybrid neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Weight parameters of the hybrid neural structure with its serial-parallel configuration are adapted by using the Back propagation learning algorithm. The ability of the proposed hybrid neural structure for nonlinear system has achieved a fast learning with minimum number

The research aims to measure the impact of knowledge management processes individually and in total in the innovative marketing.

We depart search of a problem expressed in a number of intellectual and practical questions, the application of this research in the General Company for Vegetable Oil Industry, represented composed a sample of (63) (Director General and Deputy Director General and Director of the Department and the Division) in the company researched, it has been designed measuring instrument to collect the necessary data either statistical means they are the percentage and the arithmetic mean and standard deviation and coefficient of variation and the coefficient of simple correlation and model

Image classification can be defined as one of the most important tasks in the area of machine learning. Recently, deep neural networks, especially deep convolution networks, have participated greatly in end-to-end learning which reduce need for human designed features in the image recognition like Convolution Neural Network. It is offers the computation models which are made up of several processing layers for learning data representations with several abstraction levels. In this work, a pre-trained deep CNN is utilized according to some parameters like filter size, no of convolution, pooling, fully connected and type of activation function which includes 300 images for training and predict 100 image gender using probability measures. Re

Background: A quick and easy method was developed for extraction of DNA of eukaryotes from different samples, which are bone marrow and sperms in white mice Mus musculus strain (Balb/c).
Patients and Methods: this method using high salt buffer, Ethylene diemine tetracetec acid (EDTA), Trypsine,Sodium Dodecyl Sulfate(SDS), and urea without using Proteinase-K digestion or ultracentrifugation.
Results: This method was successful in extracting DNA from different samples in eukaryotic and this DNA is suitable for Hind III digestion.
Conclusion: Without further clean-up, the extracted DNA can be used for restriction endonuclease digestion or for numerous applications.