The aim of the present study is to investigate whether or not xanthine oxidase (XO)–derived reactive oxygen species (ROS) may play a role in the pathogenesis of alloxan (ALX)–induced diabetes in rats using the specific XO inhibitor and hydroxyl radical scavenger, allopurinol

The involvement of oxidative stress in ALX – diabetes was assessed by the measurement of plasma and various tissues lipid peroxides levels ( using thiobarbituric acid ( TBA ) reactive substances ). Furthermore, the ability of allopurinol to influence these and other biochemical parameters, including plasma and urine ketones levels were also investigated in diabetic rats.

Rats were divided into four groups: control, untreated diabe

Abstract The Object of the study aims to identify the effectiveness of using the 7E’s learning cycle to learn movement chains on uneven bars, for this purpose, we used the method SPSS. On a sample composed (20) students on collage of physical education at the university of Baghdad Chosen as two groups experimental and control group (10) student for each group, and for data collection, we used SPSS After collecting the results and having treated them statistically, we conclude the use 7E’s learning cycle has achieved remarkable positive progress, but it has diverged between to methods, On this basis, the study recommended the necessity of applying 7E’s learning cycle strategy in learning the movement chain on uneven bar

Microorganisms establish both structural and functional construction in the marine environment, despite scientific advances, the identification of marine bacterial species is still considered as a common challenge in microbiology. Nevertheless, the present study aims to make an effort, although it seems modest, but it could establish a basis for studying the bacterial diversity in the Iraqi marine area, because of what this aspect entails of the poverty of studies related to this aspect in the studied area. The current results show the marine studied area are classified within worming area, where the average temperature ranged from 23.17 to 26.17 ºC. The recorded number of bacteria was increased with temperature increasing (0.210, 0.250

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an

     Deep learning (DL) plays a significant role in several tasks, especially classification and prediction. Classification tasks can be efficiently achieved via convolutional neural networks (CNN) with a huge dataset, while recurrent neural networks (RNN) can perform prediction tasks due to their ability to remember time series data. In this paper, three models have been proposed to certify the evaluation track for classification and prediction tasks associated with four datasets (two for each task). These models are CNN and RNN, which include two models (Long Short Term Memory (LSTM)) and GRU (Gated Recurrent Unit). Each model is employed to work consequently over the two mentioned tasks to draw a road map of deep learning mod

The implementation of technology in the provision of public services and communication to citizens, which is commonly referred to as e-government, has brought multitude of benefits, including enhanced efficiency, accessibility, and transparency. Nevertheless, this approach also presents particular security concerns, such as cyber threats, data breaches, and access control. One technology that can aid in mitigating the effects of security vulnerabilities within e-government is permissioned blockchain. This work examines the performance of the hyperledger fabric private blockchain under high transaction loads by analyzing two scenarios that involve six organizations as case studies. Several parameters, such as transaction send ra