Single cell gel electrophoresis (SCGE), also called comet assay, is a rapid and sensitive technique used to analyse DNA Fragmentation Index(DFI). This study aimed to evaluate DNA damage in lymphocytes due to ionizing radiation in workers of Al-Tuwaitha nuclear site, which has been used for nuclear activities and contains a potentially significant amount of radioactive waste.The workers in this site are vulnerable to pollution due to a highly polluted environment of ionizing radiation. Blood samples were collected from 36 workerswho were divided into two groups;8 workers without protection and 28workers with protection, in addition to 30 control subjects.Alkaline comet assay was applied for analysis and the result

Influence of metal nanoparticles synthesized by microorganisms upon soil-borne microscopic fungus Aspergillus terreus K-8 was studied. It was established that the metal nanoparticles synthesized by microorganisms affect the enzymatic activity of the studied culture. Silver nanoparticles lead to a decrease in cellulase activity and completely suppress the amylase activity of the fungus, while copper nanoparticles completely inhibit the activity of both the cellulase complex and amylase. The obtained results imply that the large-scale use of silver and copper nanoparticles may disrupt biological processes in the soil and cause change in the physiological and biochemical state of soil-borne microorganisms as well.

The adsorption of Malonic acid, Succinic acid, Adipic acid, and Azelaic acid from their aqueous solutions on zinc oxide surface were investigated. The adsorption efficiency was investigated using various factors such as adsorbent amount, contact time, initial concentration, and temperature. Optimum conditions for acids removal from its aqueous solutions were found to be adsorbent dose (0.2 g), equilibrium contact time (40 minutes), initial acids concentration (0.005 M). Variation of temperature as a function of adsorption efficiency showed that increasing the temperature would result in decreasing the adsorption ability. Kinetic modeling by applying the pseudo-second order model can provide a better fit of the data with a greater correla

Disease diagnosis with computer-aided methods has been extensively studied and applied in diagnosing and monitoring of several chronic diseases. Early detection and risk assessment of breast diseases based on clinical data is helpful for doctors to make early diagnosis and monitor the disease progression. The purpose of this study is to exploit the Convolutional Neural Network (CNN) in discriminating breast MRI scans into pathological and healthy. In this study, a fully automated and efficient deep features extraction algorithm that exploits the spatial information obtained from both T2W-TSE and STIR MRI sequences to discriminate between pathological and healthy breast MRI scans. The breast MRI scans are preprocessed prior to the feature

The microbial production of substances that have the ability to inhibit the growth of other microorganisms is possibly the most common defense strategy developed in nature. Microorganisms produce a variable collection of microbial defense systems, which include antibiotics, metabolic by-products, lytic agents, bacteriocins and others. The aim of the present study was to isolate and identify Enterococcus spp. and  its most prevalent species from food samples and determine its antibacterial activity against Staphylococcus aureus isolates. A total of 50 food samples from different sources (dairy products (20 samples) and vegetables and fish (15 samples each)) were collected from different local markets in Baghdad and