The current study was designed to explore the association between the pigments production and biofilm construction in local Pseudomonas aeruginosa isolates. Out of 143 patients suffering from burns, urinary tract infections (UTI), respiratory tract infections and cystic fibrosis obtained from previous study by Mahmood (2015), twenty two isolates  (15.38%) were identified  from (11) hospitals in Iraq, splitted  into three provinces, Baghdad, Al-Anbar and Karbala for the duration of June 2017 to April 2018.  Characterization was carried out by using microscopical, morphological and biochemical methods which showed that all these isolates belong to P. aeruginosa.  Screening of   biofilm production isolates was carried out by usi

This study utilized low-cost agricultural waste (molasses production waste powder) to extract copper ions from aqueous solutions. The present investigation explored a range of factors that influence the adsorption process, including temperature, pH, ionic strength, contact time, quantity of adsorbent, and particle size. Spectrophotometric analysis was used to determine the solution's absorbance both before and after the adsorption procedure. The Langmuir and Freundlich adsorption models were used to match the equilibrium data. The Freundlich model was determined to be the best isotherm model using the linear regression coefficient R2=0.9868. Thermodynamic parameters, including enthalpy, entropy, and Gibbs free energy, were calculate

Ultraviolet spectrophotometric studies for antibiotic (amino glycoside) derivatives including, Neomycin, Streptomycin, Gentamycin and Kanamycin with special reagents, which are benzoyl chloride; benzene sulfonyl chloride, toluenesulfonyl chloride and phthalic anhydride were made. Amino glycosides derivatives were followed through measurements of the ultraviolet absorbance (A) from which the absorptivity (ε) of the complexes was deduced and molar absorbances using Ultraviolet for products and calculate the number of reagents molecule that combine to amino glycosides.

ABSTRACT Background: The Iraqi hospital witnessed numerous violence incidents against medical staff working in emergency department and range from verbal to physical violence. High frequency of these attacks urged the Iraqi doctors for migration. Aim of study: To identify the prevalence of workplace violence against medical staff and to and study the risk factors related to work place violence. Materials and methods: A descriptive cross sectional study carried out among a sample of 300 medical

Simulated annealing (SA) has been an effective means that can address difficulties related to optimization problems. is now a common discipline for research with several productive applications such as production planning. Due to the fact that aggregate production planning (APP) is one of the most considerable problems in production planning, in this paper, we present multi-objective linear programming model for APP and optimized by . During the course of optimizing for the APP problem, it uncovered that the capability of was inadequate and its performance was substandard, particularly for a sizable controlled problem with many decision variables and plenty of constraints. Since this algorithm works sequentially then the current state wi

Introduction: Melanin is a high-molecular weight pigment produced through the oxidative polymerization of phenolic or indolic compounds and plays a perfect role in UV-light shielding, as well as in photoprotection. Among biopolymers, melanin is unique in many aspects. This study is designed to screen Production, extraction and characterizes of an extracellular melanin pigment from clinically isolated P. aeruginosa. Objective: The aim of the current study is isolation and diagnosis of P.aeruginosa using vitek-2 compact system and screening the ability to produce melanin and characterization of extracted melanin by UV-vis, FTIR, XRD and SEM. Materials and methods: the samples swab inoculated on cetrimide agar as selective media and incubated

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes