Listeria spp. is one of the abortion causative agents in animals, especially in ruminants. This work aimed to detect Listeria spp. in milk and aborted fetus cows in Iraq. A total of 50 organ samples from aborted cow fetuses, including (brain, liver, and spleen), and 50 milk samples from the same aborted cows were collected from Baghdad farms, Iraq from (October 2023- March 2024). The bacteria were identified by conventional culture methods, biochemical tests, and the VITEK2 compact system, followed by molecular confirmation. The antimicrobial resistance pattern assay was performed using the disc diffusion method against eight antibiotic agents, and the L.monocytogenes virulence genes involving prfA,actA, and hylA genes were detected using t

Objective: The purpose of this study was to assess the effectiveness of Vibriophage Universiti Sains Malaysia 8 (VPUSM 8), a bacteriophage that destroys bacteria, in managing the proliferation of Vibrio cholerae, specifically the El Tor serotype, as an alternate therapeutic strategy. Methods: The study entailed subjecting water samples from Kelantan, Malaysia, to reproduce the natural circumstances that promote the growth of V. cholerae. Subsequently, the samples were contaminated with the V. cholerae O1 El Tor Inaba strain and treated using VPUSM 8. The study employed a controlled experimental design, wherein the samples were divided into three groups, each experiencing different treatment methods. Quantifying the number of colony-

Abstract  

  In this work, two algorithms of Metaheuristic algorithms were hybridized. The first is Invasive Weed Optimization algorithm (IWO) it is a numerical stochastic optimization algorithm and the second is Whale Optimization Algorithm (WOA) it is an algorithm based on the intelligence of swarms and community intelligence. Invasive Weed Optimization Algorithm (IWO) is an algorithm inspired by nature and specifically from the colonizing weeds behavior of weeds, first proposed in 2006 by Mehrabian and Lucas. Due to their strength and adaptability, weeds pose a serious threat to cultivated plants, making them a threat to the cultivation process. The behavior of these weeds has been simulated and used in Invas

The present study was carried out to determine the bacterial isolates and study their antimicrobial susceptibility in case of burned wound infections. 70 burn wound swabs were taken from patients, who presented invasive burn wound infection from both sex and average age of 3-58 years, admitted to teaching medical Al- Kendi hospital from October 2007 to June 2008. Pseudomonas aeruginosa was found to be the most common isolate (48.9%) followed by Staphylococcus aureus (24.4%), Citrobacter braakii (13.3%), Enterobacter spp. (11.1%), Coagulase-negative Staphylococci (11.1%), Proteus vulgaris (6.66%), Corynebacterium spp. (6.66%), Micrococcus (6.66%), Proteus mirabilis (4.44%), Enterococcus faecalis (4.44%), E.coli (4.44%), Klebsiella spp. (2.22

Pseudomonas aeruginosa is the most common opportunistic pathogen causing morbidity and mortality in hospitalized patients due to its multiple resistance mechanisms. Therefore, as a therapeutic option becomes restricted, the search for a new agent is a preference. So P. aeruginosa is an extremely versatile Gram-negative bacterium capable of thriving in a broad spectrum of environments, and this performs main problems to workers in the field of health. One hundred and fifty samples were collected from different sources from Baghdad hospitals, divided into two main groups: clinical (100) specimens and (50) samples as an environmental, collected from October 2019 to the March 2020. All of these samples were cultured by specific and differential

Background: Neonatal septicemia is a major health problem in developing countries furthermore data on bacteriological profile in early onst sepses (EOS) and late neonatal sepsis (LOS) are lacking in context of  continuous change in bacteriological profile and increasing resistant strains. Objectives: The study done to determine the pattern of organisms implicated in neonatal septicemia in a neonatal care unit and to measure the degree of bacterial resistance to some antibiotics.

Type of the study : cross –sectional study.

Methods: Confirmed cases of neonatal septicemia admitted at Al-Alwyia pediatric teaching hospital for the period from January 20

In this research, titanium dioxide nanoparticles (TiO₂ NPs) were prepared through the sol-gel process at an acidic medium (pH3).TiO₂ nanoparticles were prepared from titanium trichloride (TiCl₃) as a precursor with Ammonium hydroxide (NH₄OH) with 1:3 ratio at 50 °C. The resulting gel was dried at 70 °C to obtain the Nanocrystalline powder. The powder from the drying process was treated thermally at temperatures 500 °C and 700 °C. The crystalline structure, surface morphology, and particle size were studied by using X-ray diffraction (XRD), Atomic Force Microscopy (AFM), and Scanning Electron Microscope (SEM). The results showed (anatase) phase of titanium dioxide with the average grain size

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Decision Tre

Many academics have concentrated on applying machine learning to retrieve information from databases to enable researchers to perform better. A difficult issue in prediction models is the selection of practical strategies that yield satisfactory forecast accuracy. Traditional software testing techniques have been extended to testing machine learning systems; however, they are insufficient for the latter because of the diversity of problems that machine learning systems create. Hence, the proposed methodologies were used to predict flight prices. A variety of artificial intelligence algorithms are used to attain the required, such as Bayesian modeling techniques such as Stochastic Gradient Descent (SGD), Adaptive boosting (ADA), Deci