The virulent genes are the key players in the ability of the bacterium to cause disease. The products of such genes that facilitate the successful colonization and survival of the bacterium in or cause damage to the host are pathogenicity determinants. This study aimed to investigate the prevalence of virulence factors (esp, agg, gelE, CylA) in E. faecalis isolated from diverse human clinical collected in Iraqi patient , as well as to assess their ability to form biofilm and to determine their haemolytic and gelatinase activities. Thirty-two isolates of bacteria Enterococcus faecalis were obtained, including 15 isolates (46.87%) of the urine, 6 isolates (18.75%) for each of the stool and uterine secretions, and 5 isolates (15.62%) of the wounds from various hospitals in Baghdad, including (Central Children's Hospital, Educational Laboratories, Ibn Al-Baladi Hospital).The isolates were confirmed to belong to the genus E.faecalis after performing morphological and biochemical microscopic examinations and for final diagnosis using the VITEC 2 system. The virulence genes viz. cylA, esp, gelE and agg were recognized in the E. faecalis, and the consequences appeared that the bacteria had eps gene in 32 isolates (100%). As for the agg gene, 32 isolates (100%) were carriers of this gene, which was responsible for these isolates' aptitude to form the biofilm. While for the gelE gene, 27 isolates (84.37%) of the isolates carried this gene, responsible for gelatinase activity whereas, the gene responsible for hemolysis cyl, there were 29 isolates (90.62%) of the total isolates. The presemce of genes in the isolates would be helpful to determine the colonization and survival of the bacterium in or causing damage to the host.
Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D
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In recent years, the evolution of the community structure in social networks has gained significant attention. Due to the rapid and continuous evolution of real-world networks over time. This makes the process of identifying communities and tracking their topology changes challenging. To tackle these challenges, it is necessary to find efficient methodologies for analyzing the behavior patterns of dynamic communities. Several previous reviews have introduced algorithms and models for community detection. However, these methods have not been very accurate in identifying communities. Moreover, none of the reviewed papers made an apparent effort to link algorithms that can accurately detect dynamic communities. This review aims to present a ta
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Background: Congenital cardiac defects have a wide spectrum of severity in infants. About 30-40% of patients with congenital cardiac defects will be symptomatic in the 1st year of life, while the diagnosis was established in 60% of patients by the 1st month of age.
Objectives: To identify the occurrence of specific types of CHD among hospitalized patients and to evaluate of growth of patients by different congenital heart lesions.
Methods: A retrospective study, done on ninety-six patients (51 male and 45 female) with congenital heart disease (CHD) admitted to central teaching hospital of pediatrics, Baghdad from 1st September 2009 to 30
Adult of dipterous flies were collected monthly from exposed animals carcasses during the period from February 2006 to January 2007 in Baghdad city. The results obtained showed that flies could be collected all over the year with variation of their population density in different seasons. The majority of the collected species were abundant during Spring and late Autumn (at lowest numbers) . In this investigation, nineteen species confined to four families were collected; these families are: Calliphoridae, Muscidae, Sarcophagidae and Fanniidae. The species Musca domestica Linn .was the most abundant followed by Chrysomya megacephala ( Fabricius ) , while Pollenia sp . and Fannia sp . were the least abundant species.
Abstract
This research aims to assess and measure the quality of health services provided by the Baghdad Educational Hospital using five dimensions of 5Qs , Developer model by (Zinelidin), 2006), the problem of the research is to try to determine the quality of health services provided at the hospital as well as the investigation of the existence of the elements of quality contribute to achieve the requirements of customers and their needs, and used for the purposes of this research two approaches, first used Applied Research to 5Qs dimension model and use the Checklist as a Key tool to collection and analysis of data and the second descriptive analytical me
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Objective(s): To Evaluate Diabetes self –management among patients in Baghdad City and to compare
between these patients self-management relative to the type of the disease.
Methodology: A descriptive design was conducted in Baghdad city, started from November 16th 2017 to the
end of May 17 th 2018 in order to evaluate Diabetes self-management. Purposive (non-probability) sample,
which was consisted of (120) patients who were diagnosed with D.M. The sample is comprised of (60) patient
with diabetes type I and (60) patient with diabetes type II. It is consisted of (60) male and (60) female. A
questionnaire is constructed for the purpose of the study. It is composed of (42) items. Reliability and validity of
the ques