Uropathogenic E. coli (UPEC) is problematic and still the leading cause of urinary tract infections worldwide. It is developed resistance against most antibiotics. The investigation, surveillance system, and efficient strategy will facilitate selecting an appropriate treatment that could control the bacterial distribution. The present study aims to investigate the epidemiology and associated risk factors of uropathogenic E. coli and to study their antibiotic resistance patterns. 1585 midstream urine specimens were collected from symptomatic urinary tract infections (UTI) patients (225 males and 1360 females) admitted to Zakho emergency hospital, Zakho, Kurdistan Region, Iraq from January 2016 until the end of December 2018. Specimens were inoculated on blood and MacConkey plates and incubated at 37оC for 24 hours. Uropathogenic E. coli was diagnosed based on gram staining, colony characteristics, and standard biochemical tests in accordance with local standards and guidelines. All isolates were screened for their antibiogram pattern using the disk diffusion method based on the Clinical and Laboratory Standards Institute guidelines. The results showed that out of 1585 urine specimens, 1026 (64.7%) were UTIs positive with a statistically higher rate in 2016 (83.6%) (P< 0.0001). The UTIs frequency in females was significantly higher than males (P< 0.0001). Generally, the uropathogenic E. coli represented 21.1% with the highest level in 2016 (22.9%). The uropathogenic E. coli rate was higher, statistically not significant, in females (21.4%) than males (18.5%) (P=0.4946). Additionally, through the three years of study, uropathogenic E. coli (UPEC) was in high frequency in February and May 2016. The female’s age group from 20 to 39 years was the most vulnerable (46%) form total infected females, while those from 70-74 years (1%) were the least susceptible in males and females. A high percentage (80.56 %) of multidrug resistance E. coli isolates was observed with high resistance against -lactamase and macrolides antibiotics. However, higher sensitivity was towards imipenem and meropenem. In conclusion, the wrong and overuse of antibiotics will increase the resistance rate of E. coli. For this reason, proper use of available antibiotics is necessary. Also, the educational programs and periodic monitoring of antimicrobial susceptibility are essential for reducing the antibiotic resistance rate.
Over the past few decades, the health benefits are under threat as many commonly used antibiotics have become less and less effective against certain illnesses not only because many of them produce toxic reactions but also due to the emergence of drug-resistant bacteria. The clinical use of a combination of antibiotic therapy for Pseudomonas aeruginosa infections is probably more effective than monotherapy. The present study aims to estimate the antibacterial and antibiofilm activity of Conocarpus erectus leaves extracts against multi-drug resistant P. aeruginosa isolated from different hospitals in Baghdad city. One hundred fifty different clinical specimens were collected from patients from September 2021 to January 2022. All samples were
... Show MoreAg nanoparticles were prepared using Nd:YAG laser from Ag matel in distilled water using different energies laser (100 and 600) mJ using 200 pulses, and study the effect of the preparation conditions on the structural characteristics of and then study the effect of nanoparticles on the rate of killing the two types of bacteria particles (Staph and E.coli). The goal is to prepare the nanoparticle effectively used to kill bacteria.
The fast evolution of cyberattacks in the Internet of Things (IoT) area, presents new security challenges concerning Zero Day (ZD) attacks, due to the growth of both numbers and the diversity of new cyberattacks. Furthermore, Intrusion Detection System (IDSs) relying on a dataset of historical or signature‐based datasets often perform poorly in ZD detection. A new technique for detecting zero‐day (ZD) attacks in IoT‐based Conventional Spiking Neural Networks (CSNN), termed ZD‐CSNN, is proposed. The model comprises three key levels: (1) Data Pre‐processing, in this level a thorough cleaning process is applied to the CIC IoT Dataset 2023, which contains both malicious and t
This research aims to clarify the importance of an accounting information system that uses artificial intelligence to detect earnings manipulation. The research problem stems from the widespread manipulation of earning in economic entities, especially at the local level, exacerbated by the high financial and administrative corruption rates in Iraq due to fraudulent accounting practices. Since earning manipulation involves intentional fraudulent acts, it is necessary to implement preventive measures to detect and deter such practices. The main hypothesis of the research assumes that an accounting information system based on artificial intelligence cannot effectively detect the manipulation of profits in Iraqi economic entities. The researche
... Show MoreThe researcher tried to concentrate on the field study included many houses for the sake of documenting it by pictures and schemes with its history using the available data that had got from elderly whereas the rest of heritage will document in other research , in God willing .
The research confined on the studying of examples heritage houses which was some of them had built at the end of 19th century AD but, other of them had built on the beginning of 20th century . In spite of considering these buildings are important and as a sign to architect art of Jubba but, there is never full studying written about them in Western city .
The importance of this study lies on documenting sides of environmental and climatic progress which Iraq
Diyala river is the most important tributaries in Iraq, this river suffering from pollution, therefore, this research aimed to predict organic pollutants that represented by biological oxygen demand BOD, and inorganic pollutants that represented by total dissolved solids TDS for Diyala river in Iraq, the data used in this research were collected for the period from 2011-2016 for the last station in the river known as D17, before the river meeting Tigris river in Baghdad city. Analysis Neural Network ANN was used in order to find the mathematical models, the parameters used to predict BOD were seven parameters EC, Alk, Cl, K, TH, NO3, DO, after removing the less importance parameters. While the parameters that used to predict TDS were fourte
... Show MoreThe purpose of this paper is to model and forecast the white oil during the period (2012-2019) using volatility GARCH-class. After showing that squared returns of white oil have a significant long memory in the volatility, the return series based on fractional GARCH models are estimated and forecasted for the mean and volatility by quasi maximum likelihood QML as a traditional method. While the competition includes machine learning approaches using Support Vector Regression (SVR). Results showed that the best appropriate model among many other models to forecast the volatility, depending on the lowest value of Akaike information criterion and Schwartz information criterion, also the parameters must be significant. In addition, the residuals
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