Pseudomonas aeruginosa has variety of virulence factors that contribute to its pathogenicity. Therefore, rapid detection with high accuracy and specificity is very important in the control of this pathogenic bacterium. To evaluate the accuracy and specificity of Polymerase Chain Reaction (PCR) assay, ETA and gyrB genes were targeted to detect pathogenic strains of P. aeruginosa. Seventy swab samples were taken from patients with infected wounds and burns in two hospitals in Erbil and Koya cities in Iraq. The isolates were traditionally identified using phenotypic methods, and DNA was extracted from the positive samples, to apply PCR using the species specific primers targeting ETA, the gene encoding for exotoxin A, and gyrB gene. The results of this study indicate that 100% of P. aeruginosa isolates harbored the gyrB gene, whereas 74% of these isolates harbored ETA gene. However, the specificity of PCR for detection of P. aeruginosa based on the both genes was 100%, since no amplified product obtained using DNA extracted from other bacterial species. Hence by considering the importance of rapid detection of this bacterium due to the presence of problems in biochemical methods, PCR targeting multiple virulence genes is suggested in identification of pathogenic strains of P. aeruginosa isolated from some infections which should speed diagnosis of an antimicrobial therapy.
With the escalation of cybercriminal activities, the demand for forensic investigations into these crimeshas grown significantly. However, the concept of systematic pre-preparation for potential forensicexaminations during the software design phase, known as forensic readiness, has only recently gainedattention. Against the backdrop of surging urban crime rates, this study aims to conduct a rigorous andprecise analysis and forecast of crime rates in Los Angeles, employing advanced Artificial Intelligence(AI) technologies. This research amalgamates diverse datasets encompassing crime history, varioussocio-economic indicators, and geographical locations to attain a comprehensive understanding of howcrimes manifest within the city. Lev
... Show MoreA total of 200 clinical samples included Burns and Wounds infections were collected from Baghdad Governorate. Results showed that rate all isolates of P. mirabilis was 31(15.5%) and rate of Burns infections was 14 (45%) and rate of wounds infection 17 (55%). Where was diagnostic based on conventional biochemical tests and confirmed by the Vitek-2 Compact system and the specific primer of the16SrRNA gene, the ability of bacterial isolates to biofilm formation to be studied. It's considered an important virulence factor in Incidence of diseases and play important role in increasing resistance to antibiotic of encased bacteria, by two methods Congo Red Agar method and Microtiter Plate method. The Congo Red Agar method showed that most isolates
... Show MoreDiarrhea is an important public health problem worldwide, several causes associated with diarrhea especially in population live under poverty and unsafe water use. Different methods are available and use in diagnosis. This study was carried out to compare of various techniques for Giardia lamblia detection and study the association with E coil and Shigella in patients with diarrhea. A total of 100 children with diarrhoea were enrolled into the study, 57 were males and 43 were females, aged from 2 months -16 years were attendant to AL-Imamin AL-Kadhimin Medical City, during the period from May 2014 to February 2015. Stool samples were collected and analysed for Giardia lamblia
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show MoreDuring COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve
... Show More<span>Distributed denial-of-service (DDoS) attack is bluster to network security that purpose at exhausted the networks with malicious traffic. Although several techniques have been designed for DDoS attack detection, intrusion detection system (IDS) It has a great role in protecting the network system and has the ability to collect and analyze data from various network sources to discover any unauthorized access. The goal of IDS is to detect malicious traffic and defend the system against any fraudulent activity or illegal traffic. Therefore, IDS monitors outgoing and incoming network traffic. This paper contains a based intrusion detection system for DDoS attack, and has the ability to detect the attack intelligently, dynami
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