Prevalence and Characterization of Some Colibactin Genes in Clinical Enterobacteriaceae isolates from Iraqi Patients

                افراد العائلة المعوية تمتلك مجموعة من الجينات تدعى Polyketide synthase (pks). هذه المجموعة من الجينات تكون مسؤولة عن تصنيع الذيفان الذي يطلق عليه Colibactin والذي له دور مهم في استحثاث تكسر اشرطة الدنا المزدوجة DNA والذي يؤدي الى استحثاث ورم او ما يعرف بسرطان القولون، احد عشر من اصل ثمانية وثمانين عزلة  بكتيرية وتمثل (12.5%) كانت قد توزعت 7(8%) عزلة تعود لبكتربا E. coli، 2(2.25%) عزلة تعود لبكتريا K. pneumonia و 2(2.25%) تعود لبكتريا E. aerogenes كانت حاملة لجينات الكولبكتين قيد الدراسة. تم اختبار لتأثير السمي الخلوي لعزلتين كانت موجبة للجينات قيد الدراسة وهي E. coli and E. aerogenes تجاه خط الخلايا السرطاني المعروف بـ HeLa  بينت النائج انخفاض عدد الخلايا وحصول استطالة في انوية الخلايامقارنة بالخلايا الغير معاملة. اظهرت النتائج حصول تغيرات نسيجية في الخلايا بأستخدام صبغة AO/EBr تم ملاحظتها بأستخدام المجهر الفلورسيني: بعض هذه التغيرات تم ملاحظتها في لون كروماتين النواة ومصحوب بتكثف الدنا النووي وكذلك حصول تكسر في النوية، خلايا الـ HeLa التي ظهرت بلون اخضر ولم تحصل فيها اي تغيرات في لون المادة الكروماتينية هي خلايا حية ولم تتم معاملتها مع البكتريا الحاملة للجينات قيد الدراسة، بينما الخلايا المعاملة مع خلايا بكتيرية حاملة للجينات ظهرت انويتها بلون برتقالي داكن وهي خلايا ميتة. يستنتج من ذلك ان عزلات البكتريا المعوية المعزولة من مرضى عراقيين يمكنها ان تفرز مواد سامة (ذيفان الكولبكتين) يمكنها قتل قتل الخلايا السرطانية نوع HeLa وهذا ناتج عن تغيرات حصلت في انوية الخلايا المعرضة للبكتريا وكان واضح في كثافة وتكسر المادة الوراثية للخلايا قيد الدراسة. The members of the family of Eentrobacteriaceae harbour a gene cluster called polyketide synthase (pks) island. This cluster is responsible for the synthesis of the genotoxin colibactin that might have an important role in the induction of double-strand DNA breaks, leading to promote human colorectal cancer (CRC). Eleven out of the eighty eight isolates (12.5%) were pks+, distributed as 7 (8%) isolates of E. coli, 2 (2.25%) of K. pneumoniae and 2 (2.25%) of E. aerogenes. The cytotoxic effects of selected pks+ isolates (E. coli and E. aerogenes) on HeLa cells were represented by decreasing cell numbers and enlarged cell nuclei in comparison to the untreated cells. Cytological changes were observed when the infected HeLa cells cultures were stained with AO/EBr and visualized under fluorescent microscope. Some changes that happened in the color of the nuclear chromatin were accompanied by DNA condensation and degradation and fragmentation of nuclei. HeLa cells with green unchanged nuclear chromatin were alive while those with orange-dark and bright red nuclei were dead. It was concluded that a proportion of the Entreobacteriaceae isolates from Iraqi patients was pks+, which exerted cytotoxic effects upon using them to kill HeLa cells. In this study the microscopic observation of the cell morphology reveals the cellular response to the genotoxic insult, with reduced numbers, striking giant cells phenotype (megalocytosis) and fragmentation of nuclei due to the cell cycle arrest and cellular senescenc

Social Media Adoption in Education: A Systematic Review of Disciplines, Applications, and Influential Factors

Employing social media applications for instructional activities has become a trendy topic, with a relatively large number of studies published yearly. However, identifying the factors influencing social media adoption and having a clear taxonomy that guides further research are neglected in the extant literature. Understanding which specific disciplines use specific social media applications also requires further investigations. Therefore, this systematic review retrieved and analyzed 713 studies published on social media adoption that relied on “Technology Acceptance Model” (TAM) as the basic model. After critically evaluating the collected publications against the inclusion and exclusion criteria, 45 studies were eventually shortlisted for in-depth analysis. The main findings indicated that most of the analyzed studies collected data from students enrolled in the business discipline. Facebook is found to be the primary application used in most of the analyzed studies for educational purposes. The influential factors affecting social media adoption were classified into three distinct groups, including external factors, behavioral intention antecedents, and moderating factors. We have also classified the behavioral intention antecedents into three distinct clusters, including user, social, and technology aspects. The majority of the existing literature was conducted in individualistic settings with limited exposure to collectivistic societies. This review is believed to provide a quick grasp of the disciplines, applications, and determinants influencing social media adoption in higher education

SYNTHESIS, CHARACTERIZATION AND BIOLOGICAL ACTIVITIES STUDY OF SOME AZO DERIVATIEVES OF AMINOTHIADIAZOLE DERIVED FROM NICOTINIC AND ISONICOTINIC ACIDS

Abstract In this study we synthesized the new compounds containing bis-1,3,4-thiadiazole 3(A-D) n through many eaction steps (cyclization, diazotiazation and etherification respectively). The compounds have been characterized by melting point, FTIR and 1 HNMR data. All the synthesized compounds have been evaluated in vitro for their antimicrobial activities against several microbes like: Escherichia coli, Klebsiellia pneumonia, Pseudomonas aerugenosa, Serratia marscens and Staphylococcus aureus and the results showed that some of these compounds have very good antibacterial activity

A systematic rank of smart training environment applications with motor imagery brain-computer interface

Brain-Computer Interface (BCI) research is considered one of the significant interdisciplinary fields. It assists people with severe motor disabilities to recover and improve their motor actions through rehabilitation sessions using Motor Imagery (MI) based BCI systems. Several smart criteria, such as virtual reality, plays a significant role in training people for motor recovery in a virtual environment. Accordingly, Smart Training Environments (STEs) based on virtual reality for MI-BCI users provide a safe environment. They are cost-effective for real-life conditions and scenarios with severe motor disabilities. Fundamentally, the literature presents a lack of comparison of the STE applications considering the smart and effective criteria of the developed applications. Accordingly, three key issues faced the comparison process: importance, multi-evaluation criteria, and data variation, which falls under complex Multi-Criteria Decision Making (MCDM). Performance issues increased comparison complexity caused by the rapidly changing market demands of the MI-BCI. Therefore, this study developed two methodology phases for evaluating and benchmarking the STE applications for the MI-BCI community; making effective decisions is vital. In the first phase, formulate the STE Decision Matrix (DM) based on two main dimensions: the evaluation of ten smart criteria of STE and the alternatives (27 STE applications) developed in the literature for MI-BCI. In the second phase, integration methods of MCDM have been formulated: Analytic Hierarchy Process (AHP) for weighting the ten smart criteria and Fuzzy Decision by Opinion Score Method (FDOSM) for benchmarking STE applications based on constructed AHP weights. The evaluation results show importunity in the obtained weights among the ten STE criteria to distinguish the greatest and lowest important weights. Through the benchmarking performance, FDOSM processes prioritized all STE applications. The ranking results were objectively validated based on five groups of alternatives, and the results were systematically ranked. Finally, this study argued three important summary points concerning the STE dataset, formulated a DM of STE applications, and smart criteria for STE applications to support the MI-BCI community and market. Developing the appropriate STE application for MI-BCI is a better choice to support a large BCI community by identifying the ten smart criteria and considering the presented methodology to establish a robust, practical, cost-efficient, and reliable BCI system

A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Methodology

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n=21/30). The second category, recurrent neural network (RNN), accounts for 10% (n=3/30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n=30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n=1/30). The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided

Systematic review of training environments with motor imagery brain–computer interface: Coherent taxonomy, open issues and recommendation pathway solution

The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes

Telehealth utilization during the Covid-19 pandemic: A systematic review

During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respect to (i) control (n = 25), (ii) technology (n = 14) and (iii) medical procedure (n = 47). Since the beginning of the pandemic, telehealth has been presented in diverse cases. However, it still warrants further attention. Regardless of category, the articles focused on the challenges which hinder the maximisation of telehealth in such times and how to address them. With the rapid increase in the utilization of telehealth in different specialised hospitals and clinics, a potential framework which reflects the authors’ implications of the future application and opportunities of telehealth has been established. This article improves our understanding and reveals the full potential of telehealth during these difficult times and beyond