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[Interactions in between cells along with biomaterials within cells design: a review].
The novel coronavirus disease 2019 (COVID-19) was firstly reported from Wuhan city of China and found as a highly contagious, transmittable and pathogenic viral infection. The World Health Organization declared COVID-19 as a pandemic since its emergence from China. The RNA-dependent RNA polymerase (nsp-12) is a complex with nsp-7 and nsp-8 cofactors and is a major constituent of viral replication and RNA synthesis machinery. In the current study, the RdRp of the virus was selected as a receptor protein for computational drug discovery. Computational homology modelling was done in order to find the hidden secondary structures and structural assessment of the viral protein to target them via antiviral drugs. The study was based on molecular docking of different phytochemicals to check their potentials against viral replicative proteins. Out of 200 ligands used in this study from different plants, the best ten were selected based on drug discovery parameters such as S-score, ligand interactions, hydrophobic interactions and druglikeness. The ten best selected ligands were found to be verbenalin, epigallocatechin, swertisin, nobiletin, pinoresinol, caftaric acid, hesperetin, islandicin, neochlorogenic acid and sesamin that exploit the potency as antagonists of viral protein. Among binding interactions of all ligands, Arg339 centred as the main interacting residue among almost all the ligands. Till now, many antiviral agents have shown potency in only mild cases of SARS-CoV-2, but no effective drug has been found for critical pulmonary cases. In clinical trials, many broad-spectrum antiviral agents have been still in trial periods of testing against SARS-CoV-2. Till date, no effective drug or vaccine has been validated with significant efficacy and potency against the SARS-CoV-2; therefore, there is an urgent need to design effective vaccine against nCoV-19 infection.Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.Both bilingualism and attention contribute to the development of executive functioning (EF), with higher levels of both leading to better outcomes. The present study treats bilingualism and attention as continuous variables to investigate their impact on EF. Eighty-two 9-year-olds who were attending a French school in an anglophone community completed a flanker task. Children's progress in French represented their level of bilingualism, and attention was assessed through a standard standardized instrument. Degree of bilingualism and degree of attention were both positively related to performance, but exposure to a third language in the home did not further affect outcomes.Successful implementation and use of learning management systems (LMSs) have become a critical challenge for many higher education institutes during the Covid-19 pandemic. Although LMSs with lots of features were developed for universities, the success of those systems is highly related to a detailed understanding of challenges and factors influencing the use of the systems among their users. HELMS (Higher Education Learning Management System) is a countrywide LMS used for teaching and learning during the quarantine period caused by covid-19 in Afghanistan universities. As it was the first experience of Afghan universities in using the learning management systems during the pandemic, challenges were expected to appear. No previous research has been conducted on either studying the challenges of using the HELMS or investigating the factors influencing the use of HELMS during the Covid-19 pandemic in Afghanistan. Hence, there was no unified view of the potential challenges of using HELMS and factors influencing the use of the HELMS among the researchers. This research aims to investigate the challenges that face the use of HELMS and explore the factors influencing the use of HELMS among both lecturers and students. click here This study employed a qualitative research method by conducting semi-structured interviews with 100 participants including university management, lecturers, and students. Thematic analysis was used as a method for the analysis of qualitative data. The findings of this research will help policymakers, researchers, and practitioners in public and private universities to grasp knowledge on the successful implementation and use of LMSs during covid-19 and afterward.COVID-19 has impacted educational processes in most countries some educational institutions have closed, while others, particularly in higher education, have converted to online learning systems, due to the advantages offered by information technologies. This study analyzes the critical factors influencing students' satisfaction with their continuing use of online learning management systems in higher education during the COVID-19 pandemic. Through the integration of social cognitive theory, expectation confirmation theory, and DeLone and McLean's IS success model, a survey was conducted of 181 UK students who engaged with learning management systems. It was found that, during the pandemic, service quality did not influence students' satisfaction, although both information quality and self-efficacy had significant impacts on satisfaction. In addition, the results revealed that neither self-efficacy nor satisfaction impacted personal outcome expectations, although prior experience and social influence did. The findings have practical implications for education developers, policymakers, and practitioners seeking to develop effective strategies for and improve the use of learning management systems during the pandemic.
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