ABSTRACT
Virtual learning platforms are important for the future of education, especially during unprecedented times like the current covid-19 pandemic. Such learning platforms are expected to be interactive and help students communicate better with teachers and other students even virtually. This research work intends to develop a virtual learning platform in the form of a website that allows teachers to connect with students via individual and group video conferencing, create basic quizzes for the students, easily evaluate the quizzes and monitor student attendance. This website would also be useful for the students as it allows them to learn better by understanding answers for the graded quizzes. It also allows the students to view their obtained marks, check their attendance, have one-to-one video interaction with the teachers using a WEBRTC technique and Python Django framework, and, much more, all in a single platform. All the data are stored and manipulated in the MYSQL database. Thereby serving as a one-stop approach for every need of a student without having multiple websites and thereby creating a hassle out of it. The entire front end was developed entirely using web technologies like HTML, CSS, Javascript. © 2021 IEEE.
ABSTRACT
Background Since the first report of COVID-19 in December 2019, there have been significant concerns regarding the effects of the disease on pregnant and recently pregnant women. Quantifying prevalence, and identifying risk factors for severe COVID-19 in this population is key to planning and providing effective clinical maternal care. Objectives To identify rates of COVID-19 amongst pregnant and recently pregnant women and to identify maternal risk factors for severe COVID-19 and worsening clinical outcomes. Design To address the objectives using the developing evidence base we are using a 'Living systematic review' study design. Methods A systematic search of various databases and sources was conducted, including: Medline, Embase, Cochrane database, WHO COVID-19 database, CNKI, Wanfang databases, preprint servers, social media, reference lists of guidelines and included studies until the 6th of October 2020. Quality assessment of prevalence studies was done using the risk of bias tool by Hoy et al. and comparative cohorts using the Newcastle Ottawa Scale. Data extraction was completed with a pre-piloted form by two independent reviewers. The analysis is undertaken monthly and findings are regularly updated. Results are disseminated through our website: https://www.birmingham.ac.uk/research/who-collabora ting-centre/pregcov/index.aspx. The living systematic review process and collated database has given rise to distinct review questions, and the authors of this focused on prevalence and maternal risk factors. Random effects meta-analysis was used to determine prevalence of COVID-19 and the maternal risk factors associated with severe COVID-19. Results 192 studies were included. Overall, 10% (95% confidence interval 7% to 12%;73 studies, 67 271 women) of pregnant and recently pregnant women attending or admitted to hospital for any reason were diagnosed as having suspected or confirmed COVID-19. Increased maternal age (1.82, 1.27 to 2.63;I2 = 30.1%;7 studies;3561 women), high body mass index (2.37, 1.83 to 3.07;I2 = 0%;6 studies;3380 women), pre-existing maternal comorbidity (1.81, 1.49 to 2.20;I2 = 0%;3 studies;2634 women), chronic hypertension (2.0, 1.14 to 3.48;I2 = 0%;2 studies;858 women), pre-existing diabetes (2.12, 1.62 to 2.78;I2 = 0%;3 studies;3333 women), and pre-eclampsia (4.21, 1.26 to 14.0;I2 = 0%;4 studies;274 women) were associated with severe COVID-19 in pregnancy. Conclusions 1 in 10 pregnant or recently pregnant women attending or admitted to hospital are estimated to have COVID-19. Pre-existing co-morbidities, chronic hypertension, pre-eclampsia, pre-existing diabetes, high maternal age, and high BMI are risk factors for severe COVID-19.
ABSTRACT
The surgical procedures in the temporal bone have the potential to generate aerosols which could affect the health care personnel. An innovative way to avoid aerosolization during ear surgery has been highlighted in this manuscript.