Relation between coronavirus disease 2019 severity and short outcome in rheumatic disease Patients

Background Three years ago, the coronavirus disease 2019 (COVID-19) was the most threatening issue that the world was forced to fight. The interrelationship between COVID-19 and autoimmunity is complex and bidirectional. There was world concern about the severity of COVID infection in rheumatic patients;however, other studies have found no difference between them and the general population. Objectives To study the clinical character of rheumatic disease patients (RDP) with COVID and the outcome and compare this outcome with the nonrheumatic patients. Patients and methods This investigation was conducted retrospectively;all patients were recruited from quarantine hospitals. This study included 100 randomly selected COVID-19 patients with RDP and 200 COVID-19 patients with comorbidities other than rheumatological disease. Results The RDP presented with fever in 75%, and more than 60% developed cough and dyspnea. One-third of the patients developed anosmia and 25% lost the taste sensation;72% of the studied RDP were admitted to Ain Shams University's quarantine hospitals. Mean of the patients' hospital stay of RDP was 15.4 +/- 6.7;38% of those were admitted to the ICU. Moreover, 27% had needed mechanical ventilation, 14% developed cytokine storm, and finally, 11% of RDP died due to COVID infection;89% of RDP had been resolved from COVID infection. Conclusions Three-quarters of the recruited patients needed hospitalization. The mortality was 11%. The frequency of hospitalization and mortality of RDP were related significantly to the severity of COVID-19 infection, presence of comorbidity, and use of cyclophosphamide. Despite this, the outcomes of rheumatic patients and the general population were not significantly different.Copyright © 2023 Wolters Kluwer Medknow Publications. All rights reserved.

Co-creative service design for online businesses in post-COVID-19

Purpose: This study aims to explore the customers' behaviour towards online shopping during COVID-19 in a Muslim country and proposes a co-creative service design for online businesses (webstore) in the post-COVID-19 era. Design/methodology/approach: The study used a qualitative design for collecting the data where in-depth interviews from 18 customers were conducted following the qualitative design protocol. Due to the limitation of physical distancing, the interviews were conducted online using Microsoft teams. Findings: Based on the findings, new potential services are proposed for webstores for customer engagement, the need for expansion of product lines and safety measures for customers and staff during and post-COVID-19 era. Following the findings, a co-creative service design is proposed for webstores to revamp the business during and post-COVID-19. Originality/value: The study is unique because it has developed various unique strategic factors essential for revamping online businesses for better growth in post-COVID-19. The proposed new co-creative service design model for an online service provider is expected to create customers engagement and loyalty during and in post-COVID-19. © 2021, Emerald Publishing Limited.

The Sustainable Design Rules of Landscape

After the global developments that occurred, the most important of which is the spread of the Covid 19 pandemic, it was imperative to pay attention to outdoor spaces to preserve public health. There are important of studying the relationships between sustainable landscape design, the people, and the urban environment. The research problem revolves around a commitment to sustainable landscape design rules, which included variables in the sustainable natural space environment: environment, economy, justice, beauty, experience, and ethics. The research hypothesis is that there must be a system for the variables of sustainable spaces that determine relationships, opportunities, contradictions, and system parameters about people's health and safety, environmental services, biodiversity, and resource management. To achieve the above goals must be studied;compatibility with systems, the positive effects of globalization, the appropriate scale, and the interactive - behavioral, meaningful landscapes, the distinctive paradigm, using the alternative landscape and regenerated landscapes. © The Authors, published by EDP Sciences.

Impact of previous infection and body mass index on interferon-gamma and immunoglobulin G level generated against three types of vaccines available in Iraq

Background: Due to increased vaccination rates and the continued spread of the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) virus, many people are developing "hybrid immunity " to the virus. On the other hand, a high body mass index (BMI) has been associated with a reduced immune response to vaccination. the aims of this study was to measuring the level of immunoglobulin G (IgG) and interferon-gamma (IFN-gamma) generated against different types of vaccines in vaccinated individuals with and without previous infection and with BMI. a cross-sectional study was conducted between November 2021 and April 2022. Methods: A blood sample was obtained from 174 vaccinated persons. SARS-CoV-2 IgG levels and IFN-gamma were detected using SARS-CoV-2 IgG II quant and ELISAtechniques, respectively. statistical Analysis Used IBM SPSS version 24 software was used. Quantitative results are indicated as mean +/- standard deviation. The statistical significance level was set at P < 0.05. Results: There was no statistically significant difference in IgG and IFN-gamma mean levels between the vaccinated individual with and without confirmed previous infection. However, there was a significant difference in the case of the AstraZeneca vaccine regarding IgG levels only. The mean antibody concentration of patients with normal weight who received the Pfizer vaccine showed a slightly significant difference. Regarding the IFN-gamma level, there was a significant difference among the three types of vaccines in obese individuals. Conclusion: Previous infection with coronavirus disease-2019 seems to have no effect on IgG and IFN-gamma levels after vaccination. In addition, normal-weight individuals might possibly respond better to the vaccine and produce more antibody levels.

Health Awareness and Attitude Towards COVID-19 Pandemic Among University Students Under Healthcare Training in Al-Madinah

Background: The modern coronavirus infectious disease COVID-19 has been labeled internationally as a pandemic and is considered an emergency to the public health. Many studies were targeting university students as they play a great role after healthcare workers in relaying and spreading the awareness of COVID-19 and its preventive measures among colleagues, families, and friends. Therefore, it is of great importance to increase the knowledge and awareness about COVID-19. Objective(s): In this review article, we compare the most relevant studies that measure health awareness and attitude towards the COVID-19 pandemic. Method(s): The articles are obtained from electronic databases such as PubMed, Science Direct and Google Scholar that are published during 2020. Conclusion(s): Most studies showed high level of awareness among university students, and social media was found to be their main source of information. Among all the selected research papers, we found that the studies conducted in both the United Arab Emirates and Qassim Universities showed results that may be comparable to our topic. Copyright © 2023 Ubiquity Press. All rights reserved.

Online Learning Experience Assessment Survey During the Covid-19 Pandemic

Due to the Covid-19 epidemic, the education system worldwide has changed profoundly. This has caused many lecturers to transform face-to-face teaching and learning to online learning. In this regard, this study focuses on students' experiences in dealing with online learning. A survey method using a questionnaire is used to gather data from students, and 46 respondents answered the survey. The survey is divided into three sections, which comprise of Online Learning (Assigning Tasks), Online Learning (Quizzes), and Online Learning (Online Meetings). The results showed that the online learning application that was mostly used for assigned tasks was Google Classroom, which was 78.3%. The online learning application that is mostly used for quizzes was Kahoot, 97.8% and for online meetings was Zoom, 63.2%. Overall, most respondents, 45.7% chose a combination of online learning and face-to-face classes. © 2022 IEEE.

Evaluation of Online Field Practice Information Systems Refer to Agile Method

The company participates in fulfilling the relevance of education and industry through field practice programs. To avoid the risk of spreading the covid-19 virus and ensure the program continues, the implementation of field practice is changed from face-to-face offline activities to online activities. The purpose of the research is to develop an online field practice information system using the agile scrum method and evaluate the effect of the information system (online mentoring activities (X1) and features contained in the application (X2)) on the level of availability of field practice activities (Y) in the company. The information system development method uses the agile scrum method was chosen because it is able to produce quality software and can adapt to changes continuously. The information system is intended to better organize field practice activities for participants and companies so that all parties can benefit from field practice activities. A survey was carried out by distributing questionnaires to respondents with a respondent profile of 90% of students, 8% of employees who became supervisors for field practices, and 2% of employees who became admins of field practices. This survey is distributed via a google form with 100% of respondents filling out and returning the survey results. The questionnaire distributed contained questions about online mentoring activities (X1) and the features in the application (X2) to measure the level of availability of the implementation of field practice through the information system (Y). Based on the evaluation results, it was found that online mentoring activities (X1) and features in the application (X2) have an influence of 63.4% on the availability of field practice activities (Y) in the company. © 2022 IEEE.

Evaluation of immunoglobulin G level among subjects vaccinated with different types of COVID-19 vaccines in the karbala population, Iraq

Background: Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) vaccines represent the only way in combating the COVID-19 pandemic. Studying the differences in immune response to different types of vaccines is considered an important tool for optimizing vaccine selection and dosage required. The aim of this study was to evaluate the immunoglobulin G (IgG) concentration following vaccination with Pfizer, AstraZeneca, and Sinopharm vaccines. A cross-sectional study was conducted between November 2021 and April 2022. Methods: A blood sample was obtained from 174 vaccinated persons, IgG levels were detected using the SARS-COV-2 IgG II Quant assay on the ARCHITECT I system. Statistical analysis used IBM SPSS VERSION 24 software. Quantitative results are indicated as mean ± standard deviation. The statistical significance level was set at P < 0.05. Results: Out of 60.3%, 33.9%, and 5.7% had received Pfizer, Sinopharm, and AstraZeneca vaccines, respectively. Ninety participants were men and 84 were women with ages ranging from 18 to 70 years. IgG concentrations were higher in participants vaccinated with Pfizer's vaccine. There were significant differences among the three types of vaccine within age groups. The mean IgG concentration was higher in male participants vaccinated with Pfizer and AstraZeneca. No significant variation was observed between the first and second doses for each type of the three vaccines. The IgG concentration for a vaccination with Pfizer varied significantly among the weeks after vaccination, the maximum concentration was seen between the 6 th and 7 th weeks. Conclusions: Participants vaccinated with the Pfizer vaccine produce the highest antibody concentration as compared to other vaccines, especially in male participants. Younger participants produce higher amount of antibody response. © 2022 American Society of Civil Engineers (ASCE). All rights reserved.

Naïve-Bayes family for sentiment analysis during COVID-19 pandemic and classification tweets

This paper proposes a system to analyze the sentiments of tweeters. It is to build an accurate model to detect different emotions in a tweet. The analysis takes place through several stages (i.e., pre-processing, feature extraction, and training more than one machine learning (ML)). Naïve Bayes, Multinomial Naïve Bayes and Bernoulli Naïve Bayes were selected as supervised machine learning for sentiment analysis using a dataset of 3,057 tweets with users ranging from fear to happiness, anger, and sadness because this method is suitable for solving a problem of this type. This system was also applied to another dataset of 10,000 Tweets (5,000 positive and 5,000 negatives). This approach, consisting of three Naïve Bayes classification models, was applied to two datasets to analyze the sentiment used in them and classify each category separately. The Multinomial Naïve Bayes model outperformed the other models Where it achieved an accuracy of (91.6%) when applied to the first dataset and accuracy (87.6%) when applied to the second dataset. The researchers aim to continue this research with larger data by using other methods of sentiment analysis to predict users' thoughts about COVID-19 or any other problem and to obtain higher accuracy for the models used. © 2022 Institute of Advanced Engineering and Science. All rights reserved.

Assessment of air quality index (AQI) in Riyadh, Saudi Arabia

Air pollution is increasingly becoming a main environmental matter in the world. It can impact public health, weather, and climate. Riyadh's air pollution poses significant environmental and health risks. The study aimed to analyze Riyadh's current and future air quality by using AQI. Main six air pollutants are considered, nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM), ground level ozone (O3), and carbon dioxide (CO2), carbon monoxide (CO). Eleven air quality stations located throughout Riyadh assess the concentration of six standard pollutants daily. A comparison of air quality in Riyadh was done at the local, regional, and international levels. Furthermore, diverse factors such as meteorological seasons, working periods, and the COVID-19 period are taken into account. Industrial emissions, as well as contributions from mobile sources and wind-blown dust, appear to be the principal pollutant sources affecting Riyadh. The measured air quality components for all contaminants were found to be below standard. PM poses the greatest damage to the city's human health of all the pollutants studied. It can be found in practically all locations of air quality stations, even though CO and O3 levels in the city are not at alarming levels.

Innovative Education Technologies for 21st Century Teaching and Learning

This book highlights all aspects of innovative 21st-century education technologies and skills which can enhance the teaching and learning process on a broader spectrum, based on best practices around the globe. It offers case studies on real problems involving higher education, it includes policies that need to be adaptable to the new environments such as the role of accreditation, online learning, MOOCs, and mobile-based learning. The book covers all aspects of the digital competencies of teachers to fulfill the required needs of 21st-century classrooms and uses a new pedagogical approach suitable for educational policies. Innovative Education Technologies for 21st Teaching and Learning is the first book that addresses the teaching and learning challenges and how those challenges can be mitigated by technology which educational institutions are facing due to the COVID-19 pandemic. This book is suitable for teachers, students, instructional and course designers, policymakers, and anyone interested in 21st-century education. © 2022 selection and editorial matter, Muhammad Mujtaba Asad, Fahad Sherwani, Razali Bin Hassan, and Prathamesh Churi;individual chapters, the contributors.

Data Security Awareness in Online Learning

The Covid-19 pandemic has intensified the online learning activities, which have becoming the most crucial platforms for learning sessions. With these online learning activities, a major concern arises particularly on the security of educators and students' data. Every element in an online learning system can be a potential target of hacking or attack. Therefore, this paper examines students' awareness of data security in online learning. Google forms have been used to gather the students' feedback. Forty-two (42) students, involving the secondary school, pre-university and university students, responded to the survey questions. The results show that most of the respondents are well aware of how to keep their data safe in online learning. It is discovered that the level of awareness about data security in online learning amongst the students is commendable. The findings of this study indicate there are various ways to secure students' data especially during online learning. © 2022. All Rights Reserved.

Impact of Covid-19 Pandemic on Wellbeing of Doctors in Kashmir

After the report of irst Coronavirus case from China in December 2020, neither the infection stopped nor its reports. The infection spread so fast that within less than a quarter of year, WHO declared it as a pandemic (March 2020). This pandemic known as Covid-19 pandemic affected almost every life on this planet and caused unprecedented injury to the global economy, job market, people's livelihood, education, development and social life. The impact of this pandemic on the doctors and other healthcare practitioners was more severe as they were the frontlines and the main actors to reduce its impact. This pandemic has increased the workload and decreased the recovery opportunities of the doctors across the globe. It affected them both physically and psychologically. The impact has been more severe on the doctors of Kashmir as it is reeling under conflict from more than three decades now. The main aim of this paper, the data for which was gathered through a survey among the allopathic doctors of Kashmir, is to understand and document the impact of Covid-19 pandemic on the wellbeing of the doctors in Kashmir.

Demography-aware COVID-19 Confinement with Game Theory

In the last decades, emerging and re-emerging epidemics such as AIDS, measles, SARS, H1N1 influenza, and tuberculosis cause death to millions of people each year. In response, a large and intensive research is evolving for the design of better drugs and vaccines. However, studies warn that the new pandemics such as Coronavirus (COVID-19) and even deadly pandemics can emerge in the future. The existing confinement approaches rely on large amount of available data to determine policies. Such dependencies could cause an irreversible effect before proper strategies are developed. Furthermore, the existing approaches follow a one-size fits all approach, which might not be effective. In contrast, we develop a game-theory inspired approach that considers societal and economic impacts and formulates the epidemic control as a non-zero sum dynamic game. Further, the proposed approach considers the demographic information leading to providing a tailored solution to each demography. We explore different strategies including masking, social distancing, contact tracing, quarantining, partial-, and full-lockdowns and their combinations and present demography-aware optimal solutions to confine a pandemic with minimal history information and optimal impact on economy.

Prognostic impact of CT severity score in childhood cancer with SARS-CoV-2

Background: CT chest severity score (CTSS) is a semi-quantitative measure done to correlate the severity of the pulmonary involvement on the CT with the severity of the disease. The objectives of this study are to describe chest CT criteria and CTSS of the COVID-19 infection in pediatric oncology patients, to find a cut-off value of CTSS that can differentiate mild COVID-19 cases that can be managed at home and moderate to severe cases that need hospital care. A retrospective cohort study was conducted on 64 pediatric oncology patients with confirmed COVID-19 infection between 1 April and 30 November 2020. They were classified clinically into mild, moderate, and severe groups. CT findings were evaluated for lung involvement and CTSS was calculated and range from 0 (clear lung) to 20 (all lung lobes were affected). Results: Overall, 89% of patients had hematological malignancies and 92% were under active oncology treatment. The main CT findings were ground-glass opacity (70%) and consolidation patches (62.5%). In total, 85% of patients had bilateral lung involvement, ROC curve showed that the area under the curve of CTSS for diagnosing severe type was 0.842 (95% CI 0.737–0.948). The CTSS cut-off of 6.5 had 90.9% sensitivity and 69% specificity, with 41.7% positive predictive value (PPV) and 96.9% negative predictive value (NPV). According to the Kaplan–Meier analysis, mortality risk was higher in patients with CT score > 7 than in those with CTSS < 7. Conclusion: Pediatric oncology patients, especially those with hematological malignancies, are more vulnerable to COVID-19 infection. Chest CT severity score > 6.5 (about 35% lung involvement) can be used as a predictor of the need for hospitalization.

A Novel Method for Multivariant Pneumonia Classification based on Hybrid CNN-PCA Based Feature Extraction using Extreme Learning Machine with Chest X-Ray Images

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and time-consuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc. Author

Modifications to Hepatopancreatobiliary surgical services during COVID-19 partial lockdown in a hospital in northern Malaysia

Maintaining hepatopancreatobiliary (HPB) services during the initial phase of a pandemic in a state referral hospital for COVID-19 presents a few challenges, especially when a nationwide, government-issued partial lockdown is in enforcement. We describe the adaptations to our practice to maintain the services whilst ensuring safety of patients and staff, by postponing non-urgent clinic cases, grouping our staff to two mutually exclusive teams that work on alternate shifts and selecting HPB operative cases according to the modified Risk Urgency Decision Matrix.

A New Feature in Mysejahtera Application to Monitoring the Spread of COVID-19 Using Fog Computing

Since the imposition of the Movement Control Order (MCO) by the government of Malaysia on 18th March 2020 due to the COVID-19 pandemic, the Ministry of Health (MOH), in Malaysia introduced an application named "Mysejahtera" to track the infected or suspected cases instead of manual tracking to saving cost and time. However, due to the huge number of users and controlling the spread of the virus we proposed a new framework using Fog Computing (FC) technology, to integrate low power-consumption by adding Identification Generator (IDG), and Risk Detector Fog Computing (RDFC), and improves secure connectivity efficiently. Based on FC concept the mobile users will consider as Internet of Things (IoT) environment, and the central storage is Cloud Computing (CC). This extension ensures the application performance higher than the original version of Mysejahtera application and notifies the users of the affected or suspected cases around.