Relationship of clinical symptoms, comorbidities and treatment options along with disease outcomes in individuals tested for COVID-19 in Pakistan: a national survey

Background: This web-based survey is done to collect and assess data from people tested for COVID-19 with PCR in Pakistan. Methods: This 3-month study is a cross-sectional online survey, conducted by Pakistan Islamic Medical Association (PIMA), Health Research Advisory Board (HealthRAB) and National Institute of Health (NIH). Data collection was done using Google Forms. People who were tested for COVID-19 using Polymerase Chain Reaction (PCR) were included in the study. The sample size of the study was 1,537. SPSS version 22 was used for data analysis. Results: Majority of the respondents belonged to the age group 20 - 39 years. The most common symptoms found were fever 633 (41%), cough 534 (34%), generalized body aches 432 (28%) and sore throat 392 (25%). The mean COVID-19 mental health score was 3.59 (SD: 5.808, range: 0-18). Treatment with antibiotics and painkillers had a strong correlation (p-value < 0.05) with the disease outcomes. The disease outcomes had moderate correlation (p-value < 0.05) with anti-allergy, steroids, plasma and oxygen therapy, and weak correlation (p-value < 0.05) with Antiviral and Antimalarial therapy. Out of the total respondents, 561 (36.1%) were cured from COVID-19, 14 (0.9%) were expired during/after hospitalization, 15 (1%) were still infected and 962 (62%) were not infected. Conclusion: Pakistani population has a better cure rate than some of its neighboring countries. However, further research in this area is required to draw a definite conclusion.

No sign of Rotavirus co-infection in COVID-19 patients with gastrointestinal symptoms

Background and aims The main goal of the present study is to investigate the incidence of Rotavirus co-infection in COVID-19 patients. Methods and Results Fecal samples of COVID-19 patients with gastrointestinal symptoms which had positive PCR-were collected from Abadan's hospital, Iran during the period December 2020 to January 2021. Samples were analyzed by RT-PCR to determine the presence of Rotavirus. Finally, the total samples size of 37 were included in this study. The mean age of patients was 48.22 years. Abdominal pain alone was detected in 48.65% of the patients. At least one gastrointestinal symptom was detected in all of the patients. Diarrhea and fever were seen in 13.51% and 59.46% of patients, respectively. Nausea and vomiting were seen in 5.41% of the patients. RT-PCR showed no infection of Rotavirus among the patients. Conclusion Gastrointestinal symptoms related to COVID-19 are common. More studies is need among these patients groups for investigate coinfection with other fecal viral shedding carries, due to a worse prognosis and its association with disease severity. © 2023 Kamuzu University of Health Sciences and the Medical Association of Malawi.

Knowledge, attitudes and practices towards COVID-19 among populations of Islamabad and Rawalpindi during lockdown: A cross-sectional survey

In Pakistan, the first confirmed case of COVID-19 was reported on 26 February 2020, having the travel history from Iran. Islamabad and Rawalpindi have also been affected by COVID-19 epidemic. On 23 March 2020, the Government of Pakistan has declared smart lockdown all over the country including Islamabad and Rawalpindi. The aim of the study was to identify the status of the knowledge, attitudes and practices regarding COVID-19 among the general population of the twin cities (Islamabad and Rawalpindi) in Pakistan during the COVID-19 outbreak. A cross-sectional web-based survey was conducted from 5 to 19 May 2020, the week during smart lockdown in Islamabad and Rawalpindi. Demographic characteristics were compared with independent-samples t-test, one-way, or Chi-square test. Multivariable linear regression analysis was used to identify factors associated with low knowledge score. Data analyses were conducted with SPSS version 21.0. A total of 1,282 participants completed the questionnaire. Among this final sample, the average age was 30.65 years. Among the survey respondents, 680 (53%) were women, 1096 (86%) held a bachelor's degree or above, 634 (50%) were engaged with the government and private sector and 606 (47%) were married. The overall correct rate of knowledge was 70%. The majority of the respondents agreed that COVID-19 will finally be successfully controlled (59%). Most of the participants had not visited any crowded place (74%) and 95% responded that they have reduced their outdoor activities. In response to precaution measures, 86% stated that they would isolate themselves if they ever felt a fever or cough. The study findings suggest that residents of the two cities have reasonable levels of knowledge on COVID-19. However, it is necessary to launch health education and awareness campaigns to improve the knowledge and practices about COVID-19, to control its transmission. © 2022 The Author(s).

Performance Measurement of the Sustainable Supply Chain During the COVID-19 Pandemic: A real-life case study

This paper aims to introduce a framework to measure the sustainable performance of the supply chain (SC) during the COVID-19 pandemic. The SC stakeholders in this investigation are Suppliers, Production / Remanufacturing / Refurbishing Centers (Factories), Collection / Distribution Centers, Recycling / Landfill Centers, and Customers. The suggested sustainable supply chain (SSC) performance measurement included three pillars with 23 indicators. To evaluate the overall sustainability of the SC understudy, a composite index has been developed that combines all the indicators to reflect the sustainability performance of the SC. Four steps are involved in creating a composite index:1) measuring the value of indicators, 2) weighing indicators, 3) Using the normalization technique, and 4) Evaluating the overall SSC indicator. The real case in Iran is selected as an illustrative case. Our research contributions are: We suggested a novelty indicator of SSC to better show the economic, environmental, and social tradeoffs during the COVID-19 pandemic and lockdowns. We have found and measured the negative and positive impacts of COVID-19 on aspects of sustainability in SC. Based on the achieved data of the real case study, a numerical example is represented to explain how to calculate the composite index. The main contribution of this paper is the development of SSC indicators during the COVID-19 epidemic.

Hybrid Learning and Teaching in Mathematics Courses Due to COVID-19

The educational strategies have changed in Pakistan due to COVID-19. This epidemic covers almost 204 countries and territories all over the world and on 11th March 2020, WHO declared this outbreak as a pandemic. Pakistan has become one of the two hundred and four countries over the world has stopped their educational activities and closed their in-stitutions for unspecified duration. As we know that spring semester was started in the month of January in Pakistani universities, and had covered almost half path of its journey. In this situation, HEC (Higher Education Commission) announced that universities must convert educational activ-ities in hybrid teaching mode and start online teaching. We made a case study on the Learning Management System (LMS), Campus management System, and WhatsApp etc. Apply these tools on private sector engineer-ing university where two hundred and fifty students enrolled in different mathematics courses. The outcome of this study shows that quick switch-ing to the online education system was little bit difficult but successful. Though in the beginning, there were some problems for teachers and stu-dents both but overall the experience was good. Government of Pakistan will reopen the education institute in the month of September and in these days we are quite close to resume educational activities as per the regular practice. The aim of this study is to support to continue the hybrid teach-ing mode at tertiary level in the regular teaching practice specifically of Mathematics courses. In hybrid teaching mode, the ratio of face to face teaching is seventy percent and online tools and teaching strategies used thirty percent. This method of teaching and learning keep students con-nected with their teachers. Lastly, we suggest that there is still a room for betterment in LMS. The result of this study shows that students' perfor-mance in mathematics class and exam has been improved by using differ-ent application, software and gadgets. E-learning teaching and learning method will be beneficial strategy.

Extracting Feelings of People Regarding COVID-19 by Social Network Mining

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public's ideas and points of view regarding this subject. In this regard, to extract the public's point of view in various countries at the early stages of this outbreak, a dataset of Coronavirus-related tweets in the English language has been collected, which consists of more than two million tweets starting from 23 March until 23 June 2020. To this end, we first use a lexicon-based approach with the GeoNames geographic database to label each tweet with its location. Next, a method based on the recently introduced and widely cited Roberta model is proposed to analyse each tweet's sentiment. Afterwards, some analysis showing the frequency of the tweets and their sentiments is reported for each country and the world as a whole. We mainly focus on the countries with Coronavirus as a hot topic. Graph analysis shows that the frequency of the tweets for most countries is significantly correlated with the official daily statistics of COVID-19. We also discuss some other extracted knowledge that was implicit in the tweets. © 2022 World Scientific Publishing Co.

Corporate social responsibility and customer loyalty during the Covid-19 pandemic: evidence from pharmacy practice

Purpose In recent years, corporate social responsibility (CSR) has taken on a more prominent role in both large and small businesses because of its significant impact on various aspects of business performance. To date, a growing body of literature has demonstrated the mechanisms whereby CSR practices affect organizational outcomes;however, there has been little research examining how CSR practices contribute to customer loyalty within the pharmacy context. As such, this study aims to explore how CSR practices influence the loyalty of pharmacy customers, particularly in relation to the mediatory effects of customer-company identification (CCI) and customer trust. Design/methodology/approach A survey questionnaire was developed and administered to collect the required data from the pharmacy context. The resultant data were subjected to exploratory factor analysis to identify the scale dimensions, followed by multiple regression analysis to test the hypotheses. Findings Analysis of the results (n = 528) revealed that perceived CSR indirectly impacts loyalty through the mediatory effects of trust and CCI. All hypothesized effects were also confirmed via empirical testing. Originality/value The findings of this research suggest that not only are CSR activities responsive to societal concerns, but they can also promote customer identification with pharmacies and strengthen customer trust, which can, in turn, lead to long-term customer loyalty.

COVID-Net MLSys: Designing COVID-Net for the Clinical Workflow

As the COVID-19 pandemic continues to devastate globally, one promising field of research is machine learning-driven computer vision to streamline various parts of the COVID-19 clinical workflow. These machine learning methods are typically stand-alone models designed without consideration for the integration necessary for real-world application workflows. In this study, we take a machine learning and systems (MLSys) perspective to design a system for COVID-19 patient screening with the clinical workflow in mind. The COVID-Net system is comprised of the continuously evolving COVIDx dataset, COVID-Net deep neural network for COVID-19 patient detection, and COVID-Net S deep neural networks for disease severity scoring for COVID-19 positive patient cases. The deep neural networks within the COVID-Net system possess state-of-the-art performance, and are designed to be integrated within a user interface (UI) for clinical decision support with automatic report generation to assist clinicians in their treatment decisions. © 2021 IEEE.

COVID-Net US: A Tailored, Highly Efficient, Self-attention Deep Convolutional Neural Network Design for Detection of COVID-19 Patient Cases from Point-of-Care Ultrasound Imaging

The Coronavirus Disease 2019 (COVID-19) pandemic has impacted many aspects of life globally, and a critical factor in mitigating its effects is screening individuals for infections, thereby allowing for both proper treatment for those individuals as well as action to be taken to prevent further spread of the virus. Point-of-care ultrasound (POCUS) imaging has been proposed as a screening tool as it is a much cheaper and easier to apply imaging modality than others that are traditionally used for pulmonary examinations, namely chest x-ray and computed tomography. Given the scarcity of expert radiologists for interpreting POCUS examinations in many highly affected regions around the world, low-cost deep learning-driven clinical decision support solutions can have a large impact during the on-going pandemic. Motivated by this, we introduce COVID-Net US, a highly efficient, self-attention deep convolutional neural network design tailored for COVID-19 screening from lung POCUS images. Experimental results show that the proposed COVID-Net US can achieve an AUC of over 0.98 while achieving 353 × lower architectural complexity, 62 × lower computational complexity, and 14.3 × faster inference times on a Raspberry Pi. Clinical validation was also conducted, where select cases were reviewed and reported on by a practicing clinician (20 years of clinical practice) specializing in intensive care (ICU) and 15 years of expertise in POCUS interpretation. To advocate affordable healthcare and artificial intelligence for resource-constrained environments, we have made COVID-Net US open source and publicly available (https://github.com/maclean-alexander/COVID-Net-US/ ) as part of the COVID-Net open source initiative. © 2021, Crown.

A Face-Mask Detection Approach based on YOLO Applied for a New Collected Dataset

Since the beginning of the COVID-19 pandemic, many lives are in danger. According to WHO (World Health Organization)'s statements, breathing without a mask is highly dangerous in public and crowded places. Indeed, wearing masks reduces the chance of being infected, and detecting unmasked people is a waste of resources if not performed automatically. AI techniques are used to increase the detection speed of masked and unmasked faces. In this research, a novel dataset and two different methods are proposed to detect masked and unmasked faces in real-time. In the first method, an object detection model is applied to find and classify masked and unmasked faces. In the second method, a YOLO face detector spots faces (whether masked or not), and then the faces are classified into masked and unmasked categories with a novel fast yet effective CNN architecture. By the methods proposed in this paper, the accuracy of 99.5% is achieved on the newly collected dataset.

Low dose radiation therapy for covid-19: Benefit or threat?

The new coronavirus outbreak emerged at the end of 2019 worldwide in a very short period of time. The number of victims of this virus until 2020/11/17 has been 1323143. The consequences of these viruses in humans are common cold or mild illness in the upper respiratory region. In more severe cases can causes severe interstitial pneumonia and acute respiratory distress syndrome (ARDS) and Middle East respiratory syndrome (MERS) and, severe acute respiratory syndrome (SARS). So far various drugs have been prescribed and used for the treatment. However, their efficiency and their side effects for treatment of pneumonia of COVID19 are unknown and should be more investigated. Low-dose radiation (LDR) (30 to 100 cGray(Gy)) has been used historically since the early 1930s with hopeful results for pneumonia treatment and was a common treatment solution for viral pneumonia until the 1940s. As some recent studies have raised the use of LDRT for COVID19 treatment, we sought to review previous evidence oftherapeutic role of LDRT in the inflammatory diseases as well as recent recommendations about consider LDRT as the treatment method for COVID19. Based on the available evidence and the background of studies, it seems that choosing a dose 0.3-0.5 Gy in severe cases of the disease, as well as using radiation for the whole body instead of the lungs, can optimize the immune system, and optimizing the immune system will help improve COVID-19.

Clinical outcomes of moderate to severe COVID-19 patients receiving invasive vs. non-invasive ventilation

Objective: To evaluate the in-hospital outcome of moderate to severe COVID-19 patients admitted in High Dependency Unit (HDU) in relation to invasive vs. non-invasive mode of ventilation. Methods: In this study, the patients required either non-invasive [oxygen ≤10 L/min or >10 L/min through mask or nasal prongs, rebreather masks and bilevel positive airway pressure (BiPAP)] or invasive ventilation. For analysis of 30-day in hospital mortality in relation to use of different modes of oxygen, Kaplan Meier and log rank analyses were used. In the end, independent predictors of survival were determined by Cox regression analysis. Results: Invasive ventilation was required by 15.1% patients while 84.9% patients needed non-invasive ventilation. Patients with evidence of thromboembolism, high inflammatory markers and hypoxemia mainly required invasive ventilation. The 30-day in hospital mortality was 72.7% for the invasive group and 12.9% for the non-invasive group (1.8% oxygen <10 L/min, 0.9% oxygen >10 L/min, 3.6% rebreather mask and 4.5% BiPAP). The median time from hospital admission to outcome was 7 days for the invasive group and 18 days for the non-invasive group (P<0.05). Age, presence of co-morbidities, number of days requiring oxygen, rebreather, BiPAP and invasive ventilation were independent predictors of outcome. Conclusions: Invasive mechanical ventilation is associated with adverse outcomes possibly due to ventilator associated lung injury. Thus, protective non-invasive ventilation remains the necessary and safe treatment for severely hypoxic COVID-19 patients. © 2021 Asian Pacific Journal of Tropical Medicine Produced by Wolters KluwerMedknow. All rights reserved.

The role of miRNAs in COVID-19 disease

Nowadays, the SARS Coronavirus 2 (SARS-CoV-2) infection is recognized as the primary cause of mortality in humans. SARS-CoV-2 is transmitted through human-to-human contact and is asymptomatic in most patients. In addition to approved vaccines against SARS-CoV-2 infection, miRNAs may also be promising options against this new virus. miRNAs are small and noncoding RNAs 18-25 nucleotides in length that target the mRNAs to degrade them or obstruct their translation miRNAs act as an observer in cells. This study reviewed the literature on the potential role of cellular miRNAs in the SARS-CoV-2-host interplay as a therapeutic option in COVID-19 patients.

Talent Optimization in Faculty Recruitment in the Post-COVID-19 Era

This paper aims to present a multiple criteria decision model that supports faculty recruitment for business schools in the post-COVID-19 era. The greatest challenge that business schools are facing is to identify relevant criteria for faculty recruitment that can help to sustain their activities in today's highly competitive and international market. Also, these criteria should be in line with the increased focus on faculty qualification by the 2020 Association to Advance Collegiate Schools of Business (AACSB) standards. © 2020 IEEE.