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Repeated public health menace caused by the pathogenic coronaviruses, including the present COVID-19 caused by the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2), has had devastating aftereffects, and an intense need for a promising solution has developed. Currently, reverse transcription polymerase chain reaction (RT-PCR) is being extensively utilized for detecting the virus from biological samples. However, it has certain limitations and fails to provide accurate and reliable results. Consequently, simple, portable, and pointof- care testing enabled biosensors have turned up as the most efficient and sustainable diagnostic tool. This review provides a brief introduction about the present global scenario due to the ongoing pandemic and concise information regarding the morphological details of coronaviruses. Thereafter, a summarized data is presented regarding the contemporary biosensing platforms fabricated to specifically identify fatal coronaviruses with particular emphasis towards surface plasmon resonance (SPR)-based biosensor, field-effect transistor (FET)-based biosensor, colorimetric sensors, fluorescence-based sensors, and electrochemical (EC) immunosensors. A comparative analysis of the sensors is also presented along with a few future perspectives that can aid the development of smart and futuristic sensors. This review is expected to provide details to researchers about the ongoing biosensor-related experimentations and encourage them to develop innovative detection devices to manage the current pandemic. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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The main objective of this study was to develop important guidelines and specifications for a vigorous and effective on-line learning specific to the civil engineering programme using a quantitative survey and analysis. A comprehensive survey was distributed during the spring semester 2021-2022 to civil engineering students at Prince Mohammad Bin Fahd University, Al Khobar, Kingdom of Saudi Arabia, with the primary purpose of establishing the main requirements and recommendations for an efficient and compelling on-line learning in the programme. This study aimed to point out the variables influencing the quality of on-line learning, including administrative support, course content, course design, instructor characteristics, learner characteristics, social support and technological support. Although the Covid-19 pandemic is nearly over and students are mostly back to the traditional face-to-face learning, the authors expect that the outcomes and recommendations from this study will complement existing virtual learning guidelines and will guide future educational policy makers developing hybrid learning that can optimise efficient on-line and face-to-face modes. © 2023 WIETE
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This study demonstrates how significantly the COVID-19 pandemic has affected Bangladesh's formal education system. Despite the fact that Bangladesh introduced Information and Communication Technology (ICT) in educational practices before the pandemic, effective ICT deployment could not be integrated at all levels. Even though online classes and other print- and electronic media-based approaches evolved into the "new normal" in an effort to address the difficulties brought on by the pandemic, both teachers and students have faced and continue to encounter many challenges. A convergent parallel mixed method design was followed for this study. Data were collected from 205 Bangladeshi students and 50 Bangladeshi teachers through semi-structured questionnaires. In addition, 11 parent interviews and 12 Key Informant Interviews were conducted. According to the findings, the lack of proper training for teachers, poor socio-economic conditions, lack of internet availability and speed, the shortage of ICT equipment, students not being technologically exposed, and the disruption of electricity are major issues hindering the fruitful implementation of online education. Creating an ICT framework, providing subsidised internet for students and instructors for instructive purposes, promoting alternative means to carry on formal education, dedicating instruction hours in TV and radio channels, ensuring proper IT infrastructure and tools, and taking initiatives to promote the learning management system can play a significant role in creating the ideal environment to promote online education. In summary, this study suggests a holistic framework to continue formal teaching-learning in different levels of education to achieve sustainable development goals (SDG) without any disruption in emergency contexts such as the COVID-19 pandemic.
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The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the 'Wheel of Life.' Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on 'Wheel of life' concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation.
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Background: During COVID-19 pandemic, the pattern of hospital admissions for acute ST-elevation myocardial infarction (STEMI) has been changing, and increased mortality and morbidity is being noted in these patients. Patient may present with acute myocardial infarction, myocarditis simulating a presentation like STEMI, coronary spasm, myocardial injury not fulfilling the criteria of type 1 & type 2 acute MI and cardiomyopathy. In this study we have tried to determine some important differences among the patients presented with STEMI during The COVID-19 pandemic versus non-COVID-19 era. Method(s): This prospective observational study was carried out in National Heart Foundation Hospital & Research Institute from 8thMarch,2019 to 7th March, 2021. Total 2531 patients were included. The study populations were divided into two groups. Group I: Acute STEMI patients presenting during pre COVID period (n=1385). Group II: Acute STEMI patients presenting during COVID period (n=1146). COVID period was calculated from 8th march, 2020 onward as first case of COVID -19 was detected on that day in Bangladesh. All patients presented with Acute STEMI was included in the study however NSTEMI-ACS, Unstable angina were excluded from the study. Result(s): Male was predominant in both groups. Regarding risk factors Hypertension, Obesity and family H/O of CAD was statistically significant (p<0.05). Acute STEMI patients presented lately during COVID-19 period probably due to lock down& lack of transport facility. Regarding coronary artery disease severity, vessel score was more during COVID period. SV-CAD were more during pre COVID period while DV-CAD & TV-CAD were more during COVID period. Gensini score was also calculated among the study populations, it was 57.21+/-28.42 and 63.16+/- 40.43respectively in group I and group I, which was statistically significant. Regarding treatment options of the patients, primary PCI was less during COVID period, however medical management, elective PCI and Thrombolysis were more during COVID era. Regarding in hospital outcome, acute LVF, cardiogenic shock were more during COVID period which were also statistically significant. [Formula presented] [Formula presented] Conclusion(s): During COVID -19, STEMI patients presented lately in comparison to pre COVID period. Coronary artery Disease were more severe during COVID period as evaluated by Vessel Score & Gensini Score. During COVID -19 period majority of patients got medical management& routine PCI were done more in comparison to primary PCI. In Hospital outcome of STEMI was worse during COVID-19 period in terms of acute LVF & cardiogenic shock. [Formula presented] [Formula presented] [Formula presented]Copyright © 2023
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The clinical outcomes in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection include asymptomatic disease or mild disease with influenza-like symptoms or severe disease condition following death by pneumonia and acute respiratory distress syndrome (ARDS). The current mRNA- and vector-based vaccines successfully addressed the antigenic challenges of the parental SARS-CoV-2 strain. However, recent concerns are being raised against some SARS-CoV-2 variants, which have the potential to escape natural immunity and vaccine-induced immune recognition partially, leading to a possible increase in transmissibility and disease severity. The coronavirus disease-19 (COVID-19)-induced rapid changes in human immune profiles might be instigating the evolution of SARS-CoV-2 with a higher propensity. Therefore, we require critical surveillance on the genomic sequence and structural conformation of the evolving variants and phenotypic impacts of the accumulating mutations on the host-immune response for possible updates in the booster vaccine sequence, if required. Here, we will highlight the role of accumulating mutations in SARS-CoV-2 genomic sequences leading to the host-immune escape by regulating the T cell- and B cell-mediated responses in infected, unvaccinated, and vaccinated individuals. © 2023 Elsevier Inc. All rights reserved.
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The worldwide spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) famously known as the COVID-19 pandemic is making shocking sceneries all over the globe. The COVID-19 virus can survive on the surface of several materials and infect the person that comes in contact with that surface during the virus lifespan. Therefore, it is recommended to ensure that bacteria, microorganisms, and viruses, including COVID-19, are eliminated from food surfaces. In this work, a study has been conducted based on COMSOL Multiphysics to evaluate the temperature at micro-droplet surfaces located at different positions and locations in microwave ovens, which are used globally to reheat food. It was found that the micro-droplet surface temperature within two and a half minutes is enough to kill the bacterial and viral microorganisms on the droplet surface. As COVID cannot tolerate 70 degreeC temperature, within this time, it can be eliminated from the food surface. The time requirement can be shortened by using high-power microwave ovens.Copyright © 2022 The Authors
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Introduction: Cavitary lesions are rarely associated with COVID-19 and have been very rarely reported. We conducted a retrospective analysis of the COVID-19 patients admitted in our center in South India and attempted to study the distribution and etiology of cavitary lesions in COVID-19. Material(s) and Method(s): All inpatients in pulmonology department over a period of 14 months who were diagnosed to have COVID-19 and were found to have cavity on thoracic imaging were included in the study. patient with pre existing cavity were excluded. Patient demographics, comorbid conditions, treatment details, length of hospital stay, and outcomes were examined. The details of the cavitary lesions including their number, the time of detection and lobe of involvement, and the etiology of the cavity were described. Result(s): 602 patients with COVID-19 were admitted during the study period. A total of 9 patients were included in this report with an incidence of cavitation in hospitalised COVID-19 patients being 1.5%. 55.5% patients had evidence of Aspergillus infection confirmed by serology or isolation of Aspergillus from respiratory specimen. Two (22.2%) patients had tuberculosis. The mortality rate of COVID-19 patients with Aspergillus lesions was 44.4%. Majority were detected to have cavity in the third week of illness. Left lower lobe was the most common involved site. Result(s): The incidence of cavitary lesions in patients hospitalised with COVID-19 was 1.5% in this study most common etiology of these cavities was fungal followed by TB. These patients had a very high mortality rate of around 45% in this study. Early identification and treatment may improve clinical outcomes in these patients.
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Deep learning (DL) is a very powerful computational tool for various applications in scientific and industrial research which can be real-time implemented for societal benefits. Several factors impact the development of optimized DL models for better prediction including the amount of quality sample data, domain-specific knowledge, and the architecture of the model for extraction of the useful features/patterns from the data. The present chapter demonstrates the state-of-the-art DL methodologies used by the researchers from different laboratories under the Council of Scientific and Industrial Research (CSIR), India to solve important research activities across several sectors like Medical, Healthcare, Agriculture, Energy, etc. The Convolutional Neural Network (CNN) techniques are utilized for Tumor diagnosis, classifying molecular subtypes of glioma tissues, and predicting driver gene mutations in glioma. Similarly, the Long short-term memory (LSTM) model is applied for the assessment of crop production, and transfer learning is used for the classification of tea leaves. Further, the ensemble LSTM methodology is implemented for short-term prediction of wind speed to enhance the renewable energy sectors. Finally, the multivariate LSTM models were developed by integrating the weather parameters for the prediction of covid-19 spread over different states in India which is an input for policy planning and supply chain management during the pandemic time. All the use cases are being validated and the results are quite satisfying and provide confidence for the real-time application of DL for scientific and industrial research and societal benefit to the common people. © 2023 Elsevier B.V.
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Air pollution is accountable for various long-term and short-term respiratory diseases and even deaths. Air pollution is normally associated with a decreasing life expectancy. Governments have been implementing strategies to improve air quality. However, natural events have always played an important role in the concentration of air pollutants. In Australia, the lockdown period followed the Black Summer of 2019-2020 and coincided with the season of prescribed burns. This paper investigates the changes in the concentration of criteria pollutants such as particulate matter, nitrogen dioxide, ozone, and sulphur dioxide. The air quality data for the lockdown period in 2020 was compared with the pre-lockdown period in 2020 and with corresponding periods of previous years from 2016 to 2019. The results were also compared with the post-lockdown scenario of 2020 and 2021 to understand how the concentration levels changed due to behavioural changes and a lack of background events. The results revealed that the COVID-19 restrictions had some impact on the concentration of pollutants;however, the location of monitoring stations played an important role.
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Purpose. The study aimed to measure the impact of manufacturing employees' high work pressure on the urge for their family-life balance, and the impact of high work-life pressure and the urge for family-life balance on manufacturing employees' concern for mental health during COVID-19 pandemic from an emerging economy perspective. Methodology. The study has picked 20 items under three different variables such as work-life, family-life, and mental health. The structured questionnaire has been developed based on the literature survey and divided into two parts. The initial part has contained demographic information and the second part has contained measure items of the model. The questionnaire has been designed through Google Docs and distributed via Facebook messenger, E-mail, WhatsApp, IMO, etc. 400 data was collected through the snowball sampling technique and 201 data (response rate 50.25%) was found usable for the research. The exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were run to test the proposed research framework with the help of MS Excel 2007, SPSS 22.0, and AMOS 23.0. Finding. The findings revealed that high work-life pressure had a positive significant impact on the urge for family-life balance and both the high work-life pressure and urge for family-life balance had a positive significant impact on manufacturing employees' concern for mental health during the COVID-19 pandemic. Originality. The novelty of this research is the manufacturing employees' context during the COVID-19 pandemic.
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The novel coronavirus disease has produced destructive effects on human life, taking away millions of lives. The biggest bottleneck in detecting the COVID-19-affected patient is the limited availability and time-consuming features of conventional RT-PCR tests and the lack of specialized sample extraction laboratories. Early detection of this virus may help in the advancement of a medication approach and disease control strategies. In this research, we have developed an Android smartphone application that can detect pneumonia and COVID-19 from chest X-ray photographs using convolutional neural network deep learning algorithms (VGG16 and VGG19). The COVID-19, pneumonia, and healthy chest X-ray images are collected from various repositories of a public database, Kaggle. After applying the data augmentation technique, 9,000 chest X-ray photographs were used for training, including 3,000 images for COVID-19, pneumonia, and normal cases. For testing, 3,000 chest X-ray photographs were collected, with 1,000 images for all three cases. VGG16 model achieved better performance than the VGG19 with a training accuracy of 98.31% and validation accuracy of 95.03%. Next, the deep learning-based automatic classification framework is deployed into a smartphone application. Finally, the application has been tested and assessed by a focused group, and analytical results have been presented. © 2022 IEEE.
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The term work-life balance can be described as a path to manage stresses and burnouts in the workplace. In this Covid-19 pandemic, work-from-home practice includes both personal and professional spaces as employees, more often, stay digitally connected. As a result, personal life hardly can be separated, which will potentially create imbalanced life, which creates problems regarding physical and mental health of the employees. In such unprecedented situations, we are required to maintain and/or integrate balanced work-life. A balanced work-life gives employees a stress-free environment to work and improves employees' mental and physical health conditions and relationships. In this study, we focus on maintaining a proper work-life balance through a monitoring tool, the ‘Wheel of Life.' Considering the drastic changes in work culture (due to Covid-19, for example), we introduce an interactive interface based on ‘Wheel of life' concept. Our interface helps tune various important factors, such as business, creative, social, love and life purpose, and provides multiple recommendations. The purpose of the study is to assist web users to balance their work-life, improve psychological well-being and quality of life in this unforeseen situation. © 2022, Universidad Internacional de la Rioja. All rights reserved.
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As of August 15, 2020, Bangladesh lost 3591 lives since the first Coronavirus disease 2019 (COVID-19) case announced on March 8. The objective of the study was to report the clinical manifestation of both symptomatic and asymptomatic COVID-19-positive patients. An online-based cross-sectional survey was conducted for initial recruitment of participants with subsequent telephone interview by the three trained physicians in 237 adults with confirmed COVID-19 infection in Bangladesh. The study period was 27 April to 26th May 2020. Consent was ensured before commencing the interview. Collected data were entered in a pre-designed case record form and subsequently analyzed by SPSS 20.0. The mean±SD age at presentation was 41.59±13.73 years and most of the cases were male (73.0%). A total of 90.29% of patients reside in urban areas. Among the positive cases, 13.1% (n=31) were asymptomatic. Asymptomatic cases were significantly more common in households with 2 to 4 members (p=0.008). Both symptomatic and asymptomatic patients shared similar ages of presentation (p=0.23), gender differences (p=0.30) and co-morbidities (p=0.11). Only 5.3% of patients received ICU care during their treatment. The most frequent presentation was fever (88.3%), followed by cough (69.9%), chest pain (34.5%), body ache (31.1%), and sore throat (30.1%). Thirty-nine percent (n=92) of the patients had co-morbidities, with diabetes and hypertension being the most frequently observed. There has been an upsurge in COVID-19 cases in Bangladesh. Patients were mostly middle-aged and male. Typical presentations were fever and cough. Maintenance of social distancing and increased testing are required to meet the current public health challenge.
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COVID-19 , Adult , Middle Aged , Humans , Male , Female , COVID-19/epidemiology , SARS-CoV-2 , Bangladesh/epidemiology , Cross-Sectional Studies , Cough/epidemiology , Cough/etiologyABSTRACT
Objectives: Mucormycosis is an aggressive, life-threatening infection caused by fungi in the order Mucorales. There was an explosion of new cases of rhino-sino-orbital mucormycosis following the COVID pandemic in India, and the need for easy and rapid diagnostics was felt. The current diagnosis of mucormycosis relies on mycological cultures, radiology, and histopathology. These methods lack sensitivity and are most definitive later in the course of infection, resulting in the failure of timely intervention. A real-time multiplex PCR platform is commercially available for the detection of Rhizopus spp., Mucor spp.Rhizomucor spp., Lichtheimia spp., and Cunninghamella spp. (PN-700, MucorGenius , PathoNostics , Maastricht, The Netherlands) This real-time PCR has been validated to identify these fungal pathogens from bronchoalveolar lavage, tissue, and serum samples. This study aimed to validate this PCR-based system to detect Mucorales from nasal swab samples and evaluate its utility in the detection of Mucorales from nasal cavities of high-risk patients developing signs and symptoms of mucormycosis. Method(s): A single-center cross-sectional observational study was conducted on 50 hospitalized adult patients with signs and symptoms of mucormycosis. Nasal swabs were taken for PCR analysis once there was a clinical suspicion and were com-pared with the results of the gold standard.The gold standard for the diagnosis of mucormycosis was the conventional method (KOHmountedmicroscopy/HPE).Demographicdetails andrisk factorsfor thesepatients wererecorded, andthe RTPCR-based test was run on the nasal swab samples of all these 50 patients. The workflow is depicted graphically in Fig. 1 (Created with BioRender.com). Result(s): The study population mean (SD) age was 50 (16) years and consisted of 32 (64%) males. A total of 39 (78%) patients were known cases of diabetes mellitus, 48 (96%) patients had amphotericin B intake, and 20 (40%) had posaconazole intake. In all, 21 (42%) patients had a past history of COVID-19 infection;14 patients had received steroids and 10 patients received oxygen support. PCR for Mucorales was positive in 15 (30%) patients while the KOH mount was positive in 4 (8%) patients. Conclusion(s): These results are not encouraging for the use of nasal swabs as the sample for diagnosis of mucormyco-sis. Though the PCR performed better on the swab samples than KOH preparation and culture techniques, it highlights the importance of using standard sampling methods.
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The educational sector of Bangladesh is severely affected due to the sudden outbreak of novel Corona virus (COVID-19). Bangladesh which is one of the densely populated countries has a significant improvement in the education sector along with the others in last some decades but this pandemic has played a serious setback to almost all the sectors of this small country. As all the educational institutes of Bangladesh are closed since 17th March 2020 till 30th June 14, 2021 and this may lead to many detrimental effects. To measure these, a survey was conducted and collected data was analyzed by SPSS Statistics v 25.0. This paper highlighted the mental stress, socio-economic crisis of the students that badly affected their education. It is observed in this study that around 80% of the students are going through mental stress particularly for internet facilities and financial crisis in pandemic period.
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The emergence of the COVID-19 pandemic and the first global infodemic have changed our lives in many different ways. We relied on social media to get the latest information about COVID-19 pandemic and at the same time to disseminate information. The content in social media consisted not only health related advise, plans, and informative news from policymakers, but also contains conspiracies and rumors. It became important to identify such information as soon as they are posted to make an actionable decision (e.g., debunking rumors, or taking certain measures for traveling). To address this challenge, we developed and publicly released the first largest manually annotated Arabic tweet dataset, ArCovidVac, for the COVID-19 vaccination campaign, covering many countries in the Arab region. The dataset is enriched with different layers of annotation, including, (i) Informativeness (more vs. less important tweets);(ii) fine-grained tweet content types (e.g., advice, rumors, restriction, authenticate news/information);and (iii) stance towards vaccination (pro-vaccination, neutral, anti-vaccination). Further, we performed in-depth analysis of the data, exploring the popularity of different vaccines, trending hashtags, topics and presence of offensiveness in the tweets. We studied the data for individual types of tweets and temporal changes in stance towards vaccine. We benchmarked the ArCovidVac dataset using transformer models for informativeness, content types, and stance detection. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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Background During the COVID-19 pandemic, online education and entertainment have increased significantly due to strict isolation and frequent lockdowns. This study intended to explore the prevalence and potential factors associated with computer vision syndrome (CVS) among the postsecondary students of Bangladesh pursuing online education. Methods In total, there were 917 postsecondary students participated in this study. Information on sociodemographic variables, and CVS symptom-related variables were collected using a prevalidated self-administered questionnaire. The CVS questionnaire was used to assess an individual's CVS status. The bivariate association between CVS and other categorical variables was obtained using a chi 2 test. A multivariable logistic regression model was used to explore variables associated with the CVS. Results The overall prevalence of CVS was 68.16%. Most common symptoms were headache (42.4%), feeling of worsening eyesight (23.2%), and eye pain (23.2%). CVS was associated with educational status (p=0.03), family history of eye-related problems (p<0.001), personal history of eye-related problems (p<0.001), usage of eye accessories (p<0.001), type of device used for online education (p<0.01), average daily use (p<0.01), and usage pattern (p=0.02). After adjusting for confounders, CVS was significantly related to the use of mobile or tablet (adjusted OR, AOR 8.954, 95% CI 1.57 to 51.063), continuing online education for more than 12 hours/day without any break or insufficient break (AOR 7.654, 95% CI 1.625 to 36.053), and previous family (AOR 3.189, 95% CI 1.751 to 5.811) or personal history of eye problems or headaches, or insomnia (AOR 6.214, 95% CI 2.783 to 13.878). Conclusion A high prevalence of CVS was observed among the post-secondary students in Bangladesh. Since an extensive use of digital screens is somewhat unavoidable during unprecedented times, such as COVID-19, educators should include CVS awareness and prevention in their curricula. Copyright © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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Introduction The delay in seeking emergency obstetric care leads to significant maternal morbidity and mortality and can worsen during pandemics, especially in humanitarian conflict settings with low uptake of obstetric services. To mitigate the challenges related to the second delay caused by lack of transport in the COVID-19 pandemic, the organisation United Nations Population Fund implements a community-based referral project called Referral Hub in the Rohingya refugee population in Bangladesh. The objective of the paper is to describe the implementation process of the Referral hub and present clients' utilisation and perception of the service. Methods Findings from part of a larger mixed-method study, the analysis of the standard operating protocol of the intervention, secondary data of routine utilisation of the 12 referral hubs between January and August 2020, 21 key informant interviews and a community survey among 100 pregnant women are presented in this paper. Results The findings show an increasing trend in the referral hub utilisation and a strong recommendation of the service. Conclusion Due to a robust referral mechanism by collaborating with the community and engaging accessible and free of cost transport service, the intervention has high potential to improve access to facility care in low-resource and humanitarian contexts, especially during pandemics. Copyright ©