Natural Products as Anti-COVID-19 Agents: An In Silico Study

Background: The coronavirus disease 2019 (COVID-19) is a life-threatening viral infection caused by a positive-strand RNA virus belonging to the Coronaviridae family called severe acute respiratory distress syndrome coronavirus 2 (SARS-CoV-2). This virus has infected millions of peo-ples and caused hundreds of thousands of deaths around the world. Unfortunately, to date, there is no specific cure for SARS-CoV-2 infection, although researchers are working tirelessly to come up with a drug against this virus. Recently, the main viral protease has been discovered and is regarded as an ap-propriate target for antiviral agents in the search for the treatment of SARS-CoV-2 infection due to its role in polyproteins processing coronavirus replication. Material(s) and Method(s): This investigation (an in silico study) explores the effectiveness of 16 natural compounds from a literature survey against the protease of SARS-CoV-2 in an attempt to identify a promising antiviral agent through a molecular docking study. Result(s): Among the 16 compounds studied, apigenin, alpha-hederin, and asiatic acid exhibited significant docking performance and interacted with several amino acid residues of the main protease of SARS-CoV-2. Conclusion(s): In summary, apigenin, alpha-hederin, and asiatic acid protease inhibitors may be effective potential antiviral agents against the main viral protease (Mpro) to combat SARS-CoV-2.Copyright © 2021 Bentham Science Publishers.

How does a hotel's hygiene protocol quality trigger guests' revisit intention during the pandemic? Evidence from Chinese guests

Purpose: Based on the Stimulus-Organism-Response Model, this study aims to investigate how the intention of Chinese guests to revisit a hotel (response) is triggered by the quality of the hotel's hygiene protocols (stimulus) during the pandemic. Brand image, perceived guest satisfaction and perceived customer trust were examined as the organism factors in this model. Design/methodology/approach: The quantitative method was adopted to collect data via a structured online survey of 385 Chinese hotel guests. Their responses were analyzed using SPSS (v.26) and SmartPLS (3.3.2) software. Findings: The quality of hotel hygiene protocols was found to have a significant impact on hotel brand image, perceived guest satisfaction and perceived guest trust. Hotel brand image, perceived guest satisfaction and perceived guest trust, in turn, demonstrated significant relationships with guests' revisit intention. Research limitations/implications: Theoretically, the present study offers a framework to understand the impact of hotel hygiene protocols on guest revisit intention. Practically, the findings of the study encourage industry practitioners to implement proper safety protocols and standard operating procedures related to COVID-19. Originality/value: Since the beginning of the pandemic, hotel hygiene standards have become a key concern for guests. The current study provides important and meaningful insights into whether and how hotel hygiene quality promotes guest revisit intention. © 2023, International Tourism Studies Association.

User Perspective Bangla Sentiment Analysis for Online Gaming Addiction using Machine Learning

Sentiment analysis is the modern Natural Language Processing (NLP) technique for determining the sentiment of a user. The recent COVID-19 pandemic has pushed people of all ages, particularly the youth to get directly or indirectly involved in internet activities, one of which is online gaming. People have become increasingly involved in online gaming since they have easy access to the internet via smartphones. This research study has attempted to investigate online gaming addiction using different machine learning classification algorithms from over 401 data points. People of all ages, particularly students in high school, college, and university, are considered for data collection. After preprocessing and feature engineering the collected data, six state-of-the-art machine learning classification algorithms viz. Decision Tree, Random Forest, Multinomial Naive Bayes, Extreme Gradient Boosting, Support Vector Machine and K Nearest Neighbor are used to train the model. All six classifiers predict with high accuracy, with Multinomial Naive Bayes (MNB) having the highest accuracy of 73.27%. © 2022 IEEE.

Preparedness of Students for Future Teaching and Learning in Higher Education: A Bangladeshi Perspective

The COVID-19 pandemic has had a significant impact on higher education (HE) across the globe, including in Bangladesh. The Bangladeshi HE system is going through an abrupt transition and transformation to cope with the crisis. This chapter is based on data collected from teachers and students of Bangladeshi public and private HE institutions regarding teaching and learning during the COVID-19 lockdown. In Bangladesh, some universities switched to online distance teaching and learning quickly during this period, and others lagged behind in this regard. Teachers and students from both groups of public and private universities participated in the study, including those who attended online teaching and learning activities and those who did not participate. This chapter highlights both teachers' and students' perspectives regarding students' future preparedness for participating fully in the changing landscape of HE, especially technology-enhanced teaching and learning. Understanding these perspectives of teachers and students is important to address the digital divide and social justice issues in the policy and practice. Within the HE sector in Bangladesh, it is especially vital while transforming its education system and adapting emerging technologies to address the challenges of education in future emergencies.

An Empirical Model of Classifying Lung Affected Diseases to Detect COVID-19 Using Chest X-ray Employing Convolutional Neural Architecture

The earlier detection and accurate diagnosis of COVID seem to be a global problem. It is difficult to make a large number of testing equipment, but then again, their reliability is relatively poor. Recent research indicates the usefulness of chest x-ray pictures in identifying COVID. This study presents a deep learning algorithm developed from the ground up to categorize as well as confirm the existence of COVID in a set of X-ray imaging data. We designed a CNN architecture from the ground up to retrieve elements from provided X-ray data to categorize them and identify the individual contaminated with COVID. Our strategy may aid in mitigating the consistency issues while working with medical data. In contrast to some other classifying activities with a large enough image database, obtaining large X-ray datasets for this classification job is challenging. So, we applied multiple data enhancement techniques to maximize the accurateness, achieving a significant accuracy of 97.75 percent. © 2022 IEEE.

Understanding Students' Online Learning Performance during Covid-19 Pandemic in Bangladesh

Academic performance of students is the measurement of their academic achievement, which is influenced by various factors. This study aims to investigate the antecedents that affected Students' online learning performance during the Covid-19 Pandemic in Bangladesh. The current study is a quantitative cross-sectional study that has been conducted by performing an online survey on 408 university students in Bangladesh. The study found that perceived satisfaction mediated e-learning quality and metacognitive strategies with the students' academic performance partially and fully, respectively, and made an indirect relationship between home environment and performance. The present study provides practical and theoretical implications by developing a framework and providing information to the stakeholders. Finally, this study has been concluded by mentioning a few limitations and giving direction for future research. © 2022 IEEE.

Factors Influencing University Students’ E-Learning Adoption in Bangladesh During COVID-19: An Empirical Study with Machine Learning

The present study investigates the factors that influence Bangladeshi university students’ behavior toward adopting the e-learning system during the COVID-19 emergency. A conceptual framework was developed by adopting the variables from several previously published studies to meet the aims of the study. The current study was conducted in the quantitative approach by performing a survey on 393 university students of Bangladesh. All the obtained data were analyzed by using SPSS, AMOS, and machine learning algorithms. The findings of the study indicate that facilitating condition, effort expectancy, performance expectancy, and social influence affect the behavioral intention of the students to adopt e-learning, and the current study provides both practical and theoretical contributions. Theoretically, it provides a research framework and the literature to analyze the factors that trigger the behavioral intention of Bangladeshi university students. Practically, the findings will assist the policymakers of the education industry in Bangladesh. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images.

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

IoT and Blockchain-Based Mask Surveillance System for COVID-19 Prevention Using Deep Learning

On the edge of the worldwide public health crisis, the COVID-19 disease has become a serious headache for its destructive nature on humanity worldwide. Wearing a facial mask can be an effective possible solution to mitigate the spreading of the virus and reduce the death rate. Thus, wearing a face mask in public places such as shopping malls, hotels, restaurants, homes, and offices needs to be enforced. This research work comes up with a solution of mask surveillance system utilizing the mechanism of modern computations like Deep Learning (DL), Internet of things (IoT), and Blockchain. The absence or displacement of the mask will be identified with a raspberry pi, a camera module, and the operations of DL and Machine Learning (ML). The detected information will be sent to the cloud server with the mechanism of IoT for real-time data monitoring. The proposed model also includes a Blockchain-based architecture to secure the transactions of mask detection and create efficient data security,monitoring, and storage fromintruders. This research further includes an IoT-based mask detection scheme with signal bulbs, alarms, and notifications in the smartphone. To find the efficacy of the proposed method, a set of experiments has been enumerated and interpreted. This research work finds the highest accuracy of 99.95% in the detection and classification of facial masks. Some related experiments with IoT and Block-chain-based integration have also been performed and calculated the corresponding experimental data accordingly.ASystemUsability Scale (SUS) has been accomplished to check the satisfaction level of use and found the SUS score of 77%. Further, a comparison among existing solutions on three emergent technologies is included to track the significance of the proposed scheme. However, the proposed system can be an efficient mask surveillance system for COVID-19 and workable in real-time mask detection and classification. © 2022 Tech Science Press. All rights reserved.

Anosmia with or without ageusia as a presenting symptom in COVID-19 positive patients: a single center study at Dhaka in Bangladesh

Background: The loss of smell and taste in COVID-19 patients is now acknowledged as one of the disease's primary symptoms.

A CNN-based CADx Model for Pneumonia Detection from Chest Radiographs with Web Application

-Pneumonia is a bacterial infection-caused life-threatening respiratory disease. About 15% all over the world kid's loss of life is triggered via pneumonia. A new virus called COVID-19 (in which) most important indications are pneumonia. Computer-aided diagnostic (CADx) methods have been studied for decades for the diagnosis of chest X-ray images based on lung diseases. For visual recognition, these tools assess the image properties derived from CNN. CNN filters a photo to acquire information from the chest X-ray. Throughout this study, we consider the performance of a customized CNN model used as feature extractors by the way of a variety of classifiers to distinguish the unusual and pneumonic chest X-Rays. Statistical findings point out that our CADx model can assist in the evaluation of clinical images as well. The user can insert their chest radiograph to the web app and find out their pneumonia condition, whether it is present or not present. Our proposed identification method's accuracy is 94% which is very high compared with other states of artwork. © 2021 IEEE.

POSSIBLE MUTATION PATHWAYS IN SARS-CoV-2

Mutations are the best way to generate genetic variation, as they provide the raw material in which evolutionary forces, like natural selection, can act. However, mutations are not always able to change the apparent behaviour of an organism, instead, they are capable of providing normal and abnormal biological functions. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has triggered a new and controversial biological scenario. Despite the hundreds of studies performed, to date, there is no specific treatment available. Several genomic and non-genomic mutations have been also reported, and due to its high genome size and mutation capacity, it can acclimatize to variable environments and has become the leading cause of high infection and mortality rates. This review outlines the possible pathways behind SARS-CoV-2 mutations.

COVID-19 Pandemic and Immune Boost up: The Synergistic Role of Nutrients

World Health Organization (WHO) declared a global public health emergency due to the recent spread of COVID-19 throughout the world. Millions of people are affected daily and thousands died. Almost all countries are now paying attention to control this pandemic outbreak. Therefore, researchers are trying to identify the pathophysiology of the disease, appropriate prognosis, effective management and prevention of COVID-19. Based on current published evidence, this review article specifies the role of different nutrients in the possible prevention and management of COVID-19 and viral infections. Balanced nutrition including adequate vitamin C, vitamin A, vitamin D, magnesium, selenium, zinc and phytonutrients have shown promising immune-boosting roles in COVID-19 and other respiratory infections due to their potential anti-inflammatory and antioxidants properties. These micronutrients act against COVID-19 infections both individually and synergistically.

Surveillance of COVID-19 Using Geospatial Data: An Emergency Department Perspective

Background: The outbreak of coronavirus 2019 (COVID-19) which emerged in December 2019 spread rapidly and created a public health emergency. Geospatial records of case data are needed in real time to monitor and anticipate the spread of infection. Methods: This study aimed to identify the emerging hotspots of COVID-19 using a geographic information system (GIS)-based approach. Data of laboratory-confirmed COVID-19 patients from March 15 to June 12, 2020, who visited the emergency department of a tertiary specialized academic hospital in Dubai were evaluated using ArcGIS Pro 2.5. Spatiotemporal analysis, including optimized hotspot analysis, was performed at the community level. Results: The cases were spatially concentrated mostly over the inner city of Dubai. Moreover, the optimized hotspot analysis showed statistically significant hotspots (p < 0.01) in the north of Dubai. Waxing and waning hotspots were also observed in the southern and central regions of Dubai. Finally, there were nonsustaining hotspots in communities with a very low population density. Conclusion: This study identified hotspots of COVID-19 using geospatial analysis. It is simple and can be easily reproduced to identify disease outbreaks. In the future, more attention is needed in creating a wider geodatabase and identifying hotspots with more intense transmission intensity.

Experiences of front-line nurses caring for patients with COVID-19 in Bangladesh: A qualitative study

Background: As the incidence of COVID-19 is increasing, the Bangladesh government has announced a countrywide shutdown instead of a lockdown. Consequently, front-line healthcare workers, particularly nurses, are confronting more challenging situations at work. Objective: This study aimed to explore front-line nurses' experiences caring for patients with COVID-19 in Dhaka, Bangladesh. Methods: A qualitative descriptive study was conducted among front-line nurses caring for patients with COVID-19. Twenty nurses were purposively chosen from January to March 2021 to participate in semi-structured online interviews. Interviews on audio and video were collected, analyzed, interpreted, transcribed verbatim, and verified by experts. Thematic analysis was used. Results: Nine themes emerged and were grouped into negative and positive experiences. The themes of negative experiences include lack of necessary medical equipment, use of non-standard personal protective equipment, work overload, long working hours, poor working environment, and lack of quality of nursing care. The positive experiences include feeling self in a patient position, nurses' coping strategy in COVID-19 patient care, and establishing emotional control. Conclusion: The study results encourage national and international health care professionals to cope with adverse working environments. Also, the findings provide nurses with techniques for dealing with any critical situation, controlling patients' emotions, and how empathy increases self-confidence and patient care. The research should also be used to enhance government policy, nursing council policy, ministry of health policy, and other healthcare agencies.

An Agent-Based Simulation Model to Evaluate Contacts, Layout, and Policies in Entrance, Exit, and Seating in Indoor Activities Under a Pandemic Situation

The outbreak of the novel coronavirus SARS-CoV2 has dramatically changed the world and has been a severe health threat in 2020 and 2021. In this article, an agent-based simulation model of pedestrian dynamics is proposed for classroom-type indoor spaces (e.g., classroom, auditorium, food court, and meeting room), which will help organizations such as universities to evaluate alternative policies (namely entrance and exit policy, seating policy, and room layout) concerning the contact-caused risk associated with activities in such places during a pandemic situation. In particular, the proposed work focuses on solving the indoor seat allocation and traffic movement problem while practicing appropriate physical distancing measures. The proposed seating policy evaluates the distance of a seat from the doors and pathways facilitating the evaluation of contact-caused risk associated with the pathway and indoor area movement. Various statistics from two perspectives, risk, and logistics, are reported in the simulation results. The risk metrics used in evaluating different policies include average exposure duration and an average number of contacts with others. To develop a highly realistic crowd simulation considering physical distancing and human intervention nature, deadlock detection and resolution mechanisms are incorporated. From this study, it has been observed that the proposed social distancing (SD) seating policy and zonal exit policy can significantly reduce the contact number and exposure duration at a higher occupancy level. The proposed work helps the organizational policymakers to evaluate different policies and ensure the safe operation of the organizations under pandemic situations. IEEE

Amentoflavone, new hope against SARS-CoV-2: an outlook through its scientific records and an in silico study

Background: The plant-derived bioflavonoid amentoflavone has many important biological activities, among them remarkable antiviral effects, even against severe acute respiratory syndrome Coronavirus (SARS-CoV). It inhibits severe acute respiratory syndrome coronavirus (SARS-CoV) with an IC50 value of 8.3 M. (TMPRSS-2 activity is now thought to be the only factor necessary for cell entry and viral pathogenesis). In comparison, 3CLPRO is needed for COVID-19 replication and maturation during its life cycle. Aim: This study aims to perform an in silico study on amentoflavone activity against structural and non-structural severe acute respiratory syndrome coronavirus (SARS-CoV)-2 3-chymotrypsin-like protease (3CLPRO) and human transmembrane protease serine 2 (TMPRSS-2) proteins. Materials and Methods: Molecular docking studies were carried out using compounds against 3CLPRO and TMPRSS-2 proteins through the Swiss model, Uniport, PROCHECK, Swiss PDB viewer, PyMol, PyRx, and Desmond (Schrodinger package) computerized software.

Short-term electrical load forecasting via deep learning algorithms to mitigate the impact of COVID-19 pandemic on power demand

The COVID-19 situation has created an exceptional challenge in the power management system (PMS). This work mainly focuses on the load management through load forecasting. Power generation and distribution is the most important part of PMS. Accurate load forecasting can help to secure electricity scheduling, supply, and reduce the wastage of power. Right now, social distancing has created a great challenge to the administrators to run the power system efficiently and uninterruptedly with minimum involvement of human. In the sector of load management, it can be done through a proper and faster load forecasting approach. Electrical Load Forecasting through deep learning algorithm can perform an effective role in Power Management System (PMS). In this research real data is collected from West Zone Power Distribution Company Limited (WZPDCL) and meteorological data like temperature and humidity are collected from the website of Bangladesh Meteorological Department to train and forecast electrical load using MATLAB. Long-Short Term Memory (LSTM), Feed Forward Back Propagation (FFBP) and ELMAN Neural Network (NN) are used to forecast electrical load. As exogenous data, the load factor (L.F.), power factor (P.F.), current and temperature were used to train algorithms in forecasting the electrical load. A comparative analysis is shown to indicate which is the best suitable method for load forecasting of WZPDCL. Electrical load forecasting results are evaluated through Root Mean Square Error (RMSE). In this research for short-term electrical load forecasting, Feed Forward Back Propagation has shown a minimum RMSE value. © 2021 IEEE.

The role of hmgb1 in the immune response to sars-cov-2 infection: From pathogenesis towards a new potential therapeutic target

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is the most important emerging pathogen since it was discovered in late 2019, infecting millions of people worldwide. The human body's defence against this new viral respiratory infection depends on the immune response of each person with a crucial impact on the appearance of clinical signs. Therefore, it is important to identify endogenous molecules with a fundamental role in severe pulmonary inflammation associated with SARS-CoV-2 infection. The impact of high mobility group proteins (HMGBs) in the pathogenesis of coronavirus disease 2019 (COVID-19) was recently proposed. There is also recent evidence that HMGBs, particularly HMGB1–2, play important roles in the replication of viral genomes. Moreover, HMGB1–4 proteins appear to be associated with inflammatory processes in the pathogenesis of many other viral diseases and disorders, including lung disease, ischemia-reperfusion-injury, sepsis, coagulopathy, trauma, neurological disorders, and cancer. This article presents the possible roles of HMGB1 in SARS-CoV-2 replication and its involvement in the pathogenesis of clinical severe pulmonary manifestations;these data can be useful in further virologic studies and the finding of new potential therapeutic targets in COVID-19.