Journal Articles
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Item Epitope-Based Potential Vaccine Candidate for Humoral and Cell-Mediated Immunity to Combat Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic(American Chemical Society, 2020) Das, B.K.; Chakraborty, D.The emergence of severe acute respiratory syndrome from novel Coronavirus (SARS-CoV-2) has put an immense pressure worldwide where vaccination is believed to be an efficient way for developing hard immunity. Herein, we employ immunoinformatic tools to identify B-cell, T-cell epitopes associated with the spike protein of SARS-CoV-2, which is important for genome release. The results showed that the highly immunogenic epitopes located at the stalk part are mostly conserved compared to the receptor binding domain (RDB). Further, two vaccine candidates were computationally modeled from the linear B-cell, T-cell epitopes. Molecular docking reveals the crucial interactions of the vaccines with immune-receptors, and their stability is assessed by MD simulation studies. The chimeric vaccines showed remarkable binding affinity toward the immune cell receptors computed by the MM/PBSA method. van der Waals and electrostatic interactions are found to be the dominant factors for the stability of the complexes. The molecular-level interaction obtained from this study may provide deeper insight into the process of vaccine development against the pandemic of COVID-19. © 2020 American Chemical Society.Item A Scientometric Analysis of Global literature on Hydroxychloroquine based on SCOPUS(University of Idaho Library, 2021) Hadagali, G.S.; Shettar, I.M.; Shastri, L.; Ramesh Babu, B.R.This paper deals with the scientometric analysis of the global literature on Hydroxychloroquine as indexed in SCOPUS database from its first publication in 1946 to 2020. The objective of the study was to perform a scientometric analysis of Hydroxychloroquine publications. The study analyzed 25,163 publications which were contributed by 88,834 individual authors affiliated to 159 countries. The results showed momentum in the research publications during the 1980s and accelerated immediately from the beginning of the 21st century. It was observed a fluctuating trend for the Annual Growth Rate and CAGR recorded to 0.129635. The Relative Growth Rate recorded between 0.04 and 0.56 for different years. The Degree of Collaboration (DC) noted was 0.88 and 5.27 Collaboration Index (CI). The Journal of Rheumatology was the most preferred journal with 864 publications; Didier A. Raoult was the most productive author in Hydroxychloroquine (HCQ) research. The AP-HP Assistance Publique - Hopitaux de Paris, France was the top productive institution globally, and the USA was the most productive country in terms of the number of publications. © 2021Item A Scientometric Analysis and Visualization Mapping of Convalescent Plasma Therapy(University of Idaho Library, 2021) Shettar, I.M.; Hadagali, G.S.This paper deals with the scientometric analysis of the scholarly literature on Convalescent Plasma Therapy, or simply Plasma Therapy, as indexed in the SCOPUS database from its first publication to 2020. In this study, 1,722 bibliographic records were analysed which are published in 545 journals by 9491 authors from 6046 organizations located in nearly 175 countries. The results showed a sudden increase in the number of publications in 2020 because of the clinical trials due to the COVID-19 pandemic. During the period an inconsistent trend of publications and the annual growth rate is observed. The average Degree of Collaboration calculated for the overall period was 0.89 and Collaboration Index was 6.83. Pediatric Nephrology and Transfusion were the most preferred journals; Chantal Loirat was the most productive author in the field. The AP-HP Assistance Publique - Hopitaux de Paris, France, was the top productive institution and the USA was the most productive country in terms of the number of publications. © 2021,Library Philosophy and Practice.All Rights ReservedItem CURRENT SCENARIO OF SARS-CORONAVIRUS 2: EPIDEMIOLOGY; POST-COVID-19 AND GLOBAL IMPACTS(Slovak University of Agriculture, 2021) Sampath, V.P.; Govindaraj, P.; Ramasamy, R.; Mohan, M.Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a highly contagious strain of coronavirus that causes Coronavirus Disease 2019 (COVID-19) infection, which has distressed the world's health and wealth. This Global Pandemic outbreak has affected public health enormously at various customs. The investigation of SARS-CoV-2 is still at infancy; however, based on the available reports, this review gives an overview of the epidemiology, genomic landscape, diversity of SARS-CoV-2, viral genome pathogenic interactions, associating factors for COVID-19 infections, post-COVID-19, disease manifestations with their comorbidities, the major obstacles and the preventive measures along with current vaccine strategies of SARS-CoV-2. This review also summarizes all the relevant evidence of COVID-19 illness, which can provide valuable information on the SARS-CoV-2 genome and its mode of action strategies, thus delivering additional knowledge about COVID-19. © 2021. All Rights Reserved.Item COVID-19: Automatic detection from X-ray images by utilizing deep learning methods(Elsevier Ltd, 2021) Nigam, B.; Nigam, A.; Jain, R.; Dodia, S.; Arora, N.; Annappa, B.In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients’ X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. © 2021 Elsevier LtdItem Impact of COVID-19 on the Indian seaport transportation and maritime supply chain(Elsevier Ltd, 2021) Narasimha, P.T.; Jena, P.R.; Majhi, R.Impacts of COVID-19 in maritime transportation and its related policy measures have been investigated by more and more organizations and researchers across the world. This paper aims to examine the impacts of COVID-19 on seaport transportation and the maritime supply chain field and its related issues in India. Secondary data are used to analyze the performance indicators of major seaports in India before and during the COVID-19 crisis. We further explore and discuss the expert's views about the impact, preparedness, response, and recovery aspects for the maritime-related sector in India. The results on the quantitative performance of Indian major seaports during the COVID-19 indicate a negative growth in the cargo traffic and a decrease in the number of vessel traffic compared to pre-COVID-19. The expert survey results suggest a lack of preparedness for COVID-19 and the need for developing future strategies by maritime organizations. The overall findings of the study shall assist in formulating maritime strategies by enhancing supply chain resilience and sustainable business recovery process while preparing for a post-COVID-19 crisis. The study also notes that the Covid-19 crisis is still an ongoing concern, as the government, maritime organizations, and stakeholders face towards providing vaccine and remedial treatment to infected people. Further, this study can be expanded to the global maritime supply chain business context and to conduct interdisciplinary research in marine technical fields and maritime environment to measure the impact of COVID-19. © 2021 Elsevier LtdItem Temperature-Dependent Conformational Evolution of SARS CoV-2 RNA Genome Using Network Analysis(American Chemical Society, 2021) Singh, O.; Venugopal, P.P.; Mathur, A.; Chakraborty, D.Understanding the dynamics of the SARS CoV-2 RNA genome and its dependence on temperature is necessary to fight the current COVID-19 crisis. Computationally, the handling of large data is a major challenge in the elucidation of the structures of RNA. This work presents network analysis as an important tool to see the conformational evolution and the most dominant structures of the RNA genome at six different temperatures. It effectively distinguished different communities of RNA having structural variation. It is found that at higher temperatures (348 K and above), 80% of the RNA structure is destroyed in both the SPC/E and mTIP3P water models. The thermal denaturation free energy change ??Gvalue calculated for the long-lived structure at higher temperatures of 348 and 363 K ranges from 2.58 to 2.78 kcal/mol for the SPC/E water model, which agrees well with the experimentally reported thermal denaturation free energy range of 2.874 kcal/mol of SARS CoV-NP at normal pH. At higher temperatures, the stability of RNA conformation is found to be due to the existence of non-native base pairs in the SPC/E water model. © 2021 American Chemical SocietyItem Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany(John Wiley and Sons Inc, 2021) Balamurugan, V.; Chen, J.; Qu, Z.; Bi, X.; Gensheimer, J.; Shekhar, A.; Bhattacharjee, S.; Keutsch, F.N.This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils. © 2021. The Authors.Item Molecular mechanism of inhibition of COVID-19 main protease by ?-adrenoceptor agonists and adenosine deaminase inhibitors using in silico methods(Taylor and Francis Ltd., 2022) Venugopal, P.P.; Chakraborty, D.Novel coronavirus (COVID-19) responsible for viral pneumonia which emerged in late 2019 has badly affected the world. No clinically proven drugs are available yet as the targeted therapeutic agents for the treatment of this disease. The viral main protease which helps in replication and transcription inside the host can be an effective drug target. In the present study, we aimed to discover the potential of ?-adrenoceptor agonists and adenosine deaminase inhibitors which are used in asthma and cancer/inflammatory disorders, respectively, as repurposing drugs against protease inhibitor by ligand-based and structure-based virtual screening using COVID-19 protease-N3 complex. The AARRR pharmacophore model was used to screen a set of 22,621 molecules to obtain hits, which were subjected to high-throughput virtual screening. Extra precision docking identified four top-scored molecules such as +/?-fenoterol, FR236913 and FR230513 with lower binding energy from both categories. Docking identified three major hydrogen bonds with Gly143, Glu166 and Gln189 residues. 100 ns MD simulation was performed for four top-scored molecules to analyze the stability, molecular mechanism and energy requirements. MM/PBSA energy calculation suggested that van der Waals and electrostatic energy components are the main reasons for the stability of complexes. Water-mediated hydrogen bonds between protein-ligand and flexibility of the ligand are found to be responsible for providing extra stability to the complexes. The insights gained from this combinatorial approach can be used to design more potent and bio-available protease inhibitors against novel coronavirus. Communicated by Ramaswamy H. Sarma. © 2020 Informa UK Limited, trading as Taylor & Francis Group.Item An empirical study of the impact of masks on face recognition(Elsevier Ltd, 2022) Jeevan, G.; Zacharias, G.C.; Nair, M.S.; Rajan, J.Face recognition has a wide range of applications like video surveillance, security, access control, etc. Over the past decade, the field of face recognition has matured and grown at par with the latest advancements in technology, particularly deep learning. Convolution Neural Networks have surpassed human accuracy in Face Recognition on popular evaluation tests such as LFW. However, most existing models evaluate their performance with an assumption of the availability of full facial information. The COVID-19 pandemic has laid forth challenges to this assumption, and to the performance of existing methods and leading-edge algorithms in the field of face recognition. This is in the wake of an explosive increase in the number of people wearing face masks. The reduced amount of facial information available to a recognition system from a masked face impacts their discrimination ability. In this context, we design and conduct a series of experiments comparing the masked face recognition performances of CNN architectures available in literature and exploring possible alterations in loss functions, architectures, and training methods that can enable existing methods to fully extract and leverage the limited facial information available in a masked face. We evaluate existing CNN-based face recognition systems for their performance against datasets composed entirely of masked faces, in contrast to the existing standard evaluations where masked or occluded faces are a rare occurrence. The study also presents evidence denoting an increased impact of network depth on performance compared to standard face recognition. Our observations indicate that substantial performance gains can be achieved by the introduction of masked faces in the training set. The study also inferred that various parameter settings determined suitable for standard face recognition are not ideal for masked face recognition. Through empirical analysis we derived new value recommendations for these parameters and settings. © 2021 Elsevier Ltd
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