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Coronavirus Disease (COVID-19) is an internationally recognized public health emergency. The disease, which has an incredibly high propagation rate, was discovered at the end of December 2019 in Wuhan, Hubei Province, China. The virus that causes COVID-19 is referred to as severe acute respiratory illness. Real-time reverse transcriptase (RT)-PCR assay is the primary diagnostic practice as a reference method for accurate diagnosis of this disease. There is a need for strong technology to detect and monitor public health. Early notification on signs and symptoms of the disorder is important and may be managed up to a few extents. To analyze the early signs and side effects of COVID-19 explicit techniques were applied. Sensors have been used as one of the methods for detection. These sensors are cost effective. These sensors will combine with a systematic device. It is utilized to detect the chemical compound and combined with a biological component. It is detected through physiochemical detector. Nanomaterials represent a robust tool against COVID-19 since they will be designed to act directly toward the infection, increase the effectiveness of standard antiviral drugs, or maybe to trigger the response of the patient. In this paper, we investigate how nanotechnology has been used in the improvement of nanosensor and the latest things of these nanosensors for different infections. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
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Background: Signal detection is one of the most advanced and promising techniques in the world of pharmacovigilance. Remdesivir is approved for emergency use by the US Food and Drug Administration (FDA) for patients with coronavirus disease 2019 (COVID-19). Its benefit- risk ratio is still being explored because data in the field are rather scant. On the other hand hyperkalemia is a potentially life-threatening electrolyte disorder. Severe hyperkalemia can occur suddenly and can cause life-threatening heart rhythm changes (arrhythmia) that cause a heart attack. Even mild hyperkalemia can cause heart related problems over time if not treated. Objective(s): To evaluate the potential association of Remdesivir with risk of Hyperkalemia by analyzing the spontaneous reports through disproportionality analysis. Method(s): Data were obtained from the public release of data in FAERS. Case/non-case method was adopted for the analysis of association between Remdesivir use and Hyperkalemia. The data-mining algorithm used for the analysis were Reporting Odds Ratio(ROR) and Proportional Reporting Ratio (PRR). A value of ROR-1.96SE>, PRR>=2 were considered as positive signal. Result(s): A total of 7 DE's associated with Remdesivir use and hyperkalemia were reported. The mean age of the patients of Remdesivir associated events was found to be 75 years [95% CI]. The reports by gender were distributed with a male to female ratio of 3:1, though gender was not revealed in 3 reports. The data mining algorithms exhibited positive signal for hyperkalemia (PRR: 2.349, ROR: 2.354) upon analysis as those were well above the pre-set threshold. Three case reports were identified which strengthened these findings and highlighted the importance of laboratory parameters for the early detection of hyperkalemia Conclusion(s): The current study found a potential risk of hyperkalemia with the use of Remdesivir and there is an urgent need to thoroughly investigate the same and take the necessary action to avoid or minimize the risk.Copyright © 2023
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Drug repurposing is an up-and-coming concept in the world of medicine. It is an efficient way to use already existing drug formulations in the treatment of diseases besides the ones which were initially intended for. This circumvents the tedious process of drug development and approval and helps to conserve resources. While multiple drugs have already been repurposed, their utility and reintroduction into the market is still a new concept. Even though it optimizes the use of resources, lab testing and animal model trials form a crucial aspect of determining the efficacy of these drugs in different diseases. This review encompasses the methods and trends in the repurposing of drugs while highlighting the use of animal models and the benefits of repurposing drugs during the COVID-19 pandemic. © Springer Nature Singapore Pte Ltd. 2023.
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Coronavirus disease-2019 (COVID-19) is a very serious health concern to the human life throughout the world. The Internet of Medical Things (IoMT) allows us to deploy several wearable Internet of Things-enabled smart devices in a patient's body. The deployed smart devices should then securely communicate to nearby mobile devices installed in a smart home, which then securely communicate with the associated fog server for information processing. The processed information in terms of transactions are formed as blocks and put into a private blockchain consisting of cloud servers. Since the patient's vital signs are very confidential and private, we apply the private blockchain. This article makes utilization of fog computing and blockchain technology simultaneously to come up with more secure system in an IoMT-enabled COVID-19 situation for patients' home monitoring purpose. We first discuss various phases related to development of a new fog-based private blockchain-enabled home monitoring framework. Next, we discuss how artificial intelligence-enabled big data analytics helps in analyzing and tracking the patients' information related to COVID-19 cases. Finally, a blockchain implementation has been performed to exhibit practical demonstration of the proposed blockchain system.
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This chapter makes an attempt to explain how different interconnected measures of globalisation (namely, tariff reform, agricultural trade liberalisation and capital account liberalisation), a land augmenting technological progress and adverse supply shocks arising due to COVID-19, play a major role not only in determining skilled-unskilled wage disparity and unemployment of skilled labour but also income distribution in general. In so doing, we construct a three-sector general equilibrium framework. We use the "luxury unemployment hypothesis" to explain the unemployment of skilled labour who earns an institutionally given fixed wage. We obtain that tariff liberalisation widens the skilled-unskilled wage gap and leaves unemployment of skilled labour unchanged. Agricultural trade liberalisation turns the income distribution in favour of the landed gentry. Capital account liberalisation reduces the unemployment of skilled labour without any effect on income distribution, while land-augmenting technological progress reduces the wage gap and turns the income distribution against the landed gentry. We use this model to analyse the adverse impacts of the pandemic crises. We introduce real government expenditure and an efficiency parameter for unskilled labour in order to explain the effects of an expansionary fiscal policy and "Covid Long Haulers" which affect the unskilled workers. While discussing the effects of expansionary fiscal policy (which is financed by lump-sum tax), what we get is the possibility of an increase in unemployment of skilled labour. The findings of this chapter have a specific accent on policy-making for a developing country like India. © 2023 selection and editorial matter, Kausik Gupta and Jayanta Kumar Dwibedi;individual chapters, the contributors. All rights reserved.
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Outlier is a value that lies outside most of the other values in a dataset. Outlier exploration has a huge importance in almost all the industry applications like medical diagnosis, credit card fraudulence and intrusion detection systems. Similarly, in economic domain, it can be applied to analyse many unexpected events to harvest new knowledge like sudden crash of stock market, mismatch between country's per capita incomes and overall development, abrupt change in unemployment rate and steep falling of bank interest. These situations can arise due to several reasons, out of which the present COVID-19 pandemic is a leading one. This motivates the present researchers to identify a few such vulnerable areas in the economic sphere and ferret out the most affected countries for each of them. Two well-known machine-learning techniques DBSCAN and Z-score are utilised to get these insights, which can serve as a guideline towards improving the overall scenario subsequently. Copyright © 2023 Inderscience Enterprises Ltd.
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Depression is a common mental problem that can fundamentally affect individuals' emotional wellness as well as their everyday lives. After COVID-19 other pandemics and subsequent social isolation this issue is more potent than ever. Numerous research works have been going on searching for methods that effectively recognize depression in order to detect depression. In this regard, a number of studies have been proposed. In this study, it examines a number of previous ones utilizing various Machine Learning (ML) and Artificial Intelligence (AI) methods for depression detection. In addition, various methods for determining an individual's mood and emotion are discussed. This study also discusses how facial expression, voice, gesture can be understood by chatbot and classified it as a depressed person or not. Addition to this, it reviews all the related research works and evaluates their methods to detect depression. © 2023 IEEE.
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Misinformation is an important topic in the Information Retrieval (IR) context and has implications for both system-centered and user-centered IR. While it has been established that the performance in discerning misinformation is affected by a person's cognitive load, the variation in cognitive load in judging the veracity of news is less understood. To understand the variation in cognitive load imposed by reading news headlines related to COVID-19 claims, within the context of a fact-checking system, we conducted a within-subject, lab-based, quasi-experiment (N=40) with eye-tracking. Our results suggest that examining true claims imposed a higher cognitive load on participants when news headlines provided incorrect evidence for a claim and were inconsistent with the person's prior beliefs. In contrast, checking false claims imposed a higher cognitive load when the news headlines provided correct evidence for a claim and were consistent with the participants' prior beliefs. However, changing beliefs after examining a claim did not have a significant relationship with cognitive load while reading the news headlines. The results illustrate that reading news headlines related to true and false claims in the fact-checking context impose different levels of cognitive load. Our findings suggest that user engagement with tools for discerning misinformation needs to account for the possible variation in the mental effort involved in different information contexts. © 2023 ACM.
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The entire world has gone through a pandemic situation due to the spread of novel corona virus. In this paper, the authors have proposed an ensemble learning model for the classification of the subjects to be infected by coronavirus. For this purpose, five types of symptoms are considered. The dataset contains 2889 samples with six attributes and is collected from the Kaggle database. Three different types of classifiers such as Support vector machine (SVM), Gradient boosting, and extreme gradient boosting (XGBoost) are considered for classification purposes. For improving the learning strategy and performance of the proposed models subjected to accuracy, the learning rates are varied for each node of the tree-based ensemble classifiers. Also, the hyperparameters of the XGBoost model are optimized by applying the Bayesian optimization (BO) technique. The best accuracy in SVM classifier is found as 91.69%. 96.58% accuracy is obtained in the modified gradient boosting model. The optimized XGBoost model is providing 100% accuracy which is better than other. © 2022 IEEE.
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Background: Severe viral pneumonia cases were observed in the people of Wuhan, China in December 2019. It has already affected almost every country around the globe and was declared a pandemic by the World Health Organization. We aim to evaluate the therapeutics and safety of various off label COVID-19 drugs. Method(s): PubMed, Research Gate, Science Direct, Google Scholar, Centre for Disease control and prevention (CDC) portal, Chinese Centre for Disease Control and prevention (CCDC) portal, World Health Organization (WHO) portal were searched for obtaining reliable data. Result(s): COVID-19 is creating a storm of deaths and active cases globally, which is forcing the pharmaceutical companies and scientists to work day and night to find an effective and safer anti-COVID-19 medication. Various in vitro and clinical trials had been performed as well as are currently ongoing to analyze the mechanisms and therapeutics of off label medications like Chloroquine, Hydroxychloro-quine, Amodiaquine, Azithromycin, Remdesivir, Favipiravir, Ritonavir/Lopinavir, Umifenovir, Osel-tamivir, Ribavirin, Nafamostat, Camostat, Tocilizumab, Ivermectin, Nitazoxanide, Famotidine, Vitamin D, Corticosteroids and Dexamethasone. In vitro studies were performed by utilizing Vero E6 cells and hSLAM cells while open/closed, randomized/non-randomized, single-centered/multi-centered and retrospective clinical trials and case studies were organized to determine their safety and efficacy. Conclusion(s): Although these drugs have shown promising results against COVID-19 patients, it cannot be concluded that these drugs are truly safe and effective because there are no conclusive evidence to support the facts since only limited researches and studies had been investigated.Copyright © 2021 Bentham Science Publishers.
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The COVID-19 pandemic caused by the coronavirus has dramatically changed the lives of students all around the world, with the virus's effects profoundly impacting students' physical and emotional well-being. Due to a series of shutdowns and lockdowns, social distancing, and further closure of schools, colleges, and institutions to ameliorate the pandemic crisis, the teaching and learning process shifted to an online form. As a result, students all over the world have been forced to deal with the problem as a last resort to accepting online education. This study looked at the efficiency of online education in the current situation and the student's reactions. To enhance the online method of education for students, we examined the success characteristics of online education in the Indian state of Odisha. The study's samples were collected from the faculty members of various graduate and post-graduate educational institutions in Odisha, who were recruited by questionnaire to get an expert opinion. © 2023 by the authors.
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Purpose: The existing literature contains few references on the better adaptors of online distance education amongst STEM (read as science, technology, engineering and mathematics) and non-STEM (composed of humanities, social science and commerce) study groups in an Indian peri-urban context. The study's objective is to determine the better adaptor amongst these two study groups in online distance learning in higher education systems in an Indian peri-urban context. Design/methodology/approach: The investigation was carried out prior to COVID-19 and during the pandemic. The inquiry is triangulated in nature with a disproportionate stratified random sampling approach used to pick 312 post-graduate students (STEM = 135 and non-STEM = 177) from a peri-urban higher education institute in West Bengal, India, using the "Raosoft” scale. Given the prevailing social distance norms, 235 samples of respondents from 312 students were evaluated via telephonic/online interviews during the COVID-19 period. The data were analysed using SPSS 22. Findings: This study's investigations reveal that the STEM respondents have better digital profiles, better basic computing and Internet knowledge and greater digital usage for academic purposes before the pandemic times than the non-STEM group. This prior digital exposure has enabled the STEM group to cope with regular online distance education during the pandemic more quickly than the non-STEM group, as evidenced by their regular attendance in online classes and their greater awareness of its utilitarian role than the other group. Originality/value: The study offers a way forward direction to evolve with more inclusive online distance learning in peri-urban Indian regions. © 2023, Aakash Ranjan Das and Asmita Bhattacharyya.
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Supramolecular architectures decorated with various conjugated building blocks give rise to numerous luminescent frameworks with interesting chemical and photophysical properties. The luminescence properties of these MOFs help global researchers achieve success in the field of recognition applications of MOFs for the detection of various targeted toxic analytes. In this regard, different MOF-based materials, along with their different host-guest recognition strategies, have been developed, emphasising selective and sensitive natures towards a particular analyte, which indeed helps in protecting our environment. The present review article discusses state-of-the art progress based on (i) advancement of electrochemical MOF-based sensors, (ii) detection of various waterborne pollutants & VOCs, and (iii) recent progress of MOFs in biomedical sciences, with regard to cancer & SARS-CoV-2, along with the advantages and current challenges to combat SARS-CoV-2 for the clinical purposes. Herein, detection of particular analytes along with their interactive mechanisms have been precisely described;however, it needs to be noted that detailed host-guest mechanistic revelations is not the topic of discussion in the present exploration. In this review, we have covered almost the last 14 years (2008-2022) of research on MOFs in the various sensing platforms. In a nutshell, the luminescent MOFs, along with their extraordinary applicability in the domains of chemical, biomedical and environmental arenas as welfare tools, have been studied in the present review article. © 2023 The Royal Society of Chemistry.
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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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Viral, Parasitic, Bacterial, and Fungal Infections: Antimicrobial, Host Defense, and Therapeutic Strategies highlight diverse types of infections, including viral, bacterial, parasitic, fungal, and the therapeutic efficacy of antibiotics, antivirals, antifungals and other medications, nutraceuticals, and phytotherapeutics. This book addresses the molecular, pathophysiological, and cellular pathways involved in the process of infection. It also examines the host defense mechanisms modulated by innate and adaptive immunity. The book starts off with an introduction, which includes etiology, pathophysiology, and diagnosis of infections. It then goes on to cover a wide spectrum of salient features involved in viral, bacterial, parasitic, and fungal infections and effective therapeutic strategies. In addition, there is a complete section of eight chapters elaborating the detailed aspects of COVID-19 infections, Mucormycosis, Omicron, and strategic vaccines and therapeutics. The book further goes on to discuss novel antibiotics, vaccines, bromhexine, boron compounds, phytotherapeutics, and aspects on boosting immune competence. Contributed by experts in the fields of viral, parasitic, bacterial, and fungal infections, the book comprehensively details the various types of infections such as herpes and COVID-19, their molecular mechanisms, and treatment strategies for those engaged in the research of infectious diseases. © 2023 Elsevier Inc. All rights reserved.
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The COVID-19 virus has fatal effect on lung function and due to its rapidity the early detection is necessary at the moment. The radiographic images have already been used by the researchers for the early diagnosis of COVID-19. Though several existing research exhibited very good performance with either x-ray or computer tomography (CT) images, to the best of our knowledge no such work has reported the assembled performance of both x-ray and CT images. Thus increase in accuracy with higher scalability is the main concern of the recent research. In this article, an integrated deep learning model has been developed for detection of COVID-19 at an early stage using both chest x-ray and CT images. The lack of publicly available data about COVID-19 disease motivates the authors to combine three benchmark datasets into a single dataset of large size. The proposed model has applied various transfer learning techniques for feature extraction and to find out the best suite. Finally the capsule network is used to categorize the sub-dataset into COVID positive and normal patients. The experimental results show that, the best performance exhibits by the ResNet50 with capsule network as an extractor-classifier pair with the combined dataset, which is composed of 575 numbers of x-ray images and 930 numbers of CT images. The proposed model achieves accuracy of 98.2% and 97.8% with x-ray and CT images, respectively, and an average of 98%. © 2023 Wiley Periodicals LLC.
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As the corona virus can mutate and due to other scientific factor associated to it, experts believe that COVID-19 will remain with us for decades. Therefore, one has to keep social distancing measures. Accepting the pandemic situation, the paper presents a mechanism for detecting violations of social distancing using deep learning to estimate the distance between individuals to diminish the influence of COVID-19. The focus of this paper is to understand the effect of social distancing on the spread of COVID-19 by using YOLOv3 and Faster-RCNN and proposes IFRCNN (improved faster region convolution neural network). The proposed method IFRCNN is checked on a live streaming video of pedestrians walking on the street. This paper keeps the live updates of the recorded video along with social distancing violation records on a location, so how many people in a location are maintaining social distancing. Updates will be stored in a cloud-based storage system and any organization or firm can get live updates of that location in their digital devices.
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A vaccine is a material administered to an individual to boost their immune system's resistance against infection. Diseases that can be prevented by vaccination can be controlled and eradicated with proper vaccine handling and storage. It is crucial to formulate and deliver stable, effective and safe vaccines. Since vaccines are intricate biological products so any kind of temperature fluctuation can result in reduction of their effectiveness. To prevent this, cold storage facility is set up;refrigerators, thermometers and storage protocols are in place. The main vaccines distributed for COVID in India are Covishield and Covaxin. In order to maintain a cold chain supply for these vaccines, they must be transported and stored at a regulated temperature in accordance with the manufacturer's guidelines. The end-to-end supply chain for COVID-19 vaccines must adhere to specific cold chain standards from manufacturing to distribution in warehouses and healthcare facilities. Audits for cold chains and temperature monitoring should be performed regularly on the vaccine lots to ensure proper distribution practices are adhered. The present study focuses on the good distribution practice and storage of vaccines. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
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Even though there has been a significant amount of research conducted on the origin of SARS-CoV-2 (SARS-CoV-2) aetiology, very little is known about the disease's long-term effects till the date(1). As with previous viral infections, SARS-CoV-2 may raise the risk of developing cancer by altering tumour suppressor genes and expression of various pro-oncogenic proteins. We will conduct a comprehensive review of the available research on it,because an infection with SARS-CoV-2 might have a potential to cause cancer in the long run, researchers are looking at the likelihood of this happening, precisely on likeliness of occurring oral and pulmonary malignancies in this work. We speculate that one of these long-term impacts may be the SARS-carcinogenic CoV-2 virus. The viral proteins Nsp-15 or Nsp-3 are hypothesised to have pro-oncogenic effects when they interact with two essential tumour suppressors, pRB and p53 inhibitors. Copyright © 2023, Ibn Sina Trust. All rights reserved.
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The pandemic that started in 2019 in Wuhan caused a vast number of deaths worldwide due to the absence of effective therapy against SARS-CoV-2. The present study investigates the interaction of AMP with viral protein and host receptors. We screened plant-derived antimicrobial peptides (AMP) from the docking web server with the help of PDB ID. We selected five anti-microbial peptides based on their antiviral and physiological activities. The interaction of anti-microbial peptide and Mpro was analyzed using the HADDOCK web server. The results revealed that the minimum Z-score was obtained by the 6LU7-1N4N complex followed by 6LU7-1GPS docked complex. The docking results showed the interaction potency of AMP with 6LU7. The dynamic simulation study of 100ns was performed to check the stability of the docked complexes of AMP and 6LU7. From the stable and positive results of dynamics studies, we can conclude that these selected AMPs have immense potential to be used as therapeutic agents for the treatment of disease. © 2022 The authors and IOS Press.