Smart Nanobiosensing for COVID-19 Diagnosis

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.

Sars-Related Corona Viruses-A Challenge

SARS (Severe acute respiratory syndrome)-related corona viruses was first of all discovered 18 years ago in china from bats. Previously some study shown that bats are infected to animal kingdom and from animal this virus spread in human. As per report of identification and characterization of novel corona virus which is responsible for epidemic of acute respiratory syndrome in human beings. First of all this protein of novel SARS are seen in Wuhan city of, China in January 2020.Copyright © 2019, Advanced Scientific Research. All rights reserved.

Homefluencers' endorsement of millennial consumers' purchase intention in new normal

PurposeThe purpose of this study is to examine how "homefluencers" sponsored posts on millennial consumers' purchase intention in the international marketing sphere can be impacted in the new normal by drawing on source credibility, parasocial interaction (PSI) and persuasion knowledge model (PKM) theory.Design/methodology/approachThis research applies structural equation modeling (SEM) and mediation analysis as the data analysis method using non-probability purposive sampling of a total of 217 local millennial Instagram and Facebook users, who have followed homefluencers sponsored posts in fashion-beauty, yoga-fitness and food sectors.FindingsBased on hypothesis testing, advertising recognition strongly mediates purchase intention with the indirect effects of expertise and trustworthiness than attractiveness.Research limitations/implicationsThis research extends the international marketing literature on source credibility, PSI, PKM and purchase intention theory in the new normal by proposing "Homefluencer's Endorsement Model for Purchase Intention" (HEMPI). Specifically, the mediating role of ad recognition of homefluencers sponsorship disclosure (#paidad, #sponsored), positively affects "change-of-persuasion meaning" on Instagram and Facebook, where research is rare.Practical implicationsThis research provides valuable suggestions for global brand owners, consumers and authorities of Instagram and Facebook to consider post-COVID consumer behavior highlighting homefluencers sponsored collaboration.Originality/valueThe authors have contributed to the use of the source credibility model and PSI to identify the antecedents in determining how the homefluencer's effective sponsorship disclosure can positively activate ad recognition on millennial consumers' purchase intention in a crisis period from an international standpoint with the practical implications in post-COVID.

Role of AI and ML in Epidemics and Pandemics

The healthcare industry, as well as business and society, have been revolutionized by Artificial Intelligence (AI) and Machine Learning (ML). Currently, microbiology, biochemistry, genetics, structural biology, and immunological concepts have all seen significant advances. In contrast, the fields of bioinformatics have seen considerable expansion in order to handle this massive data influx. The field of bioinformatics, which tries to use computational methods for a better understanding of biological sciences, sits at the crossroads of data science and wet lab. Several innovative databases and computational techniques have been proposed in this sector to advance immunology research, with many of them relying on artificial intelligence and machine learning to anticipate complicated immune system activities, such as epitope identification for lymphocytes. Models based on machine learning skilled on specific proteins have provided inexpensive and quick-to-implement strategies for the discovery of effective viral treatments in the recent decade. Given a target biomolecule, these models can predict inhibitor candidates using structural data. The emergence of the coronavirus COVID-19 has resulted in significant network data traffic and resource optimization demands, rendering standard network designs incapable of dealing calmly with COVID-19's consequences. Researchers are encouraged by the use of Machine Learning (ML) and Artificial Intelligence (AI) in previous epidemics, which offers a novel strategy to combating the latest COVID-19 pandemic. © 2023 Scrivener Publishing LLC.

COVID 19 Prediction Using Chest X-Ray And CNN Hyperparameter Optimizer

According to mid-June 2020, the abrupt escalation of coronavirus reported widespread fear and crossed 16 million confirmed cases. To fight against this growth, clinical imaging is recommended, and for illustration, X-Ray images can be applied for opinion. This paper categorizes chest X-ray images into three classes- COVID-19 positive, normal, and pneumonia affected. We have used a CNN model for analysis, and hyperparameters are used to train and optimize the CNN layers. Swarm-based artificial intelligent algorithm - Grey Wolf Optimizer algorithm has been used for further analysis. We have tested our proposed methodology, and comparative analysis has been done with two openly accessible dataset containing COVID- 19 affected, pneumonia affected, and normal images. The optimized CNN model features delicacy, insight, values of F1 scores of 97.77, 97.74, 96.24 to 92.86, uniqueness, and perfection, which are better than models at the leading edge of technology. © 2022 IEEE.

Ageing and Technology – Systematic Review of the Merger of Ageing & Technology in the New Normal Era

Technology is massive support in the current times in living our lives. Technology surrounds every day and every bit of our daily lives. The technological advancements and their acceptance also justify the need for a complete understanding across generations. We live in a society where older people are also an integral part of society. When we check them and their needs, especially during the COVID-19 pandemic and in these new normal times, we can see that their need and quick adaptation to the surrounding seem more obligatory. Therefore, the merger of technology with age is critical. Assessing their need and looking at their well-being and better living becomes the priority for every stakeholder living in our society. Technology comes as a powerful, liberating way of backing older adults. This research follows a systematic review of journal papers published between 2000–2022 and tries to check how other researchers looking for this merger of technology and ageing are reporting in this synthesis. A detailed scientific plan is followed in extraction and analysing the seminal work done in this area in the considered time period. The research identified and reported many diverse areas where researchers worked on understanding the merger between technology and ageing. The study reported the critical areas of the technology amalgamation with age and identified the gaps in the existing theories where the future direction of work can take place. The study also highlighted specific vital takeaways for the practitioner that can be considered the next big step in this advanced technology adoption in this new normal era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Deep Features for COVID-19 Detection: Performance Evaluation on Multiple Classifiers

Humanity has faced the greatest difficulties in recent years in COVID-19. These diseases are caused by significant alveolar damage and progressive respiratory failure. To address this issue, healthcare facilities needed rapid testing methods to identify COVID-19 patients and treat them immediately. In this paper, we developed a rapid testing strategy using machine and deep learning architecture with three different categories of chest x-ray images, such as COVID-19, normal, and pneumonia, were considered to identify the COVID-19 affected images. It is very difficult to diagnose COVID-19 from the pool of chest x-ray images, as pneumonia and COVID-19 affected x-ray images closely resemble each other. For this issue, feature extraction plays an important role. Here we considered deep features which were extracted from deep learning models such as VGG19 and InceptionResnetV2. These deep features were classified using different machine learning algorithms. It was observed that 96.81% accuracy was obtained after classification using MLP. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Scope of machine learning applications for addressing the challenges in next-generation wireless networks

The convenience of availing quality services at affordable costs anytime and anywhere makes mobile technology very popular among users. Due to this popularity, there has been a huge rise in mobile data volume, applications, types of services, and number of customers. Furthermore, due to the COVID-19 pandemic, the worldwide lockdown has added fuel to this increase as most of our professional and commercial activities are being done online from home. This massive increase in demand for multi-class services has posed numerous challenges to wireless network frameworks. The services offered through wireless networks are required to support this huge volume of data and multiple types of traffic, such as real-time live streaming of videos, audios, text, images etc., at a very high bit rate with a negligible delay in transmission and permissible vehicular speed of the customers. Next-generation wireless networks (NGWNs, i.e. 5G networks and beyond) are being developed to accommodate the service qualities mentioned above and many more. However, achieving all the desired service qualities to be incorporated into the design of the 5G network infrastructure imposes large challenges for designers and engineers. It requires the analysis of a huge volume of network data (structured and unstructured) received or collected from heterogeneous devices, applications, services, and customers and the effective and dynamic management of network parameters based on this analysis in real time. In the ever-increasing network heterogeneity and complexity, machine learning (ML) techniques may become an efficient tool for effectively managing these issues. In recent days, the progress of artificial intelligence and ML techniques has grown interest in their application in the networking domain. This study discusses current wireless network research, brief discussions on ML methods that can be effectively applied to the wireless networking domain, some tools available to support and customise efficient mobile system design, and some unresolved issues for future research directions.

DeepGLSTM: Deep Graph Convolutional Network and LSTM based approach for predicting drug-target binding affinity

Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them for combating the new disease. We propose a novel architecture DeepGLSTM, which is a Graph Convolutional network and LSTM based method that predicts binding affinity values between the FDA-approved drugs and the viral proteins of SARS-CoV-2. Our proposed model has been trained on Davis, KIBA (Kinase Inhibitor Bioactivity), DTC (Drug Target Commons), Metz, ToxCast and STITCH datasets. We use our novel architecture to predict a Combined Score (calculated using Davis and KIBA score) of 2,304 FDA-approved drugs against 5 viral proteins. On the basis of the Combined Score, we prepare a list of the top-18 drugs with the highest binding affinity for 5 viral proteins present in SARS-CoV-2. Subsequently, this list may be used for the creation of new useful drugs. Copyright © 2022 by SIAM.

Ageing in the unusual times and adapting to the new normal – an Indian perspective

Purpose: The COVID-19 pandemic has changed the way of our living. Social and physical distancing has become an inevitable part of our life. Although the younger counterpart can adapt to the situation quickly, it is extremely difficult for the elderly (60 years and above) who are locked in their homes to manage this situation on their own, especially those who live alone. In this scenario, how can we help the elderly who are caught at home? How will they again fight with social and physical distancing and the pandemic? The purpose of the study is to acknowledge the fact that the elderly need urgent consideration and attention and suggested ways to adapt to the “new normal.” Design/methodology/approach: The viewpoint discussed the diverse ways through which the elderly can be motivated to adapt in the current situation in a pandemic hit environment. Findings: The viewpoint highlighted in detail the prospects, challenges and considerable steps that need to be taken by the important stakeholders (practitioners and policymakers) in our society to support the elderly. Originality/value: The viewpoint emphasised on the need of creating separate policies and implementation of the same at various levels. Although the government does have acts and policies for the welfare and maintenance of the elderly, they should now also re-think of a more concrete and sustainable policy to take care of the elderly, especially during a crisis. Ageing is inevitable, the reflections of this study will also allow the families and the society to cushion the elderly in their families and around them. This work will also create an opportunity for the practitioners to work for this under-explored community and look forward to catering to their needs. © 2022, Emerald Publishing Limited.

COV-Doctor: A Machine Learning Based Scheme for Early Identification of COVID-19 in Patients

The COVID-19 pandemic brought about by the SARS-CoV-2 keeps on representing a critical danger to worldwide wellbeing. The most approved indicative test for Coronavirus, utilizing reverse transcriptase-polymerase chain response (RT-PCR) kit has deficiency sometimes in low-income countries. This adds to expanded disease rates and defers basic preventive measures. Successful screening empowers fast and effective analysis of Coronavirus and can relieve the burden on medical care services. Machine learning (ML) models are being used to anticipate the presence of COVID-19 in patients to support clinical staff worldwide, particularly with regards to restricted medical services assets. In this research, machine learning models have been developed to identify COVID-19 in the early stage of sickness using the information of symptoms and exterior activities of patients. Among the four machine learning classifiers, the Decision Tree and Extreme Gradient Boosting (XGBoost) performed equally better with 98% of accuracy, precision, and recall. The feature importance scores have been calculated also to understand the feature’s impact on the development of the machine learning model. The proposed work has outperformed the existing works with better execution. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Long-term follow-up of adult hematopoietic stem cell transplant recipients diagnosed with COVID19

Background: Long-term complications of COVID-19 in hematopoietic stem cell transplant (HCT) recipients are unknown. Recent studies have described short term outcomes of COVID19 infection post allogeneic (allo) and autologous (auto) HCT. In this study we provide long term follow-up of the outcomes of COVID19 infection in allo and auto HCT recipients. Methods: We performed a retrospective study of adult patients who have received allo or auto HCT and were subsequently diagnosed with COVID-19 infection between March-December 2020. We summarized patient characteristics, laboratory and treatment data related to COVID-19 infection in these patients. Results: In this study, we provide long-term follow-up of over 7 months. Fifteen patients were identified for inclusion (allo n = 12, auto n = 3). Median follow-up was 7.8 months (range 1.9-10.7) for surviving patients. Median age at COVID-19 diagnosis was 55 years (range 24-77). Most patients were > 1 year out from transplant (allo n = 10, auto n = 1, 73%). Two patients (allo n = 1, auto n = 1, 13%) had undergone transplant within the preceding month. Most allo patients (n = 11, 73%) had received myeloablative conditioning. At the time of COVID-19 diagnosis, 9 allo patients (75%) were on immunosuppression (IS) (n = 7 for chronic graftversus-host-disease (GVHD);n = 2 for GVHD prophylaxis). Eleven patients (73%) required hospitalization (allo n = 9, auto n = 2). Per the National Institutes of Health definitions of COVID-19 illness severity, 3 patients had critical disease (allo n = 2, auto n = 1, 20%), 5 severe (allo n = 5, 33%), 3 moderate (allo n = 2, auto n = 1, 20%), and 4 mild (allo n = 3, auto n = 1, 27%). All patients with chronic GVHD required hospitalization. Two patients died (allo n = 1, auto n = 1, 13%)-both had critical COVID-19 infections, were > 65 years old, > 3 years out from transplant, and had significant comorbidities. The allo patient was receiving prednisone < 1 mg/kg for chronic lung GVHD at COVID-19 diagnosis. Two allo patients developed either acute GVHD or chronic GVHD exacerbation within 3 months of their infection. One patient developed biopsy-proven acute GVHD (max grade III) 3 weeks after her infection and another patient developed a severe exacerbation of chronic GVHD within 3 months-both continue to require multi-modal IS. The remaining 7 patients with chronic GVHD have been maintained on either stable or tapered IS. Conclusions: Given the effect of COVID-19 infection, its impact on HCT recipients is important to define. The majority of HCT patients who contracted moderate-critical COVID-19 infections in our study were either on IS or had significant comorbidities. Our observational data points to the importance of long-term follow-up in HCT patients. Future studies are needed to delineate whether there is a relationship between COVID-19 infection and GVHD development or exacerbation. The role of vaccination in HCT recipients remains to be explored.

Assessment of water quality of river ganga during covid-19 lockdown

We have done a qualitative and quantitative analysis of Ganga River water in two areas namely Palta and Diamond Harbour, in the state of West Bengal, India. Anthropogenic activity is very high in these regions. Restriction of human activity near river basins due to the prolonged COVID-19 lockdown has brought remarkable changes in the environment. A comparison of the pre-lockdown period and the lockdown period was done. The study covered the years from March 2019 to May 2020. Results demonstrate improvement in surface water quality of River Ganga, during the lock-down period as there was less anthropogenic activity. The water quality test revealed that tur-bidity has reduced to <94% during the lockdown. River Ganga was one of the polluted rivers, unfit for a bath but physicochemical properties like turbidity, total suspended solids, and total dissolved solids have improved enormously during the lockdown. The chemical oxygen demand, biochemical oxygen demand has changed from 12 and 3 mg/L to <6 and 1.2 mg/L, respectively. Consecutively, dissolved oxygen level has increased from 6 to 12 mg/L. Low total coliform and fecal coliform counts indicated improvement in the bacteriological quality of water. The results of the present investigation establish a significant improvement in water quality. © 2021 Desalination Publications. All rights reserved.

Economy and Unemployment Due to COVID19: Secondary Research

The coronavirus pandemic has hit a hard blow on the world economy and employment rates. Countries like India, with a high population, have faced major economic degradation and high unemployment rates. Most of the countries are expected to face a major economic recession as most internal and external economic activities have ceased to operate due to the worldwide lockdown and quarantine measures being taken. This might affect the socioeconomic relationships between countries. It has also affected the economically challenged sector of the world largely. In India, about 41 lakh people lost their jobs, including several migrant workers. Several G7 countries have ensured subsidies as the jobless rates vary from 30million in the US to 1.76 million in Japan. © 2021, IFIP International Federation for Information Processing.

Saving the savior in COVID19 pandemic: Face masks

Using masks for self-protection has a long history. There are records of the use of masks ranging as far back as the Roman era to Medieval Europe, where masks were used as protective devices. During many festivities, masks were used as a fashion statement. Gas masks were commonly used during the world wars. As pollution started growing, people started using pollution masks. 2020 might be the only era when the entire human population has been forced to wear masks to protect themselves and to protect others. As expected, the healthcare workers, who are leading the fight against the COVID19, are the ones who are most at the need of these devices. Unfortunately, there is a shortage of personal protection equipment all over the world. Hence, we must understand their properties and strengths, and hence that we can achieve maximum benefits from the limited resources.