Bromhexine: Into the spotlight

Bromhexine is a synthetic molecule derived from the natural product vasicinone isolated from Adhatoda vasica, an important ayurvedic medicinal plant. Bromhexine was validated as an active ingredient for pharmaceutical use at Boehringer Ingelheim and was introduced in 1963. Bromhexine is widely prescribed as OTC mucoactive drug to treat a broad range of respiratory diseases. The mechanism of action of bromhexine is primarily associated with its secretomotoric effect through expectorating the mucus out of the lungs with the modification in the physiochemical features of mucus. Bromhexine has been recently in the spotlight for its possible application as a repurposing drug candidate for the management of COVID-19 patients. In this chapter, we have provided a perspective for the application of bromhexine in COVID-19 patients based on the safety assessment, mechanistic findings, and reports from many clinical trials around the world. The chapter also tried to shed light on the therapeutic aspects of bromhexine in different disease conditions. © 2023 Elsevier Inc. All rights reserved.

Prioritizing COVID-19 Vaccine Delivery for the Indian Population

As India has successfully developed a vaccine to fight against the COVID-19 pandemic, the government has started its immunization program to vaccinate the population. Initially, with the limited availability in vaccines, a prioritized roadmap was required to suggest public health strategies and target priority groups on the basis of population demographics, health survey information, city/ region density, cold storage facilities, vaccine availability, and epidemiologic settings. In this paper, a machine learning-based predictive model is presented to help the government make informed decisions/insights around epidemiological and vaccine supply circumstances by predicting India's more critical segments that need to be catered to with vaccine deliveries as quickly as possible. Public data were scraped to create the dataset;exploratory data analysis was performed on the dataset to extract important features on which clustering and ranking algorithms were performed to figure out the importance and urgency of vaccine deliveries in each region. Copyright © 2022 IGI Global.

Altmetric Scores in Conservation Science have Gender and Regional Biases

There is a growing view in conservation science that traditional ways to evaluate publications, researchers, and projects are too slow. This has led to a rise in the use of altmetrics, which are metrics based on social media data, news pieces, blogs, and more. Here we examine altmetric data linked to nearly 10,000 papers published in 23 conservation journals, exploring five issues that represent some of the challenges associated with using social media data in evaluating conservation. We discuss whether social media activity reflects meaningful engagement, and how easily individuals can manipulate scores by using bots or simply through active personal networks or institutional promotion services. Our analysis shows a highly skewed distribution of altmetric scores where most papers have such low scores that the scores likely convey little meaningful information. Examining scores that would be considered meritorious, we find that papers where the first author was male have higher scores than papers led by a woman, suggesting a gender bias in altmetric scores. Finally, this data set reveals regional differences that correspond with access to different social media platforms. Metrics, like altmetrics, may have a role to play when making rapid evaluations. However, such metrics should only be used after careful deliberation and should not be influenced by institutions looking for shortcuts, by companies looking to advance profits, or by individuals seeking to promote themselves, rather than generating meaningful engagement in scholarship and conservation action. Scholarly and conservation activities should be judged on the quality of their contributions, which will require the input of experts and direct contact with impacted communities.

Implications of novel Adenovirus-Host interactions in Thrombosis with Thrombocytopenia Syndrome

In this study we investigated a link between adenovirus-based vaccines, deployed to fight the SARS-CoV-2 pandemic, and lifethreatening thromboembolisms after vaccination. Post-marketing surveillance showed that, following vaccination, Vaxzevria (ChAdOx1 based, AstraZeneca) and Jcovden (Adenovirus type 26 based, Johnson & Johnson) are associated with reduced platelet counts (thrombocytopenia) and blood clots (thrombosis) in some individuals. This extremely rare condition, with a rate between 1:50,000 - 1:350,000 cases per vaccinated individual, is above background rates of thrombosis in the population and can lead to fatal ischemic events including cerebral venous thrombosis, intracranial haemorrhage, and pulmonary embolism. It has been termed vaccine induced thrombotic thrombocytopenia (VITT) or thrombosis with thrombocytopenia syndrome (TTS). Heparin induced thrombocytopenia (HIT) is another condition with a similar clinical presentation to TTS. In HIT, immunoaggregates are formed due to the presence of strong anti-selfantibodies directed against Platelet Factor 4 (PF4). When similar anti-PF4 antibodies were detected in TTS patients, we investigated whether there could be a link between the adenovirus vectors used in the vaccines and PF4. This study demonstrates a direct interaction between adenovirus capsids and PF4 using surface plasmon resonance. We then utilized an integrative structural biology workflow including cryo-electron microscopy and molecular dynamics to characterize and demonstrate the mechanism of this interaction. These results demonstrate a previously unknown adenovirushost interaction and provide critical clues as to the underlying mechanism which causes TTS, including how these pathogenic anti-PF4 antibodies may be induced. We are therefore able to present a hypothesis as to the route of pathogenesis in TTS.

Face Mask Detection using CNN

Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person’s face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security. © 2021 IEEE.

Electrospun Nanocomposite Materials for Environmental and Energy Applications

Environmental and energy issues are the two major concerns that are assigned among researchers all over the world. Researchers have introduced many versatile materials of polymers, metals, and metal-oxides matrix nanocomposites in various dimensional structures. Among different nanostructures, one-dimensional nanofibers have captured attention due to their large surface area, directional transmission, interface effect. Electrospinning is a versatile technique for generating continuous nanofibers with tunable chemical composition, diameter, porosity, and its versatility to synthesize a wide variety of materials from polymer to metal oxide. In this chapter, we have focused on the development of semiconductor and polymer-metal composite nanofiber for photocatalysis applications for dyes degradation and water splitting H2 production. Engineering the junction and interface charge transfer is a significant method to promote charge separation and migration during the photocatalysis process. In the present Coronavirus disease 2019 (COVID-19) pandemic situation, disease transmission prevention is a major task. The demand for a good face mask and the air filter is increased. Afterward, we have discussed the latest development of nanocomposite membranes for the air filter and face mask. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Academic Incentives Should Not Promote the "Extinction of Nature Experience"

Evidence suggests that a decline in people's exposure to nature corresponds to decreasing support for nature-a phenomenon we call extinction of nature experience. Here, we evaluate three current trends in conservation research and consider if they contribute to a decrease in exposure to nature. We suggest that while using sensors, algorithms, technocentric thinking, conducting meta-analyses, and taking more lab-based approaches all have significant potential to advance conservation goals, they lead to researchers spending less time in the field and an extinction of nature experience. A reduction of researcher field time will mean fewer local field assistants are hired and trained;lower engagement of researchers with ground realities;and a rift in conservation research, planning, and implementation. We suggest that the field of conservation science should balance how it allocates time and rewards to field versus non-field components. If we are not careful, we will select researchers that are distant from the biodiversity itself and the communities that are affecting it locally. Since the pandemic began many researchers were unable to go to their field sites and if care is not taken, the pressures that promote the extinction of nature experience may be promoted by institutions in a post-COVID-19 world.

Forecasting on Global Dynamics for Coronavirus (COVID-19) Outbreak Using Time Series Modelling

The spreading and Development of COVID-19 have analyzed which was first officially reported in Wuhan City, in December 2019. Firstly the data have explored in terms of information and quality and after that, the data have cleaned and gone through with feature engineering. Analyzed different types of machine learning-based prediction methods, namely Linear Regression, ARIMA, and SARIMA on the spread of COVID-19 in different regions all over the world. In the end, It has been concluded with the best machine learning model among them for COVID-19 spread forecasting based on theoretical and results in analysis. And also we have discussed that how deep learning can be considered with data limit problem in order to improve the result more dynamically with combination and comparisons of state-of-art approaches for time series problems. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A comparative analysis of opinions and sentiments on clean India campaign and sustainability goals of 2030

Human are blessed with natural intelligence. Artificial Intelligence can help human minds to make a best usage of machines to handle huge amount of data with accuracy and precision. AI has a widespread application in 21st century. Opinion mining is an application of artificial intelligence. The opinions expressed in social media can be extracted using python which can be used as an input for various machine learning algorithms to identify many patterns which can help policy makers to make effective policies. Clean India Campaign started in India with a set of goals to be achieved. Sustainability goals of 2030 given by United Nations puts light on many important aspects which need immediate attention in the next 9 years. Current pandemic Covid-19 has also triggered the necessity behind putting immediate attention for a better tomorrow. Without proper awareness programs, brainstorming knowledge cultivation, orienting minds towards the "what-why-where"aspects of sustainable growth in each sphere of life, aligning industrial development and digital era towards sustainable industrial development in digital era, sustainable economy, sustainable care of each natural resource;it is not easy to accomplish the sustainability goals of 2030 given by United Nations.This work emphasizes on the case study conducted as an initiative to motivate future policy makers to be aware of the different dimension of 2030 United Nations Agenda and the clean India campaign to take initiatives as a professional through the skills learned focusing on India. Realizing Individual social Responsibility can make a big difference in the planning and implementation of the goals and missions. Swachch Bharat Abhiyan (Clean India Campaign) started Swachch Bharat Mission-Urban (SBM-U) with a few objectives to make India Clean.This work has proposed two phases for analyzing opinions. This research have provided a methodology to apply AI to improve the opinion mining. The conventional opinion analysis is limited by reachability but the automated opinion analysis can be scaled up using artificial intelligence based applications. The uniqueness of the work lies in its focus on 'one-three verticals' in phase 1 of the methodology. Many prominent regions of India are considered as a part of the study. It helps us to provide a clearer picture across different regions of India. It also provide an avenue to list tasks to be done for each region and a set of ways which could be adopted by the future professionals and current stakeholders of higher education institute. Phase 2 focusses on more number of opinions collected from across the globe through digital platforms. © 2021 Author(s).

Healthcare Technology for Reducing the Risk and the Spread of COVID-19 Pandemic and Other Epidemics

As the very first big outbreak in our era, COVID-19 offers an outstanding incentive to legislators, and regulatory authorities should find procedural viability, moral validity, and usefulness of implementation in the time of new healthcare technologies. This chapter addresses the outbreak of coronavirus and how digital healthcare technologies can help navigate and resolve COVID-19 and future epidemics. As the coronavirus pandemic (COVID-2019) spreads, technological advancements and efforts to control the disease, manage clients safely, and promote teamwork among overburdened health care practitioners designing innovative, efficient vaccines would be critical. This review looks at how various healthcare scientific sectors are assisting in the fight against the pandemic, as well as how innovative implementations are being used to treat or cure illness. An analysis of the technical landscape in the COVID-19 sense enables some tentative reflections about the conditions of technical involvement in fighting this once-in-a-century pandemic. Firstly, unlike previous global health crises, it seems to turn people from monitoring artefacts and epidemiological analysis through information creation topics via self-tracking, information sharing, and digital information flows. Furthermore, even if many of such innovations were not implemented, in a previous medical crisis background, their extensive usage on a world basis raises concern about the extent of mobilising domestic spying techniques on racial equality and also worries about government agencies retaining higher monitoring rates long after the end of the pandemic. Within the recent epidemic framework, advanced technical technologies in data analysis and remote monitoring of the urgent laws that require the immediate termination of human rights and the approval of medical equipment and vaccinations by fast-tracking processes have been implemented. Everyone knows that digital healthcare technologies are progressively being embraced in this field for fighting against the COVID-19 pandemic. This chapter produced an overview to explain all the efforts being made towards this pandemic in digital healthcare tech. Healthcare centres throughout the world are transitioning to the new technologies listed below to help ease their workload, either by increasing the speed diagnostics or allowing physicians to monitor patients who have been virtually quarantined. © 2022, Springer Nature Switzerland AG.

Online Tools and Antiviral Databases for the Development of Drugs Against Coronaviruses

The current crisis of coronavirus pandemic has created an urgent need for readily available scientific information to the researchers, students, professionals, and journalists. The scientific information for the research is costly, and most of the universities and research institutes cannot afford to subscribe to all the coronavirus-related journals/articles during this crisis time. In order to expedite the process of vaccine development and discovery of anti-COVID drugs, most of the pharmaceutical companies, research institutes and publishers are playing a key role and working on war footing to get either a vaccine or an anti-COVID drug as early as possible. The collective efforts are required from everyone in this testing time of the corona crisis. To provide our contribution to the scientific community, we have made here an attempt to give an overview of some of the tools and resources freely available that probably provide some insights in data mining and screening of novel lead molecules toward coronavirus. We have collected and compiled the information of open access online tools and antiviral databases essential for the discovery and development of corona vaccine and anti-COVID drugs. The open access tools include Open Educational Resources (OERs), Google cloud, online prediction server, web-based viewer, etc., while the antiviral databases include libraries of synthetic and untested compounds, antiviral drug databases, antiviral peptides, etc. The information presented in this chapter will help the researchers to use them directly in their projects of coronavirus drug discovery.

Anesthetic Management in an Infant with Dandy-Walker Syndrome Presenting with Acyanotic Heart Disease and Hydrocephalous Post-COVID-19 Recovery: A Rare Experience

We report a case of hydrocephalus with Dandy-Walker malformation in a 2-month-old girl child recently recovered from COVID-19. The child was detected to have acyanotic heart disease with left-to-right shunt and severe pulmonary arterial hypertension during the preoperative evaluation process for ventriculoperitoneal (VP) shunt placement. We share our experience of the perioperative management for pulmonary artery banding (PAB) and patent ductus arteriosus ligation as a part of staged cardiac corrective surgery, followed by VP shunt to relieve hydrocephalus in the single setting. Our management was focused on the preservation of the normal cerebral and cardiac physiology to prevent rise in intracranial pressure and pulmonary artery pressure. A multidisciplinary team, consisting of cardiac- and neuroanesthesiologists and cardiac and neurosurgeons, was involved in management of the case. Diligent maintenance of airway, stable hemodynamics, meticulous ventilation, along with postoperative ICU management helped in the successful outcome of this unique case.

Deep Learning for COVID-19

The devastating outbreak of the SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus) also known as COVID-2019 has succeeded in introducing the danger to the worldwide living. Now COVID-19 is among the main potentially deadly issues in the world. Rapid and precise identification of COVID-19 infection is important to diagnose, make informed assumptions, and assure that patients receive care that aims to save people’s health and life. The entire community is making enormous endeavors to tackle the spreading of such a horrible epidemic in forms of communications true, economy, information sources, safety equipment, existence-risk treatments, and many on this and many other tools. Coronavirus triggers a vast range of viral disease;however, it is a virus of the kind RNA which can affect all humans and animals. Coronavirus is now identified in this chapter uses a form of deep learning which is a sub-branch of artificial intelligence. This chapter suggests making use of the deep learning algorithms with such a view to recognizing its daily incremental behavior and predicting the potential accessibility of COVID-2019 throughout civilizations using real-time data knowledge. COVID-19 Quick diagnosis and It is necessary to identify high-risk patients with the worst diagnosis for early treatment and optimization of medical services. And the algorithm of deep learning helps to do that and helps to combat the COVID-19 and the deep learning algorithms are more time saving;less expensive;easy to operate. COVID-19 research through deep learning involves the patient’s x-rays of the lungs and the fundamental concept is to identify the ultrasound as impaired or usual COVID. In general, the issue is a collection of identification algorithms whereby we identify Standard normal v/s COVID-19 cases. There are several benefits and drawbacks to utilizing Deep Learning to solve these circumstances. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Molecular docking study to identify potent fungal metabolites as inhibitors against SARS-CoV-2 main protease enzyme

Severe acute respiratory syndrome (SARS) is a viral respiratory disease caused by a SARS-associated coronavirus and SARS-CoV-2 has proven to be a pandemic worldwide. Coronaviruses are a type of enveloped virus. They are basically single-stranded and positive-sense RNA viruses which belongs to the subfamily Coronavirinae. Structure of SARS-CoV-2 is predicted to be the same as SARS-CoV due to high sequence similarity. SARS-CoV-2 is proven to be a major pandemic creator and affected the world at an exponential rate. The genome of COVID19 codes for the main protease 6LU7, is essential for viral replication and multiplication. To get a possible antiviral drug(s), nowadays is the major concern. In our study we screened ten fungal metabolites such as Aspirochlorine, Aflatoxin B1, Alpha-Cyclopiazonic acid, Sporogen, Asperfuran, Aspergillomarasmine A, Maltoryzine, Kojic acid, Aflatrem and Ethyl 3-nitropropionic acid against main protease 6LU7. These molecules were of fungal origin from Aspergillus flavus and Aspergillus oryzae. Aspergillomarasmine A exhibited the docking score of - 6.02 Kcal/mol, almost nearer to presently used drug Chloroquine (-6.29 Kcal/mol). Second highest docking score was found for Asperfuran (-5.5 Kcal/mol), whereas Aflatoxin B1 provided docking score was -5.0 Kcal/mol. We found similar docking score -5.4 Kcal/mol for Asperfuran, Maltoryzine and Kojic acid. Aspirochlorine and Ethyl 3-nitropropionic acid exhibited docking score were -5.3 Kcal/mol and -5.1 Kcal/mol respectively. These natural bioactive compounds could be tested in near future for their ability to inhibit viral growth both in invitro as well as invivo study.

A review on host genetic susceptibility to SARS CoV-2 related pneumonia

The pandemic of coronavirus started in December 2019 is a great challenge for mankind. Even though the mortality rate is less but still the virus has successively managed to cause this catastrophe. Many theories of the virus emergence have come forward but still the exact origin of the virus is unknown. This disease possesses serious concern as the appearance of the symptoms is different;mild to severe ARDS (Acute respiratory distress syndrome), also the strain circulating in different areas are showing different severity. It is very important to understand the host-pathogen interaction for the complete understanding of the disease. Along with the environmental factors, host genetic factors also play to accomplish disease severity. The main aim of this review is to understand various factors such as host immune response, host genes, age and factors which can change the pathogenesis of the virus from individual to individual. It might be able to address several ways to tackle the virus in different individuals.

A review on the emerging epidemic of novel coronavirus (nCoV- SARS CoV-2): Present combating strategies

The COVID-19 pandemic has reached to a reached to that degree that it has broken down the entire world driving WHO to proclaim a "General Health Emergency of International Concern"on January 30, 2020. SARS-CoV-2 is closely related to the original SARS-CoV in terms of its pathogenicity, clinical range, and the study of disease transmission. Correlation of the genome groupings of COVID-19, SARS-CoV, and Middle East Respiratory Syndrome Covid (MERS-CoV) demonstrated that COVID-19 highlights a much better succession personality with SARS-CoV contrasted with MERS CoV. Patients have appeared to go through intense respiratory trouble disorder (ARDS), which is characterized as the cytokine storm. Be that as it may, drugs like favipiravir, remdesivir, chloroquine, and so forth have been demonstrated to be compelling for the treatment of the illness. According to the reports, a novel Covid, SARS-CoV-2 is causing the sickness and it utilizes the ACE2 catalysts that plays a counter-administrative impact on the traditional ACE ward pathway, as a receptor for its entrance inside the cell. An outline of the infection inside the cell is given in this review. It likewise recommends that analogues of ADAM-17 may be utilized as medicines for this infection and further exploration must be led to know the mechanism of the RAS framework. Studies propose that the human receptor for COVID-19 could likewise be angiotensin-changing over compound 2 (ACE2) receptor practically like that of SARS CoV. In this audit we will attempt to investigate the study of disease transmission, morphology, indications, transmission & conclusion. Moreover we attempt to give a review about this life threatening infection.

An Automated System to Identify Sentiment from Micro-Blog Texts of Tweets

The recent trends of Internet help to produce the sentiment and emotion-based opinion at the time of conversation between human beings. During the Coronavirus disease (COVID-19) pandemic, we are spending maximum time of the day on Internet especially on social networking Web sites. The Web sites are Twitter, Facebook, and WhatsApp, which are taking a crucial role to communicate with each other in the form of messages. The messages are representing such as short-texts and micro-texts and carrying sentiment. The sentiment identification is challenging due to the length of the message. In the present paper, we are motivated to design a sentiment analysis system for Twitter micro-texts in the topic of Coronavirus disease (COVID-19). Hence, we have scrawled a dataset from Twitter using Twitter API and presented as our experimental dataset. Additionally, we have developed two state-of-the-art techniques, viz. unsupervised and supervised to build this system. The unsupervised technique helps to understand the characteristic of the dataset, whereas supervised technique assists in improving the accuracy of the system. The developed system may help to design various domain-specific applications such as annotation and emotion identification system for micro-texts in future. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Acute effects of hydroxychloroquine prophylaxis for COVID-19 in health care professionals - An online survey

Hydroxychloroquine, an antimalarial, is being used worldwide for prophylaxis and treatment of Corona virus disease-19 (COVID-19). Though the drug is commonly used in many chronic inflammatory diseases for protracted periods, its safety in the new indication is still under scrutiny. Therefore, this institute based study sought to assess the acute adverse effects of hydroxychloroquine among in-house health care professionals who were taking the drug for COVID-19 prophylaxis. A questionnaire seeking information on the use of the drug was prepared and disseminated electronically to the target population. The responses were also received electronically and analysed. The participants (n=54) had taken prophylaxis for 1-7 weeks. The most common adverse effects in the cohort were nausea (02) and skin rash (02). The total number of adverse effects reported by the participants was 08. One incidence each of gastric upset (01), dizziness (01), pain abdomen (01), and chest tightness (01) was reported. None of the adverse effects were serious. Our study indicates that the prophylactic weekly single dose of hydroxychloroquine is not associated with any serious adverse effects within 1-7 weeks of initiation. Elucidation of the long term and chronic adverse effects, if any, requires further studies.