Emerging Aquatic Contaminants: One Health Framework for Risk Assessment and Remediation in the Post COVID-19 Anthropocene

Emerging Aquatic Contaminants: One Health Framework for Risk Assessment and Remediation in the Post COVID-19 Anthropocene highlights various sources and pathways of emerging contamination, including their distribution, occurrence, and fate in the aquatic environment. The book provides detailed insight into emerging contaminants' mass flow and behavior in various spheres of the subsurface environment. Possible treatment strategies, including bioremediation and natural attenuation, are discussed. Ecotoxicity, relative environmental risk, human health risk, and current policies, guidelines, and regulations on emerging contaminants are analyzed. This book serves as a pillar for future studies, with the aim of bio-physical remediation and natural attenuation of biotic and abiotic pollution. © 2023 Elsevier Inc. All rights reserved.

SARS-CoV-2 Infection Increases the Gene Expression Profile for Alzheimer's Disease Risk

The COVID-19 pandemic is a global outbreak of coronavirus, an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One in five adults who have had COVID-19 in the past was still experiencing any one of the symptoms of long COVID like headache, brain fog, fatigue, and shortness of breath. Up to 30% of individuals with mild to severe infection show diverse neurological symptoms, including dementias. Hence, it is very much important to characterize the neurotropism and neurovirulence of the SARS-CoV-2 virus. This helps us understand the mechanisms involved in initiating inflammation in the brain, further leading to the development of earlyonset Alzheimer's disease and related dementias (ADRDs). In our brain gene expression analysis, we found that severe COVID-19 patients showed increased expression of innate immune response genes and genes that are implicated in AD pathogenesis. To study the infection-induced ADRDs, we used a mouse-adapted strain of the SARS-CoV-2 (MA10) virus to infect mice of different age groups (3, 6, and 20 Months). In this study, we found that aged mice showed evidence of viral neurotropism, prolonged viral infection, increased expression of tau aggregator FKBP51, interferoninducible gene Ifi204, and complement genes like C4 and C5AR1. Brain histopathology also showed the AD signature including tau-phosphorylation, tau-oligomerization, and alpha-synuclein expression in aged MA10-infected mice. The results from gene expression profiling of SARS-CoV-2 infected and AD brains and studies with MA10 aged mice show that COVID-19 infection increases the risk of AD in the aged population. Furthermore, this study helps us to understand the crucial molecular markers that are regulated during COVID infection that could act as major players in developing ADRDs. Future studies will be involved in understanding the molecular mechanisms of ADRD in response to COVID infection and developing novel therapies targeting AD.

Healthcare Industry: Embracing Potential of Big Data across Value Chain

In the fast-moving era of the Industrial Revolution (Industry 4.0), digitally fueled devices and technologies are paramount for driving innovation and creating values across a myriad of industries. A case in point is - Healthcare Industry. Healthcare insurance companies, hospitals, and other providers around the world are belligerently leveraging digital tools and technologies such as Big Data analytics, Lake, Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing, smart sensors, and the Internet of Things (IoT), for improving the overall quality of care and overall process efficiency and effectiveness. The Healthcare industry has been a center of discussion for embracing Big Data practice across the value chain for the past couple of decades due to the prodigious potential that is concealed in it. With so much abundant information, there have been numerous provocations related to the apiece stage of maneuvering big data that can only be amplified by leveraging high-end computer science results for big data analytics, as mentioned above. Well-organized healthcare ecosystem, analysis, and magnification of big data can influence the course of the game by opening new paths in terms of offering unique yet innovative products and services for the modern age technology-propelled healthcare value chain. This paper emphasizes the impetus of Big Data across the healthcare value chain, which involves the amalgamation of technology, data, and business, yielding better decisions and improving the experience across all touch points. © 2022 IEEE.

Predictive power of the natural organometallic complex ferritin - can potentiate earlier intervention in COVID-19

Treatment of COVID-19 has been a major challenge during the current pandemic. Various parameters including clinical symptoms and laboratory markers were used for predicting severity of the disease. However, limited data has been available regarding role of inflammatory markers used for monitoring the progression of the disease. Current study aimed to investigate whether serum ferritin level can be used a marker of rapid progression of the disease and if it can predict mortality in COVID-19 cases. The present study included 40 qRT-PCR positive patients who succumbed to COVID-19 as severe group, and 40 patients who were hospitalized but recovered from the disease as the mild group. Demographic details and laboratory data of the patients were obtained and evaluated retrospectively. Spearman's rank correlation was done between serum ferritin and age in patients of differing severity of the disease. Receiver operating curve (ROC) was used for identifying best cut-off level of ferritin for classification of severity. The mean age of the non-survivor (severe/critically ill group) had a tendency to be higher than the mean age of those of the survivors (mild group). Serum ferritin was significantly higher among severe COVID-19 cases compared to mild (p<0.0001). In ROC analysis area under the curve was 0.790. Here we report for the first time, a cut off value for ferritin (277 ng/ml), which can differentiate between the various hospitalization outcomes of COVID-19 patients. There was no significant association observed in age distribution with severity of the disease. Circulating ferritin level not only reflect acute phase response but rather plays critical role in inflammation of COVID-19 disease. Ferritin a natural organometallic complex is a widely available marker, that can be used for monitoring and predicting disease progression thus it can guide clinicians for the effective management of COVID-19. © 2023 Author(s).

An Optimized Ensemble Model for COVID Detection

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.

Known *Anti Sars-Cov Protease Inhibitors* On Sars-Cov-2 Related Intracellular Reception Sites: *Docking Interaction* And Computer Aided Optimization Of Molecular Structure For *Chemioselective Interactions*

Molecular docking and molecular dynamics aided virtual search of OliveNet™ directory identified potential secoiridoids that combat SARS-CoV-2 entry, replication, and associated hyperinflammatory responses. OliveNet™ is an active directory of phytochemicals obtained from different parts of the olive tree, Olea europaea (Oleaceae). Olive oil, olive fruits containing phenolics, known for their health benefits, are indispensable in the Mediterranean and Arabian diets. Secoiridoids is the largest group of olive phenols and is exclusive to the olive fruits. Functional food like olive fruits could help prevent and alleviate viral disease at an affordable cost. A systematized virtual search of 932 conformers of 78 secoiridoids utilizing Autodock Vina, followed by precision docking using Idock and Smina indicated that Nüzhenide oleoside (NZO), Oleuropein dimer (OED), and Dihydro oleuropein (DHO) blocked the SARSCoV-2 spike (S) protein-ACE-2 interface;Demethyloleuropein (DMO), Neo-nüzhenide (NNZ), and Nüzhenide (NZE) blocked the SARS-CoV-2 main protease (Mpro). Molecular dynamics (MD) simulation of the NZO-S-protein-ACE-2 complex by Desmond revealed stability during 50 ns. RMSD of the NZO-S-protein-ACE-2 complex converged at 2.1 Å after 20 ns. During MD, the interaction fractions confirmed multiple interactions of NZO with Lys417, a crucial residue for inhibition of S protein. MD of DMO-Mpro complex proved its stability as the RMSD converged at 1.6 Å. Analysis of interactions during MD confirmed the interaction of Cys145 of Mpro with DMO and, thus, its inhibition. The docking predicted IC50 of NZO and DMO was 11.58 and 6.44 μM, respectively. Molecular docking and dynamics of inhibition of the S protein and Mpro by NZO and DMO correlated well. Docking of the six-hit secoiridoids to IL1R, IL6R, and TNFR1, the receptors of inflammatory cytokines IL1β, IL6, and TNFα, revealed the anti-inflammatory potential except for DHO. Due to intricate structures, the secoiridoids violated Lipinski's rule of five. However, the drug scores of secoiridoids supported their use as drugs. The ADMET predictions implied that the secoiridoids are non-toxic and pose low oral absorption. Secoiridoids need further optimization and are a suitable lead for the discovery of anti-SARS-CoV-2 therapeutics. For the moment, olive secoiridoids presents an accessible mode of prevention and therapy of SARS-CoV-2 infection.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Post-covid Multisystem Inflammatory Syndrome in children (Mis-C) with rare gastrointestinal ramifications: Case reports from a Tertiary Care Hospital in Eastern India

Children with prior coronavirus disease 2019 (COVID-19) infection display an increased systemic inflammation causing multiorgan dysfunctions in the cardiovascular, respiratory, central nervous system, and gastrointestinal (GI) systems, known as MIS-C, that is, multisystem inflammatory syndrome in children. Most of the MIS-C cases have GI manifestations like pain abdomen, loose motion, vomiting or nausea, elevated liver enzymes, ileus, and bleeding. Angiotensin-converting enzyme 2 (ACE2) receptors in the terminal ileum and colon are responsible for the majority of ACE2-induced damage to these tissues. In the pandemic's second wave, a significant number of MIS-C patients with predominantly GI symptoms (around 80%) were reported. Although different molecular inflammatory mechanisms are involved, there is a significant overlap of the children's GI symptoms with those of MIS-C and other conditions of the abdomen caused by infection or inflammation, thus resulting in a diagnostic dilemma. Here, we report two cases of MIS-C with acute appendicular perforation and ileal perforation needing both medical management and surgical intervention. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.

United we stand: the entrepreneurs network and its role in overcoming crisis situations

PurposeThe present study aims to understand the role of the network of a woman entrepreneur in helping the business during a crisis with a focus on the stakeholders, namely, the suppliers and the customers. Design/methodology/approachCase study method is used to address the research objectives and a case of a woman entrepreneur based in India is selected for the same purpose. An interpretive approach is used to understand the underlying phenomenon. FindingsThe analysis of the case illustrates how the three major aspects of the network, i.e. content, governance and structure, manifest from the supplier and the customers' side and how do the same change during a crisis and may help the entrepreneur to overcome the crisis. Research limitations/implicationsThe present study contributes to the theory of "network success hypotheses of entrepreneurship theory" by offering a manifestation of the same during a crisis faced by the entire network of the entrepreneur. Practical implicationsThe findings provide insights on how an entrepreneur can use innovative ways of rethinking of the strategies during a crisis without compromising on the basic philosophy of the company. Originality/valueThe present study is one of a kind to identify the interplay between the entrepreneurial networks both from the customer and supplier sides of the firm during a crisis.

Real-Time Health Monitoring System Using Predictive Analytics

Medical specialists are primarily interested in researching health care as a potential replacement for conventional healthcare methods nowadays. COVID-19 creates chaos in society regardless of the modern technological evaluation involved in this sector. Due to inadequate medical care and timely, accurate prognoses, many unexpected fatalities occur. As medical applications have expanded in their reaches along with their technical revolution, therefore patient monitoring systems are getting more popular among the medical actors. The Internet of Things (IoT) has met the requirements for the solution to deliver such a vast service globally at any time and in any location. The suggested model shows a wearable sensor node that the patients will wear. Monitoring client metrics like blood pressure, heart rate, temperature, etc., is the responsibility of the sensor nodes, which send the data to the cloud via an intermediary node. The sensor-acquired data are stored in the cloud storage for detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Among the different cloud-based predictive models now being used, the model having the highest accuracy will be treated as the resultant model. This resultant model will be further used for the data dissemination mechanism by which the concerned medical actors will be provided an alert message for a proper medication in a desirable manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Reinventing healthcare services through online communities: exploring the key determinants

With the evolution of technology and the availability of advanced service delivery approaches, usage preference regarding the healthcare industry is changing. Technology has converted patient's roles from treatment seeker to health information contributor. Looking at the vast population of India, it is adequate to incorporate technology and use advanced technological features, which will make it easy, quick and transparent for users. Integration between biology and technology with the help of internet features changes the face of traditional treatment practices. It enables users to collaborate to share information, seek consultation, develop social bonding and carry research to fight critical health conditions. The current COVID-19 pandemic has forced the world to change the way it has been operating and be more technology-friendly. Wide usage of information systems with the evolution of internet technology is continuously adding new outlooks to the legacy treatment practices. These new outlooks are the combined reflection of certain key factors, which are essential from both users as well as service provider perspective. This study aims to systematically evaluate all these vital factors and understand their roles in effective healthcare delivery. Copyright © 2022 Inderscience Enterprises Ltd.

Mental health concerns in outpatients during COVID-19 pandemic: Comparison between psychiatric and general hospital clinics in an Indian population

We intended to compare mental health concerns in patients attending a general hospital clinic with that of patients attending a psychiatric clinic during the coronavirus disease 2019 (COVID-19) pandemic. We specially wished to know about the perceived stress, worries, possibility of anxiety and depression, and the quality of life (QOL) of the patients. In a cross-sectional study, we used two screening questions for depression, Generalised Anxiety Disorder-2 (GAD-2) scale for anxiety, Perceived Stress Scale (PSS) for stress, nature and degree of worries in a 0–10 scale, and QOL in a 1–10 scale. Majority (75.5 percent) of outpatients had moderate to high level of stress, 76.5 percent were screen positive for depression, and 42 percent for anxiety. Psychiatric patients had significantly higher perceived stress, level of worry, and lower QOL to those with physical illness and were screen positive for depression (99 percent v 54 percent) and anxiety (68 percent v 16 percent), respectively. Patients with major physical illnesses had significantly higher stress levels and anxiety compared with those without. Fear of getting the infection, loss of job, and financial issues were the major worries along with social stigma. The results highlighted the need for screening mental health concerns in general hospital and psychiatric outpatients during the COVID-19 pandemic and facilitate appropriate interventions. © 2022 Weston Medical Publishing. All rights reserved.

Deep Learning-Based Trend Analysis on Indian Stock Market in COVID-19 Pandemic Scenario and Forecasting Future Financial Drift

This present study has used the long-short-term memory (LSTM) network-based deep learning architecture to analyze the influence of the current widespread COVID-19 on the Indian stock market. The major contribution of this work is as follows: (1) Designing LSTM-based deep neural network is used to study the effect of the COVID-19 outbreak and Lockdown on the Indian stock exchange (Nifty 50), and (2) designing a prediction model to capture the effect of various COVID-19 waves in India on Indian Stock exchange. The outcomes of the analysis show that the increase in daily new confirmed cases, recovered cases, and death cases have a significant adverse impact on the trend of the stock market. Moreover, the results of the work have also analyzed the impact of government policy such as ‘lockdown city’ with a reaction to increased Pandemic cases. This work is briefly summarized as follow: (1) LSTM-based deep neural network is used for this study to analyze the effect of the COVID-19 outbreak on the Indian stock exchange. (2) The Indian Stock exchange affected by the COVID-19 pandemic has been studied. Here, the analysis is based on the impact of COVID-19 including the effect of lockdown. (3) A prediction model has been proposed for the study of the behavior of the Indian stock index (Nifty 50) during the COVID-19 pandemic. (4) Comparison of the efficacy of the suggested approach with other existing baseline regression models. © 2022, The Institution of Engineers (India).

Food-based probiotics: Functional dietary ingredients

Probiotics are included in the group of health promoting functional foods that promotes good gut health through the supply of essential metabolites with therapeutic characteristics. Promoting a healthy digestive tract and a healthy immune system are their most widely studied benefits of late. A series of food and pharmaceutical products have drawn the attention and interest of consumers due to their exclusive health benefits. Therefore, food-based probiotics are gaining popularity in the recent years, even though there is an increase in commercially available probiotic supplements. Probiotics can be naturally found in some food groups, whereas, the other food groups can act as a vehicle for probiotics. One of the most abundant sources of probiotics is the group of lactic acid bacteria (LAB), which plays an important role in preventing intestinal problems. Under certain stress conditions, intestinal microbiota may be altered manifesting in gut disorders. Probiotic bacteria stimulate the growth of indigenous beneficial gut microbiota by inhibiting the growth of opportunistic pathogenic microbes. These are also commonly known as healthy bacteria which are obtained from food, beverages and dietary supplements. Thus, this chapter will focus on the probiotics naturally occurring across different food groups of the food pyramid, and also their potential in fermented food products for healthy diets. © 2022 Elsevier Inc. All rights reserved.

A Deep Learning Approach for COVID-19 Infection Probability Prediction

COVID-19 is one of the major health crises worldwide. Though number of vaccines is introduced by many countries but still it is challenging to detect the disease at early stage. Many advanced technologies are introduced for this purpose to stop the spread. Machine learning based COVID detection can be a supportive tool for both physicians as well as patients for early prediction of this illness. Different automated technologies are also found from the literature for this purpose. Deep learning approach is used for predicting the infection probability by analyzing the five types of COVID symptoms. The experiment is carried out with 2889 samples collected from a publicly available database. A deep neural network (DNN) classifier is designed for this purpose and the result is also compared with support vector machine (SVM). From the result it is observed that around 97% classification accuracy is observed with DNN classifier and it is better than SVM. © 2021 IEEE.

An Unsupervised Clustering Algorithm to Cluster the New SARS-CoV-2 Virus Mutation

The Coronavirus disease 2019 SARS-CoV-2 is a disease which causes fear to human lives that has taken thousands and hundreds of lives globally. The pandemic which has resulted in a global health emergency is currently a much sought-after research topic. The frequently mutating virus which has originated from Chiroptera and subsequently got transmitted to other mammals including humans. However, at the genomic level, it is yet to be unraveled what makes humans more prone to getting infected by the coronaviruses. Here, we have implemented a Machine Learning model known as K-means Clustering that uses the combination of different features to determine the risk of infection. In this research paper, the K-means clustering method is used since it is a good performer for Clustering analysis. The algorithm can group the sequences of the dataset into five clusters based on the Elbow plot and co-linearity of co-efficient. Using dimensional reduction technique PCA is used with a 3D visualization and a heat map to showcase the correlation efficiency between the mutated and original sequence considered. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.