Nanomaterial-based smart coatings for antibacterial, antifungal, and antiviral activities

Infectious diseases caused by different pathogens (parasites, protozoa, bacteria, viruses, and fungi) have affected the world at various times in the form of epidemics and pandemics. The coronavirus has also directly affected the world's economy and public health. Various drugs such as antibiotics, antimicrobials, antifungals, and antivirals have been investigated to combat these diseases. However, these fatal infections are still a major concern because of their transmission through contaminated surfaces, human-to-human contact, airborne diffusion, and microbial resistance. Therefore, considerable efforts are required to suppress the transmission of these pathogens. Smart coatings are able to sense their environment and adapt their properties according to the stimulus. Furthermore, various parameters of coating technology can be controlled on a molecular level to influence the morphology. Nanomaterial (NM)-based smart coatings are 99.99% effective against bacteria, viruses, and fungi because of the unique properties of NMs involved. Moreover, NM-based smart coatings are 1000-fold more efficient than traditional coating technologies. Besides their antifungal, antiviral, and antibacterial application, they are anticorrosive and self-cleaning. This chapter summarizes various NM-based smart coatings (organic, inorganic, and carbon) implemented in antibacterial, antifungal, and antiviral applications. Furthermore, the application of these coatings in various fields and their associated challenges will be discussed. © 2023 Elsevier Inc. All rights reserved.

Global Evidence of Pandemic Effects on Educational Disruption

The article provides a vivid illustration of the challenges faced by the education sector during the pandemic. Education disruptions have increased stress and anxiety in students and their families. In addition to this, schools in rural areas and underdeveloped countries failed to provide the necessary equipment and facilities to help the students proceed with online classes. These articles have relied on secondary data and information to understand various concepts and theories. In order to combat these consequences, a worldwide initiative called REDS was formed to analyze the opinion of students and individuals connected with the education sector and remodel the system for combating the challenges posed by the pandemic. The study proceeded with the help of thematic analysis. © 2023 EDP Sciences. All rights reserved.

To Assess the Adequacy of Knowledge to Prevent COVID-19 Pandemic: Cross-Sectional Descriptive Online Survey

Aim: To determine the level of knowledge towards COVID-19 among people. Material(s) and Method(s): A cross-sectional descriptive research design was used for the present study and was conducted among people attending Darbhanga Medical College, Darbhanga, Bihar, India, to assess their knowledge regarding COVID-19. A total of 461 people were recruited for this study and sample of 400 eligible people who fulfill the inclusion criteria were enrolled. Result(s): The association of socio-demographic variables of participants and their knowledge score. It shows that group (p>0.001), gender (p=0.020), education (p=0.001), marital status (p=0.001), age (p=0.020), and inhabitants (p=0.001) were significantly associated with knowledge. Majority of participants 63% having good knowledge while 33% and 1.4% having average and poor knowledge respectively regarding the corona virus pandemic. Conclusion(s): Study concluded that many people were still had average and poor knowledge on COVID-19. Higher authorities must find the ways for making people more aware on this pandemic to control its impact.Copyright © 2023, Dr. Yashwant Research Labs Pvt. Ltd.. All rights reserved.

Detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) Archimedes optimization algorithm using ultra-low-dose CT images

In this article, the detection of COVID-19 patient based on attention segmental recurrent neural network (ASRNN) with Archimedes optimization algorithm (AOA) using ultra-low-dose CT (ULDCT) images is proposed. Here, the ultra-low-dose CT images are gathered via real time dataset. The input images are preprocessed with the help of convolutional auto-encoder to recover the ULDCT images quality by removing noises. The preprocessed images are given to generalized additive models with structured interactions (GAMI) for extracting the radiomic features. The radiomic features, such as morphologic, gray scale statistic, Haralick texture are extracted using GAMI-Net. The ASRNN classifier, whose weight parameters optimized with Archimedes optimization algorithm enables COVID-19 ULDCT images classification as COVID-19 or normal. The proposed approach is activated in MATLAB platform. The proposed ASRNN-AOA-ULDCT attains accuracy 22.08%, 24.03%, 34.76%, 34.65%, 26.89%, 45.86%, and 32.14%;precision 23.34%, 26.45%, 34.98%, 27.06%, 35.87%, 34.44%, and 22.36% better than the existing methods, such as DenseNet-HHO-ULDCT, ELM-DNN-ULDCT, EDL-ULDCT, ResNet 50-ULDCT, SDL-ULDCT, CNN-ULDCT, and DRNN-ULDCT, respectively. © 2023 John Wiley & Sons, Ltd.

Application of Convolutional Neural Network techniques to the classification of lung illnesses

The heterotypic perspective of cancer depicts solid tumors as ecosystems composed of aberrant epithelium tumor cells and a multitude of cell types together referred to as stromal cells. Macrophages, which are innate immune cells, are overrepresented in certain environments. Tumor-associated macrophages (TAMs) are macrophages found in the tumor microenvironment;they are derived from the blood's monocytes and are essential for tumor progression. TAMs acquiring tumorigenic qualities is dependent on a complicated interaction between TAMs and tumor cells. Using co-culture studies, we showed that tumor-derived secretory signals promote Tams' tumor-promoting characteristics, shaping up Tams' features in ways that are advantageous to the tumor. When model human monocytes (THP-1) were co-cultured with A549 cells, the A549 cells exhibited increased proliferation, migration, and invasiveness due to the secretion of tumor-promoting cytokines from the THP-1 cells. We showed that EDA-containing Fibronectin secreted by A549 cells reliably mediates the pro-inflammatory response of THP-1 monocytes in a paracrine manner. Ablation of such responses by the treatment of THP-1 cells with TLR-4 blocking antibody implicated Fibronectin-TLR4 axis in tumor-associated inflammation and suggests a paradigm wherein lung carcinoma cell derived EDA-containing Fibronectin drives a pro-inflammatory and pro-metastatic tumor microenvironment. Interestingly, autocrine proliferation, migration, and invasion were all boosted by EDA-containing Fibronectin secreted by A549 cells. Lastly, we demonstrated that the EDA in Fibronectin activates the epithelial-mesenchymal transition pathway in A549 cells, hence granting these cells the ability to metastasize. © 2023 IEEE.

Structure-Based Identification and Analysis of Viral Protease Inhibitors Targeting SARS CoV-2 Virus Replication

The re-emergence of SARS-CoV, known as SARS-CoV-2, has proven extremely infectious that has infected a huge population worldwide. SARS-CoV-2 genome is translated into polyproteins that is processed by virus-specific protease enzymes. 3CLprotease is named as the main protease (Mpro) enzyme that cleaves nsp4 to nsp16. This crucial role of Mpro makes this enzyme a prime and promising antiviral target. Till date, there is no effective commercially available drug against COVID-19 and launching a new drug into the market is a complicated and time-consuming process. Therefore, drug repurposing is a new but familiar approach to reduce the time and cost of drug discovery. We have used a high-throughput virtual screening approach to examine FDA approved library, natural compound library, and LOPAC 1280 (Library of Pharmacologically Active Compounds, Sigma-Aldrich, St. Louis, MO) library against Mpro. Primary screening identified potential drug molecules for the target, among which ten molecules were studied further using biophysical and biochemical techniques. SPR was used to validate the binding of inhibitors to purified Mpro and using FRET-based biochemical protease assay these inhibitors were confirmed to have Mpro inhibitory activity. Based on the kinetic studies, the antiviral efficacy of these compounds was further analysed by cell-culture based antiviral assays. Four out of ten molecules inhibited SARS-CoV-2 replication in Vero cells at a concentration range of 12.5 to 50 muM. The antiviral activity was evaluated by RT-PCR assay and TCID50 experiments. The co-crystallization of Mpro in complex with inhibitor for determining their structures is being carried out. Collectively, this study will provide valuable mechanistic and structural insights for development of effective antiviral therapeutics against SARS-CoV-2.

Coronavirus Disease (COVID-19) Possible Transmission Routes and Alleviation Strategies

Coronavirus disease is a contagious respiratory ailment that has spread significantly around the world. Most cases of COVID-19 are spread from person to person by coming into contact with respiratory droplets that are released when an infected person coughs or sneezes. In this manuscript, we have highlighted the possible transmission of COVID-19 through food, water, air and paper. In the case of food, we have extensively covered the transmission of COVID-19 through meat, frozen foods, food packaging and food market along with the incidences worldwide. In the nextsection, we have highlighted the different components of air which are responsible for the transmission and also covered its relation with PM 2.5 incidence. The SARS-CoV-2 was isolated from sewage water/wastewater of various countries namely the United States, India, Australia, Netherlands and France signifying that wastewater can be a mode of virus transmission. The paper circulation by the infected COVID-19 patients can also be a virus conveyance route. It can be concluded that SARS-CoV-2 can therefore be transmitted indirectly through food via the workers involved in food packing or food marts.By following general safety precautions (wearing masks, using hand sanitisers, cleaning and disinfecting contact surfaces, and avoiding close contact), heating and using chemicals like ethanol (67-71%), sodium hypochlorite (0.1%) and hydrogen peroxide (0.5%) on environmental surfaces, along with vaccination, it is possible to reduce the spread of the SARS-CoV-2 virus.Copyright © 2023 The International Journal of Pharmaceutical Research and Allied Sciences (IJPRAS).

RECENT DEVELOPMENTS IN SOFT COMPUTING BASED TECHNIQUES FOR FEATURE SELECTION AND DISEASE CLASSIFICATION

Computational prediction of diseases is vital in medical research that contributes to computer-aided diagnostics and helps doctors and medical practitioners in critical decision-making for various diseases such as bacterial and viral kinds of disease, including COVID-19 of the current pandemic situation. Feature selection techniques function as a preprocessing phase for classification and prediction algorithms. For disease prediction, these features may be the patient's clinical profiles or genomic features such as gene expression profiles from microarray and read counts from RNA-Seq. The performance of a classifier depends primarily on the selected features. In addition, genomic features are too large in numbers, resulting in the curse of dimensionality problem. In the last few years, several feature selection algorithms have been developed to overcome the existing problems to get rid of eliminating chronic diseases, such as various cancers, Zika virus, Ebola virus, and the COVID-19 pandemic. In this review article, we systematically associate soft computing-based approaches for feature selection and disease prediction by applying three data types: patients' clinical profiles, microarray gene expression profiles, and RNA-Seq sample profiles. According to related work, when the discussion took place, the percentage of medical data types highlighted through pictorial representation and the respective ratio of percentages mentioned were 52%, 27%, 9% and 12% for clinical symptoms, gene expression, MRI-Image and other data types such as signal or text-based utilized, respectively. We also highlight the significant challenges and future directions in this research domain © 2023, Suranaree Journal of Science and Technology.All Rights Reserved.

Role and mechanistic actions of protein kinase inhibitors as an effective drug target for cancer and COVID.

Kinases can be grouped into 20 families which play a vital role as a regulator of neoplasia, metastasis, and cytokine suppression. Human genome sequencing has discovered more than 500 kinases. Mutations of the kinase itself or the pathway regulated by kinases leads to the progression of diseases such as Alzheimer's, viral infections, and cancers. Cancer chemotherapy has made significant leaps in recent years. The utilization of chemotherapeutic agents for treating cancers has become difficult due to their unpredictable nature and their toxicity toward the host cells. Therefore, targeted therapy as a therapeutic option against cancer-specific cells and toward the signaling pathways is a valuable avenue of research. SARS-CoV-2 is a member of the Betacoronavirus genus that is responsible for causing the COVID pandemic. Kinase family provides a valuable source of biological targets against cancers and for recent COVID infections. Kinases such as tyrosine kinases, Rho kinase, Bruton tyrosine kinase, ABL kinases, and NAK kinases play an important role in the modulation of signaling pathways involved in both cancers and viral infections such as COVID. These kinase inhibitors consist of multiple protein targets such as the viral replication machinery and specific molecules targeting signaling pathways for cancer. Thus, kinase inhibitors can be used for their anti-inflammatory, anti-fibrotic activity along with cytokine suppression in cases of COVID. The main goal of this review is to focus on the pharmacology of kinase inhibitors for cancer and COVID, as well as ideas for future development.

Crowd cluster data in the USA for analysis of human response to COVID-19 events and policies.

We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts.

The interplay of skills, digital financial literacy, capability, and autonomy in financial decision making and well-being

Covid-19 and the unprecedented surge in financial technology contributed to unexpected financial challenges, affecting the relevance of financial decision making and perceived financial well-being. This paper examines the mediating effects of digital financial literacy, financial autonomy, financial capability, and impulsivity on financial decision making and perceived financial well-being. The data come from 512 re-spondents in Delhi/NCR (National Capital Region), India, using a snowball-sampling technique and partial least squares structural equation modeling to test 13 structural hypotheses with SmartPLS3.3. Partial least squares (PLS) prediction is employed to estimate the out-of-sample predictive power of the proposed model. Our findings reveal that skills directly affect financial decision making and perceived financial well-being, and digital financial literacy emerges as a direct and mediating predictor of financial decision making. The dominance of financial capability and financial autonomy as mediators in financial decision making and financial well-being become more evident, and impulsivity fails to have mediating effects on financial decision making. The results have academic, regulatory, and managerial implications, all of which calls for more concerted efforts at recognizing the unique interaction among skills-financial decision making-perceived financial well-being, the cu-mulative effect of which enhances the critical ability to deal with environmental challenges, manage socioeconomic pressures in a sustainable manner, and translate the benefits into prudent gender-specific policy decisions and practices.Copyright (c) 2022 Borsa Istanbul Anonim S , irketi. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Silymarin: A Phytoconstituent with Significant Therapeutic Potential-A Narrative Review

Silymarin, is a phytoactive constituent isolated from the fruits and seeds of Silybum maria-num L Gaetn.), also called milk thistle belonging to the family of Asteracease. The phytoactive has been used to treat several physiological disorders. The objective of this manuscript was to review the therapeutic prospective of silymarin due to its ability to treat several physiological disorders. The da-tabases such as Pubmed, Elsevier, and Google Scholar were reviewed for the investigations or reviews published related to the title. The discussion is focused on the immunomodulatory, chemopreventive, and anti-inflammatory mechanisms of silymarin in various metabolic and dermatological disorders. In addition, the review discusses the different therapeutic potentials of silymarin such as the management of the liver disorder, skin carcinogenesis, cardiovascular disorders, diabetes mellitus, neurodegenera-tive disorders, and several dermatological disorders such as melasma, anti-aging, acne, rosacea, atopic dermatitis, and psoriasis. Silymarin is safe even with a dose higher than the therapeutic dose. Si-lymarin had good potential for the safe and effective treatment of numerous metabolic and dermatological disorders. © 2023 Bentham Science Publishers.

Automated system to detect social distancing among humans during covid pandemic situation

Maintaining the purported Social Separating is one of the essential and greatest ways to stop the new popular episode. Legislators are enacting restrictions on the standard of private distance between people in order to concur with this restriction. In light of this real-life occurrence, it is crucial to evaluate how consistent with realistic imperatives in our lives this is, in order to ascertain the causes of any prospective cracks in such distance obstacles and determine whether this portends an anticipated risk. In order to do this, we offer the Visual Social Removing (VSD) problem, which is defined as the automatic evaluation of the difference between the depiction of connected person aggregations and the private separation from an image.When this requirement is violated, it is vital for VSD to conduct painless research to determine whether people agree to the social distance restriction and to provide assessments of the degree of wellbeing of particular places. We first draw attention to the fact that measuring VSD involves more than simply math;it also suggests a deeper comprehension of the social behavior in the setting. The goal is to genuinely identify potentially dangerous circumstances while avoiding false alerts (such as a family with children or other family members, an elderly person with their guardians), all while adhering to current security protocols. Then, at that point, we discuss how VSD links to earlier research in social sign handling and demonstrate how to investigate fresh PC vision techniques that might be able to address this issue. Future issues about the viability of VSD systems, ethical repercussions, and potential application scenarios are the result. © 2023 IEEE.

Healthcare Diagnostics Service Using Federated Learning

Healthcare systems around the world rely on powerful computational prediction tools in order to make accurate diagnostics with regard to the human body. In order to estimate the severity of lung damage post-COVID infection, healthcare providers rely on AI prediction tools to perform diagnosis. While such tools exist at a rudimentary level, there is a growing demand for more reliable and democratised systems that train models over a diverse data-set. To that end, the focus of this research paper turns to federated learning, a distributed machine learning paradigm. The system proposed consists of a central server that pools features and weights across various nodes, thereby cutting bias in the prediction models. This also achieves data decentralisation which ensures patient privacy. An end-to-end application is realised that facilitates distributed training of batch data that is visualised in real-time with the help of sockets. The application also features an inference service, classifying chest x-rays based on whether the image displays damage in case of Pneumonia. © 2023 IEEE.

Face Mask Detection Using Convolution Neural Network

Face recognition in the industry now is playing an important role in each sector. Each person has different type of features and face;therefore, each identity is unidentical. In this COVID outbreak, a major crisis has occurred due to which preventions are to be made. One such prevention is use of a face mask which is very much important. Nowadays, various firms and organizations are using facial recognition systems for their own general purpose. We all know that it has now been a crucial task to wear a mask every time, when we go somewhere. But as we know it is not possible to keep track of who wears a mask and who does not. We make the use of AI in our daily life. We achieve this with the help of a neural network system, which we train so that it can further describe people's features. Even though the original dataset was limited, the Convolutional Neural Network (CNN) model achieved exceptional accuracy utilizing the deep learning technique. With the use of a face mask detection dataset that contains both with and without face mask photographs, we are able to recognize faces in real-time from a live webcam stream using OpenCV. We will develop a COVID-19 face mask detection system using our dataset, along with Python, OpenCV, Tensor Flow, and Keras. © 2022 IEEE.

A Review on Covid-19

COVID-19 (Corona Virus Disease 2019) is a matter of concern since the end of the year 2019, when China informed WHO that there is some pneumonia-like disease with unknown causes in Wuhan.Corona disease is related to the SARS-CoV-2 i.e. Severe Acute Respiratory Syndrome Corona Virus 2. SARS CoV-2 is the strain of coronavirus that causes the disease. Coronaviruses are a group of single-stranded viral RNA genome.WHO has declared COVID-19 as pandemic. Among the various varieties of coronavirus, six are known to infect the human host and cause respiratory diseases. Belonging to the family "Coronaviridae", coronavirus causes a broad spectrum of human and animal diseases. In this review, we have worked to possibly cover all the information about coronavirus.Copyright © 2020 Ubiquity Press. All rights reserved.

An Evaluative Study on Strategic Directives for Brand Delight and Its Impact on Customer Retention in Indian Food Industry Amidst Covid-19

This study is an evaluative study on strategic perspectives on brand delight and its impact on Indian fast-food Industry amidst Covid19. The global crisis and turmoil have crippled and transformed the entire operations of the globe. The Indian fast-food industry has been closed for almost six months. There is a need for revamping and restarting this industry for economic and social contributions.As social distancing, wearing masks and following the government norms has become the order of the day, this has also led to change in consumer's perceptions and attitudes. The study by nature evaluates strategic perspectives which could lead to brand delight in Indian fast food industry. This would also ensure brand retention in these challenging times. As there has been very little empirical investigations which have been attempted in this industry during these tough times in India, this study addresses the research gaps and also provides practical inputs for Indian fast-food industry on consumer perception towards brand delight and retention, which is the need of the hour. This is an empirical and quantitative study which attempts to provide strategic perspectives for Indian fast-food industry by collecting primary data through Questionnaire. The primary data for the study has been collected with 64 fast food consumers from Delhi, Mumbai, Ahmedabad, Amritsar and Calcutta. Stratified random sampling has been used in this study. The collected data has been analysed using SPSS tools as correlation, Wilcoxon signed rank test and Friedman two-way non-parametric Anova test has been conducted in this study. In this study it is found that brand communication can be made very effective using Digital display & advertisements &SEO Tools getting next Social media like FB getting least ranking which is very surprising. In this study it is also found that image of the product and awareness, popularity of fast-food leads to more purchase of fast-food products. It is found from this study that there is a positive correlation between the factors of satisfaction with fast food products and engagement and expectation of more products in fast food industry. It was also found that majority of the respondents love Indian dishes and Indian restaurants, Dominos, Pizza hut, McDonald are their favourite choices and also, they expecting to have more spicy and vegetarian varieties in fast food industry. The factors for customer retention have been provided in this study which includes Pleasing environment and good food are my preferences for happiness and satisfaction, Variety of dishes and menu has attracted customers to this food and customers are happy with the experience and would come again, & Communication of staff is very essential which makes consumers happy and satisfied. From these findings it is concluded that Indian fast-food industry must have these strategic perspectives for brand delight and customer retention.Copyright © 2020 Ubiquity Press. All rights reserved.

Stereoselective and regioselective Heck arylation at C-17 exocyclic double bond of andrographolide to generate labdane-based lead molecule against acute lung injury

Andrographolide (AG-1) is identified as an attractive scaffold based on in silico/in vitro/in vivo (preclinical and clinical) studies against COVID-19 infection, for which hardly any effective drug is available to date. Due to complexity of its chemical structure, stereoselective and regioselective Heck arylation reactions at C-17 exocyclic double bond of AG-1 is a major challenge and we stepped forward to generate a small focused library of compounds. Among all the molecules, AG-12 and AG-13 were predicted to have better pharmacokinetic profiles than AG-1. Upon evaluation of in vivo efficacy of AG-12 and AG-13 in comparison to AG-1 using an LPS-induced acute lung injury model, AG-13 showed promising action towards reduction of the neutrophil count, minimization of oxidative stress, and inhibition of inflammatory cytokines. Further, lead optimization should be carried out towards developing potential natural product-driven therapeutics to combat acute respiratory distress syndrome (ARDS) situations during COVID-19. © 2023 Elsevier B.V.

The role of classroom engagement on academic grit, intolerance to uncertainty and well-being among school students during the second wave of the COVID-19 pandemic in India.

The forced changes and disruptions in educational systems and learning experiences due to the pandemic has impacted students' mental health and well-being. The present study aims to understand the effects of the determinants of well-being on students in India during the second wave (April to August 2021) of the COVID-19 pandemic. The determinants of well-being in this study are academic grit, intolerance to uncertainty and students' engagement in an online learning environment. In this study, well-being is characterized as students' confidence and satisfaction in an online learning and pandemic environment. The data collected from 1174 students (12-19 years) from various states, using standardized tools, were analyzed to find out about the mediating effect of students' engagement on the relationship between academic grit and well-being, and between intolerance to uncertainty and well-being. Further, the model fit analysis of the determinants of well-being is explored. The paper reports that students' classroom engagement does mediate in the path of academic grit and well-being, and in the path of intolerance to uncertainty and well-being. It also evidence the model fit of the influence of the determinants of well-being on that of school students during the second wave of the COVID-19 pandemic. The study also draws implications and suggestions for educators using the current model of students' well-being.