A survey of image encryption for healthcare applications.

Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.

Identifying the natural compound Catechin from tropical mangrove plants as a potential lead candidate against 3CLpro from SARS-CoV-2: An integrated in silico approach.

SARS-CoV-2, a member of beta coronaviruses, is a single-stranded, positive-sense RNA virus responsible for the COVID-19 pandemic. With global fatalities of the pandemic exceeding 4.57 million, it becomes crucial to identify effective therapeutics against the virus. A protease, 3CLpro, is responsible for the proteolysis of viral polypeptides into functional proteins, which is essential for viral pathogenesis. This indispensable activity of 3CLpro makes it an attractive target for inhibition studies. The current study aimed to identify potential lead molecules against 3CLpro of SARS-CoV-2 using a manually curated in-house library of antiviral compounds from mangrove plants. This study employed the structure-based virtual screening technique to evaluate an in-house library of antiviral compounds against 3CLpro of SARS-CoV-2. The library was comprised of thirty-three experimentally proven antiviral molecules extracted from different species of tropical mangrove plants. The molecules in the library were virtually screened using AutoDock Vina, and subsequently, the top five promising 3CLpro-ligand complexes along with 3CLpro-N3 (control molecule) complex were subjected to MD simulations to comprehend their dynamic behaviour and structural stabilities. Finally, the MM/PBSA approach was used to calculate the binding free energies of 3CLpro complexes. Among all the studied compounds, Catechin achieved the most significant binding free energy (-40.3 ± 3.1 kcal/mol), and was closest to the control molecule (-42.8 ± 5.1 kcal/mol), and its complex with 3CLpro exhibited the highest structural stability. Through extensive computational investigations, we propose Catechin as a potential therapeutic agent against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

A Review of Mirzapur Season 1 Showing How Media Frames Gun Culture and Abusive Languages

Studios have been compelled to push back the arrivals of their most prominent motion pictures to the following year or skirt dramatic deliveries entirely, drop-kicking films directly to web-based features, prompting a film industry 77.2 percent more regrettable than as of now last year, as per the media investigation organization comscore [1]. The Alternatives With the increasing number of corona cases, the hope for theatres to open decreased. [...]the search for new alternatives began and the most favourable outcome was using the OTT platform. While there are different types of OTT stages, OTT television allude to great video content transferred straightforwardly from the supplier, on to a client's screen (versatile, tablet, PC, television and so on) through Web Convention over a public organization. Various creators have utilized different models including - Innovation Acknowledgment Model (Hat), (Davis, 1989), Hypothesis of Panned Conduct (TPB) (Ajzen, 1991), Dissemination of Development (DOI), (Roger, 1995), Hypothesis of Contemplated Activity (TRA) to survey and comprehend the acknowledgment of another innovation by clients and relate it with reception of OTT media including television, voice, intuitive and others.20 as for OTT stage, Cap is utilized essentially by different creators like Cha, 2013;Cha and Chan-Olmsted, 2012.

Effect of COVID-19 on recreational trips to tourist destination - An Indian context

The present work reports an investigation on perception of tourists towards recreational trips to tourist destinations due to the effect of COVID-19 in India. Responses of tourists were captured from several popular tourist destinations in India for different travel modes, different activities within a destination and various interventions in the context of COVID-19 by a survey questionnaire and the responses were analyzed using RIDIT to rank as per their perceived risk and importance. Public transport like air, train and bus were found to be the high-risk travel modes over personal vehicles along with visiting attraction points/shopping areas within a destination. Interventions like sanitization, social distancing, use of musk and self-vaccination got higher importance by the tourists. An SP survey was conducted and depending on various interventions and their levels, a model was developed based on binary logit, which gives the probability of making a recreational trip to a tourist destination.

Seroprevalence of severe acute respiratory syndrome coronavirus 2 immunoglobulin G antibodies among health-care workers prior and after 4-6 weeks of coronavirus disease vaccine administration at tertiary care center of Southwest Bihar, India

AIM: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an ongoing global health emergency. To control the spread, a mass vaccination program is initiated. Antibody titer after vaccination can be a better marker to monitor immunological response. MATERIALS AND METHODS: The study was carried out at the Department of Microbiology, Narayan Medical College and Hospital, Jamuhar Sasaram, southwest Bihar, considering the sample size, type, and collection. First, antibody was tested before vaccination and second antibody value after 28 days of the first dose of COVID vaccine among the health-care workers and housekeeping staff. RESULTS: A total of 251 subjects were administered with vaccination (Covishield) to check the immunoglobulin g (IgG) responses. The concentration of the SARS-CoV-2 IgG antibody in female patients tended to be higher than in male patients. CONCLUSION: There is a difference in antibody positivity among males and females. Most of the participants had IgG positivity, because of their profession, vaccination boosted percentage positivity in both males and females. Females have more IgG levels compared to males. Hence, recommend that separate guidelines can be made between males and females for vaccination dosages. [ FROM AUTHOR] Copyright of Indian Journal of Health Sciences & Biomedical Research is the property of Wolters Kluwer India Pvt Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A computational essential dynamics approach to investigate structural influences of ligand binding on Papain like protease from SARS-CoV-2.

Papain like protease (PLpro) is a cysteine protease from the coronaviridae family of viruses. Coronaviruses possess a positive sense, single-strand RNA, leading to the translation of two viral polypeptides containing viral structural, non-structural and accessory proteins. PLpro is responsible for the cleavage of nsp1-3 from the viral polypeptide. PLpro also possesses deubiquitinating and deISGlyating activity, which sequesters the virus from the host's immune system. This indispensable attribute of PLpro makes it a protein of interest as a drug target. The present study aims to analyze the structural influences of ligand binding on PLpro. First, PLpro was screened against the ZINC-in-trials library, from which four lead compounds were identified based on estimated binding affinity and interaction patterns. Next, based on molecular docking results, ZINC000000596945, ZINC000064033452 and VIR251 (control molecule) were subjected to molecular dynamics simulation. The study evaluated global and essential dynamics analyses utilising principal component analyses, dynamic cross-correlation matrix, free energy landscape and time-dependant essential dynamics to predict the structural changes observed in PLpro upon ligand binding in a simulated environment. The MM/PBSA-based binding free energy calculations of the two selected molecules, ZINC000000596945 (-41.23 ± 3.70 kcal/mol) and ZINC000064033452 (-25.10 ± 2.65 kcal/mol), displayed significant values which delineate them as potential inhibitors of PLpro from SARS-CoV-2.

A survey of image encryption for healthcare applications

Recently, medical image encryption has attracted many researchers because of security issues in the communication process. The recent COVID-19 has highlighted the fact that medical images are consistently created and disseminated online, leading to a need for protection from unauthorised utilisation. This paper intends to review the various medical image encryption approaches along with their merits and limitations. It includes a survey, a brief introduction, and the most utilised interesting applications of image encryption. Then, the contributions of reviewed approaches are summarised and compared regarding different technical perspectives. Lastly, we highlight the recent challenges along with several directions of potential research that could fill the gaps in these domains for researchers and developers.

SecDH: Security of COVID-19 images based on data hiding with PCA.

Nowadays, image security and copyright protection become challenging, especially after the COVID-19 pandemic. In the paper, we develop SecDH as a medical data hiding scheme, which can guarantee the security and copyright protection of the COVID-19 images. Firstly, the cover image is normalized, which offers high resistance against the geometric attacks. Secondly, the normalized principal component as embedding factor is computed, which are calculated based on principal component analysis (PCA) between cover and mark image. Thirdly, the medical image is invisibly marked with secret mark based on normalized component, redundant discrete wavelet transform (RDWT) and randomized singular value decomposition (RSVD) is introduced. Finally, Arnold cat map scheme employed to ensure the security of the watermarking system. Under the experimental evaluation, our SecDH tool is not only imperceptible, but also has a satisfactory advantage in robustness and security compared with the traditional watermarking schemes.

COVID-19 Fake News Detection Using Ensemble-Based Deep Learning Model

Fake news on various medicines, foods, and vaccinations relating to the COVID-19 pandemic has increased dramatically. These fake news reports lead individuals to believe in false and sometimes harmful claims and stories, and they also influence people’s vaccination opinions. Immediately detecting COVID-19 false news can help to reduce the spread of fear, confusion, and potential health risks among citizens. An ensemble-based deep learning model for detecting COVID-19-related fake news on Twitter is proposed in this article. CT-BERT, BERTweet, and roberta are three different models that are fine-tuned on COVID-19-linked text data to separate fake and authentic news. In addition, the proposed ensemble-based model is compared to a variety of standard machine learning and deep learning models. In the detection of COVID-19 fake news from Twitter, the proposed ensemble-based deep learning model achieved state-of-the-art performance with a weighted $F_1$F1-score of 0.99.

Opposing roles for sMAdCAM and IL-15 in COVID-19 associated cellular immune pathology.

Immune cell dysregulation and lymphopenia characterize COVID-19 pathology in moderate to severe disease. While underlying inflammatory factors have been extensively studied, homeostatic and mucosal migratory signatures remain largely unexplored as causative factors. In this study, we evaluated the association of circulating IL-6, soluble mucosal addressin cell adhesion molecule (sMAdCAM), and IL-15 with cellular dysfunction characterizing mild and hypoxemic stages of COVID-19. A cohort of SARS-CoV-2 infected individuals (n = 130) at various stages of disease progression together with healthy controls (n = 16) were recruited from COVID Care Centres (CCCs) across Mumbai, India. Multiparametric flow cytometry was used to perform in-depth immune subset characterization and to measure plasma IL-6 levels. sMAdCAM, IL-15 levels were quantified using ELISA. Distinct depletion profiles, with relative sparing of CD8 effector memory and CD4+ regulatory T cells, were observed in hypoxemic disease within the lymphocyte compartment. An apparent increase in the frequency of intermediate monocytes characterized both mild as well as hypoxemic disease. IL-6 levels inversely correlated with those of sMAdCAM and both markers showed converse associations with observed lympho-depletion suggesting opposing roles in pathogenesis. Interestingly, IL-15, a key cytokine involved in lymphocyte activation and homeostasis, was detected in symptomatic individuals but not in healthy controls or asymptomatic cases. Further, plasma IL-15 levels negatively correlated with T, B, and NK count suggesting a compensatory production of this cytokine in response to the profound lymphopenia. Finally, higher levels of plasma IL-15 and IL-6, but not sMAdCAM, were associated with a longer duration of hospitalization.

Deep-AVPpred: Artificial Intelligence Driven Discovery of Peptide Drugs for Viral Infections.

Rapid increase in viral outbreaks has resulted in the spread of viral diseases in diverse species and across geographical boundaries. The zoonotic viral diseases have greatly affected the well-being of humans, and the COVID-19 pandemic is a burning example. The existing antivirals have low efficacy, severe side effects, high toxicity, and limited market availability. As a result, natural substances have been tested for antiviral activity. The host defense molecules like antiviral peptides (AVPs) are present in plants and animals and protect them from invading viruses. However, obtaining AVPs from natural sources for preparing synthetic peptide drugs is expensive and time-consuming. As a result, an in-silico model is required for identifying new AVPs. We proposed Deep-AVPpred, a deep learning classifier for discovering AVPs in protein sequences, which utilises the concept of transfer learning with a deep learning algorithm. The proposed classifier outperformed state-of-the-art classifiers and achieved approximately 94% and 93% precision on validation and test sets, respectively. The high precision indicates that Deep-AVPpred can be used to propose new AVPs for synthesis and experimentation. By utilising Deep-AVPpred, we identified novel AVPs in human interferons- α family proteins. These AVPs can be chemically synthesised and experimentally verified for their antiviral activity against different viruses. The Deep-AVPpred is deployed as a web server and is made freely available at https://deep-avppred.anvil.app, which can be utilised to predict novel AVPs for developing antiviral compounds for use in human and veterinary medicine.

Aptamer-based diagnostic and therapeutic approaches in animals: Current potential and challenges.

Fast and precise diagnosis of infectious and non-infectious animal diseases and their targeted treatments are of utmost importance for their clinical management. The existing biochemical, serological and molecular methods of disease diagnosis need improvement in their specificity, sensitivity and cost and, are generally not amenable for being used as points-of-care (POC) device. Further, with dramatic changes in environment and farm management practices, one should also arm ourselves and prepare for emerging and re-emerging animal diseases such as cancer, prion diseases, COVID-19, influenza etc. Aptamer - oligonucleotide or short peptides that can specifically bind to target molecules - have increasingly become popular in developing biosensors for sensitive detection of analytes, pathogens (bacteria, virus, fungus, prions), drug residues, toxins and, cancerous cells. They have also been proven successful in the cellular delivery of drugs and targeted therapy of infectious diseases and physiological disorders. However, the in vivo application of aptamer-mediated biosensing and therapy in animals has been limited. This paper reviews the existing reports on the application of aptamer-based biosensors and targeted therapy in animals. It also dissects the various modifications to aptamers that were found to be successful in in vivo application of the aptamers in diagnostics and therapeutics. Finally, it also highlights major challenges and future directions in the application of aptamers in the field of veterinary medicine.

Physical Activity Patterns in India Stratified by Zones, Age, Region, BMI and Implications for COVID-19: A Nationwide Study.

RATIONALE: India has a high prevalence of noncommunicable diseases (NCDs), which can be lowered by regular physical activity. To understand this association, recent population data is required which is representative of all the states and union territories of the country. OBJECTIVE: We aimed to investigate the patterns of physical activity in India, stratified by zones, body mass index (BMI), urban, rural areas, and gender. METHOD: We present the analysis of physical activity status from the data collected during the phase 1 of a pan-India study. This (Niyantrita Madhumeha Bharata 2017) was a multicenter pan-India cluster sampled trial with dual objectives. A survey to identify all individuals at a high risk for diabetes, using a validated instrument called the Indian Diabetes Risk Score (IDRS), was followed by a two-armed randomized yoga-based lifestyle intervention for the primary prevention of diabetes. The physical activity was scored as per IDRS (vigorous exercise or strenuous at work = 0, moderate exercise at home/work = 10, mild exercise at home/work = 20, no exercise = 30). This was done in a selected cluster using a mobile application. A weighted prevalence was calculated based on the nonresponse rate and design weight. RESULTS: We analyzed the data from 2,33,805 individuals; the mean age was 41.4 years (SD 13.4). Of these, 50.6% were females and 49.4% were males; 45.8% were from rural areas and 54% from urban areas. The BMI was 24.7 ± 4.6 kg/m2. Briefly, 20% were physically inactive and 57% of the people were either inactive or mildly active. 21.2% of females were found physically inactive, whereas 19.2% of males were inactive. Individuals living in urban localities were proportionately more inactive (21.7% vs. 18.8%) or mildly active (38.9% vs. 34.8%) than the rural people. Individuals from the central (29.6%) and south zones (28.6%) of the country were also relatively inactive, in contrast to those from the northwest zone (14.2%). The known diabetics were found to be physically inactive (28.3% vs. 19.8%) when compared with those unaware of their diabetic status. CONCLUSION: 20% and 37% of the population in India are not active or mildly active, respectively, and thus 57% of the surveyed population do not meet the physical activity regimen recommended by the World Health Organization. This puts a large Indian population at risk of developing various NCDs, which are being increasingly reported to be vulnerable to COVID-19 infections. India needs to adopt the four strategic objectives recommended by the World Health Organization for reducing the prevalence of physical inactivity.

Targeting SARS-CoV-2: a systematic drug repurposing approach to identify promising inhibitors against 3C-like proteinase and 2'-O-ribose methyltransferase.

The recent pandemic associated with SARS-CoV-2, a virus of the Coronaviridae family, has resulted in an unprecedented number of infected people. The highly contagious nature of this virus makes it imperative for us to identify promising inhibitors from pre-existing antiviral drugs. Two druggable targets, namely 3C-like proteinase (3CLpro) and 2'-O-ribose methyltransferase (2'-O-MTase) were selected in this study due to their indispensable nature in the viral life cycle. 3CLpro is a cysteine protease responsible for the proteolysis of replicase polyproteins resulting in the formation of various functional proteins, whereas 2'-O-MTase methylates the ribose 2'-O position of the first and second nucleotide of viral mRNA, which sequesters it from the host immune system. The selected drug target proteins were screened against an in-house library of 123 antiviral drugs. Two promising drug molecules were identified for each protein based on their estimated free energy of binding (ΔG), the orientation of drug molecules in the active site and the interacting residues. The selected protein-drug complexes were then subjected to MD simulation, which consists of various structural parameters to equivalently reflect their physiological state. From the virtual screening results, two drug molecules were selected for each drug target protein [Paritaprevir (ΔG = -9.8 kcal/mol) & Raltegravir (ΔG = -7.8 kcal/mol) for 3CLpro and Dolutegravir (ΔG = -9.4 kcal/mol) and Bictegravir (ΔG = -8.4 kcal/mol) for 2'-OMTase]. After the extensive computational analysis, we proposed that Raltegravir, Paritaprevir, Bictegravir and Dolutegravir are excellent lead candidates for these crucial proteins and they could become potential therapeutic drugs against SARS-CoV-2. Communicated by Ramaswamy H. Sarma.

Stochastic filtering based transmissibility estimation of novel coronavirus.

In this study, the transmissibility estimation of novel coronavirus (COVID-19) has been presented using the generalized fractional-order calculus (FOC) based extended Kalman filter (EKF) and wavelet transform (WT) methods. Initially, the state-space representation for the bats-hosts-reservoir-people (BHRP) model is obtained using a set of fractional order differential equations for the susceptible-exposed-infectious-recovered (SEIR) model. Afterward, the EKF and Kronecker product based WT methods have been applied to the discrete vector representation of the BHRP model. The main advantage of using EKF in this system is that it considers both the process and the measurement noise, which gives better accuracy and probable states, which are the Markovian (processes). The importance of proposed models lies in the fact that these models can accommodate conventional EKF and WT methods as their special cases. Further, we have compared the estimated number of contagious people and recovered people with the actual number of infectious people and recovered people in India and China.

A comprehensive review on promising anti-viral therapeutic candidates identified against main protease from SARS-CoV-2 through various computational methods.

BACKGROUND: The COVID-19 pandemic caused by SARS-CoV-2 has shown an exponential trend of infected people across the planet. Crediting its virulent nature, it becomes imperative to identify potential therapeutic agents against the deadly virus. The 3-chymotrypsin-like protease (3CLpro) is a cysteine protease which causes the proteolysis of the replicase polyproteins to generate functional proteins, which is a crucial step for viral replication and infection. Computational methods have been applied in recent studies to identify promising inhibitors against 3CLpro to inhibit the viral activity. This review provides an overview of promising drug/lead candidates identified so far against 3CLpro through various in silico approaches such as structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS) and drug-repurposing/drug-reprofiling/drug-retasking. Further, the drugs have been classified according to their chemical structures or biological activity into flavonoids, peptides, terpenes, quinolines, nucleoside and nucleotide analogues, protease inhibitors, phenalene and antibiotic derivatives. These are then individually discussed based on the various structural parameters namely estimated free energy of binding (ΔG), key interacting residues, types of intermolecular interactions and structural stability of 3CLpro-ligand complexes obtained from the results of molecular dynamics (MD) simulations. CONCLUSION: The review provides comprehensive information of potential inhibitors identified through several computational methods thus far against 3CLpro from SARS-CoV-2 and provides a better understanding of their interaction patterns and dynamic states of free and ligand-bound 3CLpro structures.

Identification of promising antiviral drug candidates against non-structural protein 15 (NSP15) from SARS-CoV-2: an in silico assisted drug-repurposing study.

The recent COVID-19 pandemic caused by SARS-CoV-2 has recorded a high number of infected people across the globe. The virulent nature of the virus makes it necessary for us to identify promising therapeutic agents in a time-sensitive manner. The current study utilises an in silico based drug repurposing approach to identify potential anti-viral drug candidates targeting non-structural protein 15 (NSP15), i.e. a uridylate specific endoribonuclease of SARS-CoV-2 which plays an indispensable role in RNA processing and viral immune evasion from the host immune system. The NSP15 protein was screened against an in-house library of 123 antiviral drugs obtained from the DrugBank database from which three promising drug candidates were identified based on their estimated binding affinities (ΔG), estimated inhibition constants (Ki), the orientation of drug molecules in the active site and the key interacting residues of NSP15. Molecular dynamics (MD) simulations were performed for the screened drug candidates in complex with NSP15 as well as the apo form of NSP15 to mimic their physiological states. Based on the stable MD simulation trajectories, the binding free energies of the screened NSP15-drug complexes were calculated using the MM/PBSA approach. Two candidate drugs, Simeprevir and Paritaprevir, achieved the lowest binding free energies for NSP15, with a value of -259.522 ± 17.579 and -154.051 ± 33.628 kJ/mol, respectively. In addition, their complexes with NSP15 also exhibited the strongest structural stabilities. Taken together, we propose that Simeprevir and Paritaprevir are promising drug candidates to inhibit NSP15 and may act as potential therapeutic agents against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.