ABSTRACT
We draw the attention of readers and governments to the death rate from coronavirus disease 2019 in Japan, continuing as a fraction of that experienced by many other developed nations. We think this is due to the activity of the powerful, protective lactoperoxidase system (LPO) which prevents serious airborne infections. The LPO system requires iodine, which is liberally provided by the typical Japanese diet but lacking in many others. One might consider the Japanese experience an incredibly large, open-label study exhibiting the preventative power of a high-iodine diet. We predict this favourable trend will continue for Japan because deadly variants of the severe, acute respiratory syndrome coronavirus 2 will be with us, forever.Copyright © 2023 The Scandinavian Foundation for Immunology.
ABSTRACT
The botnet have developed into a severe risk to Internet of Things (IoT) systems as a result of manufacturers ‘insufficient security policies and end users' lack of security awareness. By default, several ports are open and user credentials are left unmodified. ML and DL strategies have been suggested in numerous latest research for identifying and categorising botnet assaults in the IoT context, but still, it has a few issues like high error susceptibility, working only with a large amount of data, poor quality, and data acquisition. This research provided use of a brand-new IoT botnet detector built on an improved hybrid classifier. The proposed work's main components are "pre-processing, feature extraction, feature selection, and attack detection." Following that, the improved Information Gain (IIG) model is used to choose the most reliable characteristics from the received information. To detect an attack, a hybrid classifier is utilized which can be constructed by integrating the optimized Bi-GRU with the Recurrent Neural Network (RNN). To increase the detection accuracy of IoT-BOTNETS, a novel hybrid optimization approach called SMIE (Slime Mould with Immunity Evolution) is created by conceptually integrating two conventional optimization modes: Coronavirus herd immunity optimizer (CHIO) and the Slime mould algorithm. The final output of the hybrid classifier displays the presence or absence of IoT-BOTNET attacks. The projected model's accuracy is 97%, which is 22.6%, 18.5%, 27.8%, 22.6%, and 24.8% higher than the previous models like GWO+ HC, SSO+ HC, WOA+ HC, SMA+ HC, and CHIO+ HC, respectively. © 2022
ABSTRACT
SARS-CoV-2, the causing agent of coronavirus disease (COVID-19), first broke out in Wuhan and rapidly spread worldwide, resulting in a global health emergency. The lack of specific drugs against the coronavirus has made its spread challenging to control. The main protease (Mpro) is a key enzyme of SARS-CoV-2 used as a key target in drug discovery against the coronavirus. Medicines derived from plant phytoconstituents have been widely exploited to treat various diseases. The present study has evaluated the potential of Illicium verum (star anise) phytoconstituents against Mpro by implementing a computational approach. We performed molecular docking and molecular dynamics simulation study with a set of 60 compounds to identify their potential to inhibit the main protease (Mpro) of SARS-CoV-2. DFT study and post dynamics free energy calculations were also performed to strengthen the findings. The identified four compounds by docking study exhibited the highest potential compared to other selected phytoconstituents. Further, density functional theory (DFT) calculation, molecular dynamics simulation and post dynamics MM-GBSA energy calculation predicted Verimol-G as a potential compound, which formed stable interactions through the catalytic dyad residues. The HOMO orbital energy (-0.250038) from DFT and the post dynamics binding free energy calculation (-73.33 Kcal/mol) correlate, suggesting Verimol-G is the best inhibitor compared to the other phytoconstituents. This compound also complies with the ADME properties of drug likeliness. Thus, based on a computational study, we suggest that Verimol G may be developed as a potential inhibitor against the main protease to combat COVID-19.Communicated by Ramaswamy H. Sarma.
ABSTRACT
In the present study, an inventory model with parabolic holding cost, quadratic demand rate, partial backlogging over a time horizon for weibull rate of deteriorating item is proposed. We have supposed the demand rate to be a quadratic function of time. Since the outbreak of pandemic COVID- 19 problem disturbed the political, social, economic, and financial structure of the whole world and both the demand and supply chain management have been affected badly therefore, parabolic holding cost is far better to be taken in account. We explore the inventory system to incorporate three parameters .i.e. purchase cost , backordering cost and cycle time which have been fuzzified using pentagonal fuzzy numbers to obtain total inventory cost. Graded mean integration method and Signed distance method are used to de-fuzzify the total cost. The main aim of the paper is to minimize the total cost per unit time in fuzzy environment. Sensitivity analysis of the optimal solution and its effects have been discussed.
ABSTRACT
Severe acute respiratory syndrome is a viral respiratory infection known as COVID-19, which is caused by a novel coronavirus, called SARS-associated coronavirus-2 (SARS-CoV-2). Considering it as an international concern, WHO declared COVID-19 as the “sixth public health emergency” and has termed it as ‘pandemic”. Currently, no specific drugs are available, and studies about COVID-19 treatment are still in progress. As the world is facing a major challenge in trying to adapt and defend itself against this new pandemic disease, computational intelligence offers a new hope that a cure to this disease might be developed faster than ever before. Many targets for the design of drugs have been already identified, and studies are in progress to explore these potential targets. Computational approaches like virtual screening, molecular docking, machine learning, deep learning and natural language processing (NLP) play a vital role in drug repurposing studies. Repurposing drugs involves discovering novel drug-target interactions and their use against the treatment of different diseases. This strategy has regained significant interest to develop a drug against the COVID-19 considering this pandemic scenario, and offers the best chance to identify potent drugs from the list of approved drugs. Various research efforts are currently focusing on the identification of existing drugs which might be useful in mitigating the infection and some compounds namely favipiravir, remdesivir, lopinavir, hydroxychloroquine etc. are in the final stage of human testing. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The recent outbreak of the SARS-CoV-2 virus leading to the disease COVID 19, a global pandemic has resulted in an unprecedented loss of life and economy worldwide. Hence, there is an urgent need to discover effective drugs to control this pandemic. NSP16 is a methyltransferase that methylates the ribose 2'-O position of the viral nucleotide. Taking advantage of the recently solved structure of NSP16 with its inhibitor, S-Adenosylmethionine, we have virtually screened FDA approved drugs, drug candidates and natural compounds. The compounds with the best docking scores were subjected to molecular dynamics simulations followed by binding free energy calculations using the MM-PBSA method. The known drugs which were identified as potential inhibitors of NSP16 from SARS-CoV-2 included DB02498, DB03909, DB03186, Galuteolin, ZINC000029416466, ZINC000026985532, and ZINC000085537017. DB02498 (Carba-nicotinamide-adenine-dinucleotide) is an approved drug which has been used since the late 1960s in intravenous form to significantly lessen withdrawal symptoms from a variety of drugs and alcohol addicts and it has the best MM-PBSA binding free energy of-12.83 ± 0.52 kcal/mol. The second best inhibitor, Galuteolin is a natural compound that inhibits tyrosinase enzyme with MM-PBSA binding free energy value of -11.21 ± 0.47 kcal/mol. Detailed ligand and protein interactions were analyzed and common residues across SARS-CoV, SARS-CoV-2, and MERS-CoV were identified. We propose Carba-nicotinamide-adenine-dinucleotide and Galuteolin as the potential inhibitors of NSP16. The results in this study can be used for the treatment of COVID-19 and can also form the basis of rational drug design against NSP16 of SARS-CoV-2.
Subject(s)
COVID-19 , Pharmaceutical Preparations , Drug Repositioning , Humans , Molecular Docking Simulation , Molecular Dynamics Simulation , SARS-CoV-2ABSTRACT
The recent outbreak of the SARS-CoV-2 virus leading to the disease COVID 19 has become a global pandemic that is spreading rapidly and has caused a global health emergency. Hence, there is an urgent need of the hour to discover effective drugs to control the pandemic caused by this virus. Under such conditions, it would be imperative to repurpose already known drugs which could be a quick and effective alternative to discovering new drugs. The main protease (Mpro) of SARS-COV-2 is an attractive drug target because of its essential role in the processing of the majority of the non-structural proteins which are translated from viral RNA. Herein, we report the high-throughput virtual screening and molecular docking studies to search for the best potential inhibitors against Mpro from FDA approved drugs available in the ZINC database as well as the natural compounds from the Specs database. Our studies have identified six potential inhibitors of Mpro enzyme, out of which four are commercially available FDA approved drugs (Cobicistat, Iopromide, Cangrelor, and Fortovase) and two are from Specs database of natural compounds (Hopeaphenol and Cyclosieversiodide-A). While Cobicistat and Fortovase are known as HIV drugs, Iopromide is a contrast agent and Cangrelor is an anti-platelet drug. Furthermore, molecular dynamic (MD) simulations using GROMACS were performed to calculate the stability of the top-ranked compounds in the active site of Mpro. After extensive computational studies, we propose that Cobicistat and Hopeaphenol show potential to be excellent drugs that can form the basis of treating COVID-19 disease.Communicated by Ramaswamy H. Sarma.