Identification of potential Mpro inhibitors for the treatment of COVID-19 by using systematic virtual screening approach

Molecular Diversity - The Corona virus Disease (COVID-19) is caused because of novel coronavirus (SARS-CoV-2) pathogen detected in China for the first time, and from there it spread across the...     

SARS病毒攻击肺泡组织的耗氧机理研究

本文对SARS病毒攻击肺泡组织后的耗氧机理进行了理论研究。建立支气管末端肺泡组织内病毒和氧扩散模型,从工程角度解释了SARS攻击肺部的原因,特别讨论了影响肺部组织氧传输的因素。计算结果有助于更好地理解和提出抑制肺泡组织处氧耗乃至病毒攻击强度的措施。     

Therapeutics for COVID-19: from computation to practices—where we are,where we are heading to

Molecular Diversity - After the 1918 Spanish Flu pandemic caused by the H1N1 virus, the recent coronavirus disease 2019 (COVID-19) brought us to the time of serious global health catastrophe....     

The Use of High-Power Electromagnetic Pulses on Bacteria and Viruses

Doklady Physics - The hypothesis about the possibility of using strong electromagnetic pulses for effective action on bacteria and viruses is discussed. A simple coronavirus model that allows us to...     

Systematic virtual screening in search of SARS CoV-2 inhibitors against spike glycoprotein: pharmacophore screening,molecular docking,ADMET analysis and MD simulations

Molecular Diversity - In the absence of efficient anti-viral medications, the coronavirus disease 2019 (COVID-19), stemming from severe acute respiratory syndrome coronavirus-2 (SARS CoV-2), has...     

A new glimpse on the active site of SARS-CoV-2 3CLpro,coupled with drug repurposing study

Molecular Diversity - Coronavirus disease 2019 (COVID-19) is caused by novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Its main protease, 3C-like protease (3CLpro), is an...     

Exploring naphthyl derivatives as SARS-CoV papain-like protease (PLpro) inhibitors and its implications in COVID-19 drug discovery

Molecular Diversity - Novel coronavirus disease 2019 (COVID-19) emerges as a serious threat to public health globally. The rapid spreading of COVID-19, caused by severe acute respiratory syndrome...     

Identifying structural–functional analogue of GRL0617, the only well-established inhibitor for papain-like protease (PLpro) of SARS-CoV2 from the pool of fungal metabolites using docking and molecular dynamics simulation

Molecular Diversity - The non-structural protein (nsp)-3 of SARS-CoV2 coronavirus is sought to be an essential target protein which is also named as papain-like protease (PLpro). This protease...     

Tackling COVID-19 Using Antiviral Nanocoating's—Recent Progress and Future Challenges

In the current situation of the global coronavirus disease 2019 (COVID-19) pandemic, there is a worldwide demand for the protection of regular handling surfaces from viral transmission to restrict the spread of COVID-19 infection. To tackle this challenge, researchers and scientists are continuously working on novel antiviral nanocoatings to make various substrates capable of arresting the spread of such pathogens. These nanocoatings systems include metal/metal oxide nanoparticles, electrospun antiviral polymer nanofibers, antiviral polymer nanoparticles, graphene family nanomaterials, and etched nanostructures. The antiviral mechanism of these systems involves depletion of the spike glycoprotein that anchors to surfaces by the nanocoating and makes the spike glycoprotein and viral nucleotides inactive; however, the nature of the interaction between the spike proteins and virus depends on the type of nanostructure and a surface charge over the coating surface. In this article, the current scenario of COVID-19 and how it can be tackled using antiviral nanocoatings from the further transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), along with their different mode of action, are discussed. Additionally, it is also highlighted different types of nanocoatings developed for various substrates to encounter transmission of SARS-CoV-2, future research areas along with the current challenges related to it, and how these challenges can be resolved.     

Sonocrystallization of a novel ZIF/zeolite composite adsorbent with high chemical stability for removal of the pharmaceutical pollutant azithromycin from contaminated water

Water pollution management, reduction, and elimination are critical challenges of the current era that threaten millions of lives. By spreading the coronavirus in December 2019, the use of antibiotics, such as azithromycin increased. This drug was not metabolized, and entered the surface waters. ZIF-8/Zeolit composite was made by the sonochemical method. Furthermore, the effect of pH, the regeneration of adsorbents, kinetics, isotherms, and thermodynamics were attended. The adsorption capacity of zeolite, ZIF-8, and the composite ZIF-8/Zeolite were 22.37, 235.3, and 131 mg/g, respectively. The adsorbent reaches the equilibrium in 60 min, and at pH = 8. The adsorption process was spontaneous, endothermic associated with increased entropy. The results of the experiment were analyzed using Langmuir isotherms and pseudo-second order kinetic models with a R2 of 0.99, and successfully removing the composite by 85% in 10 cycles. It indicated that the maximum amount of drug could be removed with a small amount of composite.     

Chest MRI of patients with COVID-19

During the pandemic of novel coronavirus infection (COVID-19), computed tomography (CT) showed its effectiveness in diagnosis of coronavirus infection. However, ionizing radiation during CT studies causes concern for patients who require dynamic observation, as well as for examination of children and young people. For this retrospective study, we included 15 suspected for COVID-19 patients who were hospitalized in April 2020, Russia. There were 4 adults with positive polymerase chain reaction (PCR) test for COVID-19. All patients underwent magnetic resonance imaging (MRI) examinations using MR-LUND PROTOCOL: Single-shot Fast Spin Echo (SSFSE), LAVA 3D and IDEAL 3D, Echo-planar imaging (EPI) diffusion-weighted imaging (DWI) and Fast Spin Echo (FSE) T2 weighted imaging (T2WI). On T2WI changes were identified in 9 (60,0%) patients, on DWI – in 5 (33,3%) patients. In 5 (33,3%) patients lesions of the parenchyma were visualized on T2WI and DWI simultaneously. At the same time, 4 (26.7%) patients had changes in lung tissue only on T2WI. (P(McNemar) = 0,125; OR = 0,00 (95%); kappa = 0,500). In those patients who had CT scan, the changes were comparable to MRI. The results showed that in case of CT is not available, it is advisable to conduct a chest MRI for patients with suspected or confirmed COVID-19. Considering that T2WI is a fluid-sensitive sequence, if imaging for the lung infiltration is required, we can recommend the abbreviated MRI protocol consisting of T2 and T1 WI. These data may be applicable for interpreting other studies, such as thoracic spine MRI, detecting signs of viral pneumonia of asymptomatic patients. MRI can detect features of viral pneumonia.     

Parameter inference in small world network disease models with approximate Bayesian Computational methods

Small world network models have been effective in capturing the variable behaviour of reported case data of the SARS coronavirus outbreak in Hong Kong during 2003. Simulations of these models have previously been realized using informed “guesses” of the proposed model parameters and tested for consistency with the reported data by surrogate analysis. In this paper we attempt to provide statistically rigorous parameter distributions using Approximate Bayesian Computation sampling methods. We find that such sampling schemes are a useful framework for fitting parameters of stochastic small world network models where simulation of the system is straightforward but expressing a likelihood is cumbersome.     

新型冠状病毒肺炎的流行病学参数与模型

一种新型冠状病毒感染导致的肺炎自2019年12月至今在我国以及200多个国家和地区传播.本文旨在介绍近期关于新型冠状病毒肺炎的几个重要流行病学参数的研究进展和估计方法,包括基本再生数、潜伏期和代间隔,同时还介绍两个动力学模型及其结果.这些参数刻画了新型冠状病毒肺炎的传播特点,影响控制策略的制定和有效性.简要来说,新型冠状病毒肺炎的基本再生数R0的中位数为2.6,潜伏期均值约为5.0 d,代间隔均值约为5.5 d.这表明新型冠状病毒肺炎传播速度快.诸如对确诊病人的隔离治疗、对疑似病例的隔离、对密切接触者的追踪、对疾病信息的宣传和采取自我防护等防控措施能有效降低疾病暴发的风险和规模.     

Network Autoregressive Model for the Prediction of COVID-19 Considering the Disease Interaction in Neighboring Countries

Predicting the way diseases spread in different societies has been thus far documented as one of the most important tools for control strategies and policy-making during a pandemic. This study is to propose a network autoregressive (NAR) model to forecast the number of total currently infected cases with coronavirus disease 2019 (COVID-19) in Iran until the end of December 2021 in view of the disease interactions within the neighboring countries in the region. For this purpose, the COVID-19 data were initially collected for seven regional nations, including Iran, Turkey, Iraq, Azerbaijan, Armenia, Afghanistan, and Pakistan. Thenceforth, a network was established over these countries, and the correlation of the disease data was calculated. Upon introducing the main structure of the NAR model, a mathematical platform was subsequently provided to further incorporate the correlation matrix into the prediction process. In addition, the maximum likelihood estimation (MLE) was utilized to determine the model parameters and optimize the forecasting accuracy. Thereafter, the number of infected cases up to December 2021 in Iran was predicted by importing the correlation matrix into the NAR model formed to observe the impact of the disease interactions in the neighboring countries. In addition, the autoregressive integrated moving average (ARIMA) was used as a benchmark to compare and validate the NAR model outcomes. The results reveal that COVID-19 data in Iran have passed the fifth peak and continue on a downward trend to bring the number of total currently infected cases below 480,000 by the end of 2021. Additionally, 20%, 50%, 80% and 95% quantiles are provided along with the point estimation to model the uncertainty in the forecast.     

Comparative Analysis of Different Univariate Forecasting Methods in Modelling and Predicting the Romanian Unemployment Rate for the Period 2021–2022

Unemployment has risen as the economy has shrunk. The coronavirus crisis has affected many sectors in Romania, some companies diminishing or even ceasing their activity. Making forecasts of the unemployment rate has a fundamental impact and importance on future social policy strategies. The aim of the paper is to comparatively analyze the forecast performances of different univariate time series methods with the purpose of providing future predictions of unemployment rate. In order to do that, several forecasting models (seasonal model autoregressive integrated moving average (SARIMA), self-exciting threshold autoregressive (SETAR), Holt–Winters, ETS (error, trend, seasonal), and NNAR (neural network autoregression)) have been applied, and their forecast performances have been evaluated on both the in-sample data covering the period January 2000–December 2017 used for the model identification and estimation and the out-of-sample data covering the last three years, 2018–2020. The forecast of unemployment rate relies on the next two years, 2021–2022. Based on the in-sample forecast assessment of different methods, the forecast measures root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percent error (MAPE) suggested that the multiplicative Holt–Winters model outperforms the other models. For the out-of-sample forecasting performance of models, RMSE and MAE values revealed that the NNAR model has better forecasting performance, while according to MAPE, the SARIMA model registers higher forecast accuracy. The empirical results of the Diebold–Mariano test at one forecast horizon for out-of-sample methods revealed differences in the forecasting performance between SARIMA and NNAR, of which the best model of modeling and forecasting unemployment rate was considered to be the NNAR model.     

Impact of Coronavirus Outbreaks on Science and Society: Insights from Temporal Bibliometry of SARS and COVID-19

A global event such as the COVID-19 crisis presents new, often unexpected responses that are fascinating to investigate from both scientific and social standpoints. Despite several documented similarities, the coronavirus pandemic is clearly distinct from the 1918 flu pandemic in terms of our exponentially increased, almost instantaneous ability to access/share information, offering an unprecedented opportunity to visualise rippling effects of global events across space and time. Personal devices provide “big data” on people’s movement, the environment and economic trends, while access to the unprecedented flurry in scientific publications and media posts provides a measure of the response of the educated world to the crisis. Most bibliometric (co-authorship, co-citation, or bibliographic coupling) analyses ignore the time dimension, but COVID-19 has made it possible to perform a detailed temporal investigation into the pandemic. Here, we report a comprehensive network analysis based on more than 20,000 published documents on viral epidemics, authored by over 75,000 individuals from 140 nations in the past one year of the crisis. Unlike the 1918 flu pandemic, access to published data over the past two decades enabled a comparison of publishing trends between the ongoing COVID-19 pandemic and those of the 2003 SARS epidemic to study changes in thematic foci and societal pressures dictating research over the course of a crisis.     

Boosting COVID-19 Image Classification Using MobileNetV3 and Aquila Optimizer Algorithm

Currently, the world is still facing a COVID-19 (coronavirus disease 2019) classified as a highly infectious disease due to its rapid spreading. The shortage of X-ray machines may lead to critical situations and delay the diagnosis results, increasing the number of deaths. Therefore, the exploitation of deep learning (DL) and optimization algorithms can be advantageous in early diagnosis and COVID-19 detection. In this paper, we propose a framework for COVID-19 images classification using hybridization of DL and swarm-based algorithms. The MobileNetV3 is used as a backbone feature extraction to learn and extract relevant image representations as a DL model. As a swarm-based algorithm, the Aquila Optimizer (Aqu) is used as a feature selector to reduce the dimensionality of the image representations and improve the classification accuracy using only the most essential selected features. To validate the proposed framework, two datasets with X-ray and CT COVID-19 images are used. The obtained results from the experiments show a good performance of the proposed framework in terms of classification accuracy and dimensionality reduction during the feature extraction and selection phases. The Aqu feature selection algorithm achieves accuracy better than other methods in terms of performance metrics.     

新型冠状病毒S蛋白在金纳米粒子中的表面增强拉曼效应

表面增强拉曼光谱技术因其高灵敏度、操作简单、快速检测等优点,被广泛用于病毒检测方面。国内外的病毒拉曼检测研究主要集中在检测病毒核酸以及组成核酸的各种碱基的表面增强拉曼光谱(SERS),但少见对病毒蛋白的SERS检测。以新型冠状病毒（SARS-CoV-2）的S蛋白为检测对象,采用无标记SERS检测方法,对比SARS-CoV-2固态、饱和液态S蛋白的普通拉曼光谱和选用40 nm金纳米粒子为基底的SARS-CoV-2低浓度S蛋白SERS光谱。结果表明,以40 nm金纳米粒子为基底,采用SERS技术检测SARS-CoV-2的S蛋白是完全可行的。SARS-CoV-2的S蛋白分子中的羧基与金纳米粒子发生了分子增强,氨基与金纳米粒子发生了电磁增强,从而使得SARS-CoV-2的S蛋白拉曼效应得到了增强,并使得峰位发生一定移动。实验获得了较好的SARS-CoV-2低浓度S蛋白SERS光谱,为建立敏感、特异、快速的SARS-CoV-2检测新技术提供了一种方法。     

Understanding the Impact of Walkability,Population Density,and Population Size on COVID-19 Spread: A Pilot Study of the Early Contagion in the United States

The novel coronavirus disease 2019 (COVID-19) pandemic is an unprecedented global event that has been challenging governments, health systems, and communities worldwide. Available data from the first months indicated varying patterns of the spread of COVID-19 within American cities, when the spread was faster in high-density and walkable cities such as New York than in low-density and car-oriented cities such as Los Angeles. Subsequent containment efforts, underlying population characteristics, variants, and other factors likely affected the spread significantly. However, this work investigates the hypothesis that urban configuration and associated spatial use patterns directly impact how the disease spreads and infects a population. It follows work that has shown how the spatial configuration of urban spaces impacts the social behavior of people moving through those spaces. It addresses the first 60 days of contagion (before containment measures were widely adopted and had time to affect spread) in 93 urban counties in the United States, considering population size, population density, walkability, here evaluated through walkscore, an indicator that measures the density of amenities, and, therefore, opportunities for population mixing, and the number of confirmed cases and deaths. Our findings indicate correlations between walkability, population density, and COVID-19 spreading patterns but no clear correlation between population size and the number of cases or deaths per 100 k habitants. Although virus spread beyond these initial cases may provide additional data for analysis, this study is an initial step in understanding the relationship between COVID-19 and urban configuration.