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...     

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...     

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...     

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.     

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.     

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.     

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.     

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.     

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。     

Modeling the COVID-19 epidemic and awareness diffusion on multiplex networks

The coronavirus disease 2019(COVID-19)has been widely spread around the world,and the control and behavior dynamics are still one of the important research directions in the world.Based on the characteristics of COVID-19’s spread,a coupled disease-awareness model on multiplex networks is proposed in this paper to study and simulate the interaction between the spreading behavior of COVID-19 and related information.In the layer of epidemic spreading,the nodes can be divided into five categories,where the topology of the network represents the physical contact relationship of the population.The topological structure of the upper network shows the information interaction among the nodes,which can be divided into aware and unaware states.Awareness will make people play a positive role in preventing the epidemic diffusion,influencing the spread of the disease.Based on the above model,we have established the state transition equation,through the microscopic Markov chain approach(MMCA),and proposed the propagation threshold calculation method under the epidemic model.Furthermore,MMCA iteration and the Monte Carlo method are simulated on the static network and dynamic network,respectively.The current results will be beneficial to the study of COVID-19,and propose a more rational and effective model for future research on epidemics.     

新型冠状病毒肺炎疫情确诊病例的统计分析及自回归建模

基于安徽省卫生健康委员会截至2020年2月19日公布的800余例新型冠状病毒肺炎病例信息,根据病例中公布的接触史构建确诊患者间的有向传播关系,发现源传染患者中男性居多,被传染患者中女性居多.从病例信息中可知,安徽省新型冠状病毒肺炎疫情的发展从初期的具有武汉居住或接触史的输入病例转入后期本地传播为主的小范围社区传播,且严格的防控隔离措施有效切断了社区内的进一步传播.源传染患者与被传染患者的确诊时间间隔可用G分布拟合,确诊时间间隔的中位数为2 d,平均值为2.67 d.基于有向传播关系的统计特点,构建安徽省疫情发展后期的自回归传播模型,模型仿真结果与疫情发展数据符合.对除湖北省的全国确诊病例数据同样采取自回归建模与仿真,结果仍与疫情发展数据符合.这一发现为控制疫情在湖北省以外区域的防控提供了参考:通过严格的防控措施和隔离措施,疫情在湖北省之外的传播具有很大的黏滞性,多为家庭程度的密切接触传播,且能有效控制新型冠状病毒肺炎在当地的传播深度,有效控制了疫情的蔓延.     

High-performance low field MRI enables visualization of persistent pulmonary damage after COVID-19

The outbreak of coronavirus disease 2019 (COVID-19) with the origin of the spread assumed to be located in Wuhan, China, began in December 2019, and is continuing until now. With the COVID-19 pandemic showing a progressive spread throughout the countries of the world, there is emerging interest for the potential long-term consequences of suffering from a COVID-19 pneumonia. Imaging plays a central role in the diagnosis and management of COVID-19 pneumonia, with chest X-ray examinations and computed tomography (CT) being undoubtedly the modalities most widely used, allowing for a fast and sensitive detection of infiltration patterns associated with COVID-19 pneumonia. For a better understanding of underlying pathomechanisms of pulmonary damage, longitudinal imaging series are warranted, for which CT is of limited usability due to repeated exposure of X-rays. Recent advances in MRI suggested that high-performance low-field MRI might represent a valuable method for pulmonary imaging without the need of radiation exposure. However, so far, low-field MRI has not been applied to study pulmonary damage after COVID-19 pneumonia. We present a case report of a patient who suffered from COVID-19 pneumonia using 0.55 T MRI for follow-up examinations three months after initial infection. Low-field MRI enables a precise visualization of persistent pulmonary changes including ground-glass opacities, which are consistent with CT performed on the same day. Low-field MRI seems to be feasible in the detection of pulmonary involvement in patients with COVID-19 pneumonia and may have the potential for repetitive lung examinations in monitoring the reconvalescence after pulmonary infections.