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.     

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.     

耦合不同年龄层接触模式的新冠肺炎传播模型

搜集广东省自1月23日到2月16日期间944例新冠肺炎样本信息.对确诊人群进行年龄特征分析,将人群分为儿童组(0—5岁)、青少年组(6—19岁)、中青年组(20—64岁)、老年组(65岁及以上),耦合不同年龄层的接触模式,建立离散年龄结构新冠肺炎模型,得出模型的基本再生数及最终规模.通过蒙特卡罗数值算法(MCMC)辨识模型的参数、拟合累计病例数、计算消亡时间、感染峰值及到达时间等有关生物量.研究发现中青年人群感染人数最多;相比于居家模式,社区模式下中青年人群感染峰值上升41%,峰值推迟一周到达.通过分析不同年龄层的最终规模在对应年龄层的占比,发现老年人的易感性较高,青少年的易感性相对较低.在居家模式下,若各年龄层患者能及时就诊,住院峰值将进一步减少,但住院高峰将提前一周到达.此模型可揭示个体接触行为对新冠肺炎的传播的影响,定量评价居家隔离措施的有效性.     

Prediction of epidemics dynamics on networks with partial differential equations: A case study for COVID-19 in China

Since December 2019, the COVID-19 epidemic has repeatedly hit countries around the world due to various factors such as trade, national policies and the natural environment. To closely monitor the emergence of new COVID-19 clusters and ensure high prediction accuracy, we develop a new prediction framework for studying the spread of epidemic on networks based on partial differential equations (PDEs), which captures epidemic diffusion along the edges of a network driven by population flow data. In this paper, we focus on the effect of the population movement on the spread of COVID-19 in several cities from different geographic regions in China for describing the transmission characteristics of COVID-19. Experiment results show that the PDE model obtains relatively good prediction results compared with several typical mathematical models. Furthermore, we study the effectiveness of intervention measures, such as traffic lockdowns and social distancing, which provides a new approach for quantifying the effectiveness of the government policies toward controlling COVID-19 via the adaptive parameters of the model. To our knowledge, this work is the first attempt to apply the PDE model on networks with Baidu Migration Data for COVID-19 prediction.     

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.     

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...     

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.     

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.     

The Impact of the COVID-19 Pandemic on the Unpredictable Dynamics of the Cryptocurrency Market

The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.     

Accurate Evaluation on the Interactions of SARS-CoV-2 with Its Receptor ACE2and Antibodies CR3022/CB6

The spread of the coronavirus disease 2019(COVID-19) caused by severe acute respiratory syndrome coronavirus-2(SARS-CoV-2) has become a global health crisis.The binding affinity of SARS-CoV-2(in particular the receptor binding domain,RBD) to its receptor angiotensin converting enzyme 2(ACE2) and the antibodies is of great importance in understanding the infectivity of COVID-19 and evaluating the candidate therapeutic for COVID-19.We propose a new method based on molecular mechanics/Poisson-Boltzmann surface area(MM/PBSA) to accurately calculate the free energy of SARS-CoV-2 RBD binding to ACE2 and antibodies.The calculated binding free energy of SARS-CoV-2 RBD to ACE2 is-13.3 kcal/mol,and that of SARS-CoV RBD to ACE2 is-11.4 kcal/mol,which agree well with the experimental results of-11.3 kcal/mol and-10.1 kcal/mol,respectively.Moreover,we take two recently reported antibodies as examples,and calculate the free energy of antibodies binding to SARS-CoV-2 RBD,which is also consistent with the experimental findings.Further,within the framework of the modified MM/PBSA,we determine the key residues and the main driving forces for the SARS-CoV-2 RBD/CB6 interaction by the computational alanine scanning method.The present study offers a computationally efficient and numerically reliable method to evaluate the free energy of SARS-CoV-2 binding to other proteins,which may stimulate the development of the therapeutics against the COVID-19 disease in real applications.     

新型冠状病毒肺炎早期时空传播特征分析

通过最新公布的流行病学数据估计了易感者-感染者模型参数,结合百度迁徙数据和公开新闻报道,刻画了疫情前期武汉市人口流动特征,并代入提出的支持人口流动特征的时域差分方程模型进行动力学模拟,得到一些推论:1)未受干预时传染率在一般环境下以95%的置信度位于区间[0.2068,0.2073],拟合优度达到0.999;对应地,基本传染数R0位于区间[2.5510,2.6555];极限环境个案推演的传染率极值为0.2862,相应的R0极值为3.1465;2)百度迁徙规模指数与铁路发送旅客人数的Pearson相关系数达到0.9108,有理由作为人口流动的有效估计;3)提出的模型可有效推演疫情蔓延至外省乃至全国的日期,其中41.38%的预测误差≤1 d,79.31%的预测误差≤3 d,96.55%预测误差≤5 d,总体平均误差约为2.14 d.     

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.     

Piecewise Modeling the Accumulated Daily Growth of COVID-19 Deaths: The Case of the State of São Paulo,Brazil

The pandemic scenery caused by the new coronavirus, called SARS-CoV-2, increased interest in statistical models capable of projecting the evolution of the number of cases (and associated deaths) due to COVID-19 in countries, states and/or cities. This interest is mainly due to the fact that the projections may help the government agencies in making decisions in relation to procedures of prevention of the disease. Since the growth of the number of cases (and deaths) of COVID-19, in general, has presented a heterogeneous evolution over time, it is important that the modeling procedure is capable of identifying periods with different growth rates and proposing an adequate model for each period. Here, we present a modeling procedure based on the fit of a piecewise growth model for the cumulative number of deaths. We opt to focus on the modeling of the cumulative number of deaths because, other than for the number of cases, these values do not depend on the number of diagnostic tests performed. In the proposed approach, the model is updated in the course of the pandemic, and whenever a “new” period of the pandemic is identified, it creates a new sub-dataset composed of the cumulative number of deaths registered from the change point and a new growth model is chosen for that period. Three growth models were fitted for each period: exponential, logistic and Gompertz models. The best model for the cumulative number of deaths recorded is the one with the smallest mean square error and the smallest Akaike information criterion (AIC) and Bayesian information criterion (BIC) values. This approach is illustrated in a case study, in which we model the number of deaths due to COVID-19 recorded in the State of São Paulo, Brazil. The results have shown that the fit of a piecewise model is very effective for explaining the different periods of the pandemic evolution.     

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.     

Multi-scale mechanism of antiviral drug-alike phytoligands from Ayurveda in managing COVID-19 and associated metabolic comorbidities: insights from network pharmacology

The novel coronavirus disease (COVID-19), which emerged in Wuhan, China, is continuously spreading worldwide, creating a huge burden on public health and economy. Ayurveda, the oldest healing schema of Traditional Indian Medicinal (TIM) system, is considered as a promising CAM therapy to combat various diseases/ disorders. To explore the regulatory mechanisms of 3038 Ayurvedic herbs (AHs) against SARS-CoV-2, in this study, multi-targeting and synergistic actions of constituent 34,472 phytochemicals (APCs) are investigated using a comprehensive approach comprising of network pharmacology and molecular docking. Immunomodulatory prospects of antiviral drug-alike potentially effective phytochemicals (PEPs) are presented as a special case study, highlighting the importance of 6 AHs in eliciting the antiviral immunity. By evaluating binding affinity of 292 PEPs against 24 SARS-CoV-2 proteins, we develop and analyze a high-confidence “bi-regulatory network” of 115 PEPs having ability to regulate protein targets in both virus and its host human system. Furthermore, mechanistic actions of PEPs against cardiovascular complications, diabetes mellitus and hypertension are also investigated to address the regulatory potential of AHs in dealing with COVID-19-associated metabolic comorbidities. The study further reports 12 PEPs as promising source of COVID-19 comorbidity regulators.

Graphical abstract

     

A simple statistical physics model for the epidemic with incubation period

Based on the classical SIR model, we derive a simple modification for the dynamics of epidemics with a known incubation period of infection. The model is described by a system of integro-differential equations. Parameters of our model are directly related to epidemiological data. We derive some analytical results, as well as perform numerical simulations. We use the proposed model to analyze COVID-19 epidemic data in Armenia.     

Structure-based virtual screening,in silico docking,ADME properties prediction and molecular dynamics studies for the identification of potential inhibitors against SARS-CoV-2 Mpro

COVID-19 is a viral pandemic caused by SARS-CoV-2. Due to its highly contagious nature, millions of people are getting affected worldwide knocking down the delicate global socio-economic equilibrium. According to the World Health Organization, COVID-19 has affected over 186 million people with a mortality of around 4 million as of July 09, 2021. Currently, there are few therapeutic options available for COVID-19 control. The rapid mutations in SARS-CoV-2 genome and development of new virulent strains with increased infection and mortality among COVID-19 patients, there is a great need to discover more potential drugs for SARS-CoV-2 on a priority basis. One of the key viral enzymes responsible for the replication and maturation of SARS-CoV-2 is M^pro protein. In the current study, structure-based virtual screening was used to identify four potential ligands against SARS-CoV-2 M^pro from a set of 8,722 ASINEX library compounds. These four compounds were evaluated using ADME filter to check their ADME profile and druggability, and all the four compounds were found to be within the current pharmacological acceptable range. They were individually docked to SARS-CoV-2 M^pro protein to assess their molecular interactions. Further, molecular dynamics (MD) simulations was carried out on protein–ligand complex using Desmond at 100 ns to explore their binding conformational stability. Based on RMSD, RMSF and hydrogen bond interactions, it was found that the stability of protein–ligand complex was maintained throughout the entire 100 ns simulations for all the four compounds. Some of the key ligand amino acid residues participated in stabilizing the protein–ligand interactions includes GLN 189, SER 10, GLU 166, ASN 142 with PHE 66 and TRP 132 of SARS-CoV-2 M^pro. Further optimization of these compounds could lead to promising drug candidates for SARS-CoV-2 M^pro target.

     

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.     

New insights into the catalytic mechanism of the SARS-CoV-2 main protease: an ONIOM QM/MM approach

Molecular Diversity - SARS-CoV-2 Mpro, also known as the main protease or 3C-like protease, is a key enzyme involved in the replication process of the virus that is causing the COVID-19 pandemic....     

Inference on COVID-19 Epidemiological Parameters Using Bayesian Survival Analysis

The need to provide accurate predictions in the evolution of the COVID-19 epidemic has motivated the development of different epidemiological models. These models require a careful calibration of their parameters to capture the dynamics of the phenomena and the uncertainty in the data. This work analyzes different parameters related to the personal evolution of COVID-19 (i.e., time of recovery, length of stay in hospital and delay in hospitalization). A Bayesian Survival Analysis is performed considering the age factor and period of the epidemic as fixed predictors to understand how these features influence the evolution of the epidemic. These results can be easily included in the epidemiological SIR model to make prediction results more stable.