Preventive and therapeutic benefits of nelfinavir in rhesus macaques and human beings infected with SARS-CoV-2.

Effective drugs with broad spectrum safety profile to all people are highly expected to combat COVID-19 caused by SARS-CoV-2. Here we report that nelfinavir, an FDA approved drug for the treatment of HIV infection, is effective against SARS-CoV-2 and COVID-19. Preincubation of nelfinavir could inhibit the activity of the main protease of the SARS-CoV-2 (IC50 = 8.26 µM), while its antiviral activity in Vero E6 cells against a clinical isolate of SARS-CoV-2 was determined to be 2.93 µM (EC50). In comparison with vehicle-treated animals, rhesus macaque prophylactically treated with nelfinavir had significantly lower temperature and significantly reduced virus loads in the nasal and anal swabs of the animals. At necropsy, nelfinavir-treated animals had a significant reduction of the viral replication in the lungs by nearly three orders of magnitude. A prospective clinic study with 37 enrolled treatment-naive patients at Shanghai Public Health Clinical Center, which were randomized (1:1) to nelfinavir and control groups, showed that the nelfinavir treatment could shorten the duration of viral shedding by 5.5 days (9.0 vs. 14.5 days, P = 0.055) and the duration of fever time by 3.8 days (2.8 vs. 6.6 days, P = 0.014) in mild/moderate COVID-19 patients. The antiviral efficiency and clinical benefits in rhesus macaque model and in COVID-19 patients, together with its well-established good safety profile in almost all ages and during pregnancy, indicated that nelfinavir is a highly promising medication with the potential of preventative effect for the treatment of COVID-19.

High-throughput screening of SARS-CoV-2 main and papain-like protease inhibitors.

The global COVID-19 coronavirus pandemic has infected over 109 million people, leading to over 2 million deaths up to date and still lacking of effective drugs for patient treatment. Here, we screened about 1.8 million small molecules against the main protease (Mpro) and papain like protease (PLpro), two major proteases in severe acute respiratory syndrome-coronavirus 2 genome, and identified 1851Mpro inhibitors and 205 PLpro inhibitors with low nmol/l activity of the best hits. Among these inhibitors, eight small molecules showed dual inhibition effects on both Mpro and PLpro, exhibiting potential as better candidates for COVID-19 treatment. The best inhibitors of each protease were tested in antiviral assay, with over 40% of Mpro inhibitors and over 20% of PLpro inhibitors showing high potency in viral inhibition with low cytotoxicity. The X-ray crystal structure of SARS-CoV-2 Mpro in complex with its potent inhibitor 4a was determined at 1.8 Å resolution. Together with docking assays, our results provide a comprehensive resource for future research on anti-SARS-CoV-2 drug development.

A new method for spatio-temporal transmission prediction of COVID-19.

COVID-19 is the most serious public health event of the 21st century and has had a huge impact across the world. The spatio-temporal pattern analysis and simulation of epidemic spread have become the focus of current research. LSTM model has made a lot of achievements in the prediction of infectious diseases by virtue of its advantages in time prediction, but lacks the spatial expression. CA model plays an important role in epidemic spatial propagation modeling due to its unique evolution characteristics from local to global. However, no existing studies of CA have considered long-term dependence due to the impact of time changes on the evolution of the epidemic, and few have modeled using location data from actual diagnosed patients. Therefore, we proposed a LSTM-CA model to solve above mentioned problems. Base on the advantages of LSTM in temporal level and CA in spatial level, LSTM and CA are integrated from the spatio-temporal perspective of geography based on the fine-grained characteristics of epidemic data. The method divides the study area into regular grids, simulates the spatial interactions between neighborhood cells with the help of CA model, and extracts the parameters affecting the transition probability in CA with the help of LSTM model to assist evolution. Simulations are conducted in Python 3.4 to model the propagation of COVID-19 between Feb, 6 to Mar 20, 2020 in China. Experimental results show that, LSTM-CA performs a higher statistical accuracy than LSTM and spatial accuracy than CA, which could demonstrate the effectiveness of the proposed model. This method could be universal for the temporal and spatial transmission of major public health events. Especially in the early stage of the epidemic, we can quickly understand its development trend and cycle, so as to provide an important reference for epidemic prevention and control and public sentiment counseling.

Simulation and forecasting models of COVID-19 taking into account spatio-temporal dynamic characteristics: A review

The COVID-19 epidemic has caused more than 6.4 million deaths to date and has become a hot topic of interest in different disciplines. According to bibliometric analysis, more than 340,000 articles have been published on the COVID-19 epidemic from the beginning of the epidemic until recently. Modeling infectious diseases can provide critical planning and analytical tools for outbreak control and public health research, especially from a spatio-temporal perspective. However, there has not been a comprehensive review of the developing process of spatio-temporal dynamic models. Therefore, the aim of this study is to provide a comprehensive review of these spatio-temporal dynamic models for dealing with COVID-19, focusing on the different model scales. We first summarized several data used in the spatio-temporal modeling of the COVID-19, and then, through literature review and summary, we found that the existing COVID-19 spatio-temporal models can be divided into two categories: macro-dynamic models and micro-dynamic models. Typical representatives of these two types of models are compartmental and metapopulation models, cellular automata (CA), and agent-based models (ABM). Our results show that the modeling results are not accurate enough due to the unavailability of the fine-grained dataset of COVID-19. Furthermore, although many models have been developed, many of them focus on short-term prediction of disease outbreaks and lack medium- and long-term predictions. Therefore, future research needs to integrate macroscopic and microscopic models to build adaptive spatio-temporal dynamic simulation models for the medium and long term (from months to years) and to make sound inferences and recommendations about epidemic development in the context of medical discoveries, which will be the next phase of new challenges and trends to be addressed. In addition, there is still a gap in research on collecting fine-grained spatial-temporal big data based on cloud platforms and crowdsourcing technologies to establishing world model to battle the epidemic.

Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures.

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Heparin: An old drug for new clinical applications.

Heparin, an old but first-line anticoagulant, has been used over a century. It is a heterogeneous, linear, highly sulfated, anionic glycosaminoglycan with a broad distribution in relative molecular weight and charge density. These structural properties allow heparin to selectively interact with multiple proteins, leading to heparin's various pharmacological functions, such as anticoagulant, anti-viral, anti-tumor and anti-inflammatory activities. Clinical data suggest that unfractionated heparin or low molecule weight heparin could decrease mortality in COVID-19 patients with sepsis-induced hypercoagulation through the anticoagulant, anti-viral and anti-inflammatory activities of these drugs. Thus, the non-anticoagulant activity of heparin has again aroused attention. This review highlights recent advances in the preparation of heparin-derived drugs and clinical research on its non-anticoagulant properties over the past decade, to further the development and utilization of these important drugs.

Discovery of highly potent SARS-CoV-2 Mpro inhibitors based on benzoisothiazolone scaffold.

The COVID-19 pandemic has drastically impacted global economies and public health. Although vaccine development has been successful, it was not sufficient against more infectious mutant strains including the Delta variant indicating a need for alternative treatment strategies such as small molecular compound development. In this work, a series of SARS-CoV-2 main protease (Mpro) inhibitors were designed and tested based on the active compound from high-throughput diverse compound library screens. The most efficacious compound (16b-3) displayed potent SARS-CoV-2 Mpro inhibition with an IC50 value of 116 nM and selectivity against SARS-CoV-2 Mpro when compared to PLpro and RdRp. This new class of compounds could be used as potential leads for further optimization in anti COVID-19 drug discovery.

The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method.

Corona Virus Disease 2019 (COVID-19) has spread rapidly to countries all around the world from the end of 2019, which caused a great impact on global health and has had a huge impact on many countries. Since there is still no effective treatment, it is essential to making effective predictions for relevant departments to make responses and arrangements in advance. Under the limited data, the prediction error of LSTM model will increase over time, and its prone to big bias for medium- and long-term prediction. To overcome this problem, our study proposed a LSTM-Markov model, which uses Markov model to reduce the prediction error of LSTM model. Based on confirmed case data in the US, Britain, Brazil and Russia, we calculated the training errors of LSTM and constructed the probability transfer matrix of the Markov model by the errors. And finally, the prediction results were obtained by combining the output data of LSTM model with the prediction errors of Markov Model. The results show that: compared with the prediction results of the classical LSTM model, the average prediction error of LSTM-Markov is reduced by more than 75%, and the RMSE is reduced by more than 60%, the mean [Formula: see text] of LSTM-Markov is over 0.96. All those indicators demonstrate that the prediction accuracy of proposed LSTM-Markov model is higher than that of the LSTM model to reach more accurate prediction of COVID-19.

Safety, tolerability, pharmacokinetic characteristics, and immunogenicity of MW33: a Phase 1 clinical study of the SARS-CoV-2 RBD-targeting monoclonal antibody.

MW33 is a fully humanized IgG1κ monoclonal neutralizing antibody, and may be used for the prevention and treatment of coronavirus disease 2019 (COVID-19). We conducted a randomized, double-blind, placebo-controlled, single-dose, dose-escalation Phase 1 study to evaluate the safety, tolerability, pharmacokinetics (PK), and immunogenicity of MW33. Healthy adults aged 18-45 years were sequentially enrolled into the 4, 10, 20, 40, and 60 mg/kg dose groups and infused with MW33 over 60 ± 15 min and followed for 85 days. All 42 enrolled participants completed the MW33 infusion, and 40 participants completed the 85-day follow-up period. 34 participants received a single infusion of 4 (n = 2), 10 (n = 8), 20 (n = 8), 40 (n = 8), and 60 mg/kg (n = 8) of MW33. 27 subjects in the test groups experienced 78 adverse events (AEs) post-dose, with an incidence of 79.4% (27/34). The most common AEs included abnormal laboratory test results, vascular and lymphatic disorders, and infectious diseases. The severity of AEs was mainly Grade 1 (92 AEs), and three Grade 2 and one Grade 4. The main PK parameters, maximum concentration (Cmax), and area under the concentration-time curve (AUC0-t, and AUC0-∞) in 34 subjects showed a linear kinetic relationship in the range of 10-60 mg/kg. The plasma half-life was approximately 25 days. The positive rates of serum ADAs and antibody titres were low with no evidence of an impact on safety or PK. In conclusion, MW33 was well-tolerated, demonstrated linear PK, with a lower positive rate of serum ADAs and antibody titres in healthy subjects.Trial registration: ClinicalTrials.gov identifier: NCT04427501.Trial registration: ClinicalTrials.gov identifier: NCT04533048.Trial registration: ClinicalTrials.gov identifier: NCT04627584.

Delivering an online course in emergency nursing education during the pandemic: What are the effects on students' learning?

BACKGROUND: Online learning emerged as an auxiliary approach in 2013 when MOOCs were imported and popularized in Chinese universities, particularly in the duration of pandemic outbreaks worldwide. World health organization (WHO) had recommended online education to keep social distance which still needs further evaluation. This study aimed to examine whether an open online course is superior to conventional education in emergency nursing during the COVID-19 pandemic. METHODS: Two groups of conventional education students (CG) and two groups of students participating in an online course that utilized an application (called SuperStar) as the SuperStar Group (SSG) were studied to compare their abilities in the process of new knowledge acquisition. The SSG was divided into a blended group (S1) and an online group (S2). The emergency nursing course was scheduled in 16 independent classes, which contained stochastic tests at least eight times. RESULTS: The CG group showed better performance on the final exam than the SSG group, but there was no statistically significant difference. The CG group obtained better scores on the memory capacity tests while the SSG had better scores on the application capacity tests. The SSG group scored higher on the later tests during the process of education compared to the CG group. CONCLUSIONS: Comprehension of an emergency nursing course was stronger in the SSG group than in the CG group. Horizontal comparison of subentry tests discriminated between the groups, with a better trend for the SSG group in application ability. There are potential effects on chronological learning through the use of the online course for emergency nursing education, not only during COVID-19 but also in the post-pandemic era.

Clinical, inflammatory, and immune differences between COVID-19 patients with and without cancer: A protocol for systematic review and meta-analysis.

INTRODUCTION: The World Health Organization announce that novel coronavirus (COVID-19) is pandemic worldwide on March 11, 2020. In this pandemic, cancer patients are prone to become critically ill after being infected with COVID-19 due to special immune conditions, and cannot effectively benefit from the treatment plan designed for normal people. However, only a few literatures report the differences between cancer patients and normal people after being infected with COVID-19. There is no systematic review to evaluate the clinical, inflammatory, and immune differences between COVID-19 patients with and without cancer. The systematic review aims to summarize and analyze the clinical, inflammatory, and immune differences between them. METHODS AND ANALYSIS: We plan to conduct a systematic review according to the Preferred Reporting Items for Systematic Review and Meta-analysis Protocols (PRISMA-P) guidelines. Several databases (PubMed/MEDLINE, Embase, Web of Science, The Cochrane Library, CNKI, CBM, VIP, WanFang) were searched for relevant eligible observational studies on COVID-19 patients with cancer published from December 2019 to September 2020. Two researchers (Y.ZY and W.PP) will independently complete search strategy formulation, literature selecting, Information extraction, data collation, and quality assessment. The primary outcome will be the clinical characteristics differences between COVID-19 patients with and without cancer. Secondary outcomes will include immune function regulation characteristics such as T cell subset status, inflammation and other factors for COVID-19 patients with cancer. We intend to perform a meta-analysis of studies calculating odds ratio differences (Hedge g) for comparison in Forest plots and subgroup analysis after assessment of heterogeneity using I statistics based on compatibility on the basis of population and outcomes. ETHICS AND DISSEMINATION: We will use the information from published researches with no need for ethical assessment. Our findings will be published in a peer-reviewed journal according to the PRISMA guidelines. PROSPERO REGISTRATION NUMBER: CRD42020204417.

Time series prediction for the epidemic trends of COVID-19 using the improved LSTM deep learning method: Case studies in Russia, Peru and Iran.

The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19.

Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics.

COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics ("origin", "host", "organization", "quarantine measures", "role models", "education", "economic", "rumor") and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) "Double peaks" feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a "double peaks" phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of "rumor" will enable people to have more communication and discussion. The interactive topics of "rumors" always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.

Prediction of epidemic trends in COVID-19 with logistic model and machine learning technics.

COVID-19 has now had a huge impact in the world, and more than 8 million people in more than 100 countries are infected. To contain its spread, a number of countries published control measures. However, it's not known when the epidemic will end in global and various countries. Predicting the trend of COVID-19 is an extremely important challenge. We integrate the most updated COVID-19 epidemiological data before June 16, 2020 into the Logistic model to fit the cap of epidemic trend, and then feed the cap value into FbProphet model, a machine learning based time series prediction model to derive the epidemic curve and predict the trend of the epidemic. Three significant points are summarized from our modeling results for global, Brazil, Russia, India, Peru and Indonesia. Under mathematical estimation, the global outbreak will peak in late October, with an estimated 14.12 million people infected cumulatively.