Prokaryotic expression and purification of SARS-CoV-2 N protein

Bioinformatics methods were used to predict the hydrophilicity, hydrophobicity, antigen epitopes and analyse multiple sequence alignment of the nucleocapsid protein (N protein) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The recombinant plasmid pET28a/ N was constructed. In the prokaryotic expression system of Escherichia coli, the solubility and expression level of the protein were improved by adjusting the change of induction temperature and time, and the expressed recombinant N protein was purified and identified. The results showed that SARS-CoV-2 N encoded 419 amino acids, with an isoelectric point (PI) of 10.10, no transmembrane region, no signal peptide sequence, and strong local hydrophilicity. The full-length protein had a high antigenic index and was highly conserved, and its homology with SARS-CoV N protein was 90.5%. After fermentation with Escherichia coli prokaryotic expression system, the engineering strain BL21 (DE3)/pET28a/N was induced at 16 °C for 20 h with the final IPTG concentration of 0.2 mmol/L, and the protein was soluble and most pressed at this time, accounting for 70% of the total protein expression. The target protein purified by Ni-NTA affinity chromatography and gel filtration chromatography had a purity of 90% and a molecular weight of 55 kDa, which was specific. [ FROM AUTHOR] Copyright of Journal of Light Industry is the property of Journal of Zhengzhou University of Light Industry, Natural Science Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Perspectives from remote sensing to investigate the COVID-19 pandemic: A future-oriented approach.

As scientific technology and space science progress, remote sensing has emerged as an innovative solution to ease the challenges of the COVID-19 pandemic. To examine the research characteristics and growth trends in using remote sensing for monitoring and managing the COVID-19 research, a bibliometric analysis was conducted on the scientific documents appearing in the Scopus database. A total of 1,509 documents on this study topic were indexed between 2020 and 2022, covering 165 countries, 577 journals, 5239 institutions, and 8,616 authors. The studies related to remote sensing and COVID-19 have a significant increase of 30% with 464 articles. The United States (429 articles, 28.42% of the global output), China (295 articles, 19.54% of the global output), and the United Kingdom (174 articles, 11.53%) appeared as the top three most contributions to the literature related to remote sensing and COVID-19 research. Sustainability, Science of the Total Environment, and International Journal of Environmental Research and Public Health were the three most productive journals in this research field. The utmost predominant themes were COVID-19, remote sensing, spatial analysis, coronavirus, lockdown, and air pollution. The expansion of these topics appears to be associated with cross-sectional research on remote sensing, evidence-based tools, satellite mapping, and geographic information systems (GIS). Global pandemic risks will be monitored and managed much more effectively in the coming years with the use of remote sensing technology.

Use of Latent Dirichlet Allocation and Structural Equation Modeling in Determining the Factors for Continuance Intention of Knowledge Payment Platform

Knowledge payment is a new type of E-learning that has developed in the era of social media. With the influence of the COVID-19 epidemic, the knowledge payment market is developing rapidly. Exploring the influencing factors of users' continuance intention is beneficial for the sustainable development of knowledge payment platforms. Our study took 'Himalayan FM';as an example and included two studies: Study 1 used latent dirichlet allocation (LDA) to explore the main factors affecting the users' willingness to continue use, through mining user comment data on the knowledge payment platform;Study 2 constructed the conceptual model by integrating the technology acceptance model (TAM) and IS success model (IS) and carried out empirical analysis by SPSS and AMOS using the data that were collected through the questionnaire. The results show that: (1) perceived usefulness, user satisfaction, and spokesperson identity have a direct positive impact on users' willingness to continuous use, while perceived cost has a direct negative impact on users' willingness to continue use;(2) perceived ease of use, content quality, and system quality of knowledge payment platforms impacted user satisfaction directly, then affected users' willingness to continue use indirectly;(3) users' perceived enjoyment, membership experience, auditory experience, and other factors also directly impacted user satisfaction, affecting users' willingness to continue use indirectly. This study effectively expands the factors influencing knowledge payment users' willingness to continue use and provides a useful reference for the sustainable development of knowledge payment platforms.

Delayed Diagnosis and Treatment of Cancer Patients During the COVID-19 Pandemic in Henan, China: An Interrupted Time Series Analysis

Objective To investigate the possible impact of lockdown policies on the diagnosis and treatment of cancer patients in Henan, China. Design, Setting, and Participants We collected data from the Henan Cancer Hospital, affiliated with Zhengzhou University. The monthly numbers of inpatient admissions from January 2014 to December 2019 were used to forecast the number of inpatient admissions in 2020, which was then compared to the actual number of patients admitted during the pandemic to evaluate how the actual number diverges from this forecast. We conducted an interrupted time series analysis using the autoregressive integrated moving average (ARIMA) model. Main Outcomes and Measures For specific diagnoses, treatment modalities, and age groups, we compared the changes in monthly admissions after the pandemic with the forecasted changes from the model. Results The observed overall monthly number of inpatient admissions decreased by 20.2% [95% confidence interval (CI), 11.7–27.2%], 78.9% (95% CI, 77.3–80.4%), and 40.9% (95% CI, 35.6–45.5%) in January, February, and March 2020, respectively, as compared with those predicted using the ARIMA model. After the lockdown, visits for all treatment modalities decreased sharply. However, apparent compensation and recovery of the backlog appeared in later surgeries. As a result, the number of patients who underwent surgery in 2020 (30,478) was close to the number forecasted by the ARIMA model (30,185). In the same period, patients who received other treatments or underwent examinations were 106,074 and 36,968, respectively;the respective numbers that were forecasted by ARIMA were 127,775 and 60,025, respectively. These findings depict a decrease of 16.9 and 38.4% in patients who received other treatments or underwent examinations only, respectively. Regarding diagnosis, the reported incidence of various cancers decreased dramatically in February, with varying extent and speed of recovery. Conclusion and Relevance The COVID-19 pandemic has significantly delayed the diagnosis and treatment of cancer in Henan, China. Long-term research should be conducted to assess the future effects of lockdown policies.

Structural insights into the viral proteins binding by TRIM7 reveal a general C-terminal glutamine recognition mechanism (preprint)

The E3 ligase TRIM7 has emerged as a critical player in viral infection and pathogenesis. A recent study found that TRIM7 inhibits human enteroviruses through ubiquitination and proteasomal degradation of viral 2BC protein by targeting the 2C moiety of 2BC protein. Here, we report the crystal structures of TRIM7 in complex with 2C, where the C-terminal region of 2C is inserted into a positively charged groove of the TRIM7 PRY-SPRY domain. Structure-guided biochemical studies revealed the C-terminus glutamine residue of 2C as the primary determinant for TRIM7 binding. Such a glutamine-end motif binding mechanism can be successfully extended to other substrates of TRIM7. More importantly, leveraged by this finding, we were able to identify norovirus and SARS-CoV-2 proteins, and physiological proteins, as new TRIM7 substrates. We further show that TRIM7 may function as a restriction factor to promote the degradation of the viral proteins of norovirus and SARS-CoV-2, thereby restoring the Type I interferon immune response and inhibiting viral infection. Several crystal structures of TRIM7 in complex with SARS-CoV-2 proteins are also determined, and a conserved C-terminus glutamine-specific interaction is observed. These findings unveil a common recognition mode by TRIM7, providing the foundation for further mechanistic characterization of antiviral and cellular functions of TRIM7.

Design and Control of a Highly Redundant Rigid-Flexible Coupling Robot to Assist the COVID-19 Oropharyngeal-Swab Sampling (preprint)

The outbreak of novel coronavirus pneumonia (COVID-19) has caused mortality and morbidity worldwide. Oropharyngeal-swab (OP-swab) sampling is widely used for the diagnosis of COVID-19 in the world. To avoid the clinical staff from being affected by the virus, we developed a 9-degree-of-freedom (DOF) rigid-flexible coupling (RFC) robot to assist the COVID-19 OP-swab sampling. This robot is composed of a visual system, UR5 robot arm, micro-pneumatic actuator and force-sensing system. The robot is expected to reduce risk and free up the clinical staff from the long-term repetitive sampling work. Compared with a rigid sampling robot, the developed force-sensing RFC robot can facilitate OP-swab sampling procedures in a safer and softer way. In addition, a varying-parameter zeroing neural network-based optimization method is also proposed for motion planning of the 9-DOF redundant manipulator. The developed robot system is validated by OP-swab sampling on both oral cavity phantoms and volunteers.

Using simulation Technology to Analyze the COVID-19 Epidemic in Changsha, Hunan Province, China (preprint)

This study mainly uses simulation technology to simulate the COVID-19 epidemic in Changsha, Hunan Province, China, and analyze the impact of different prevention and control measures on the epidemic. we Collect the information of all COVID-19 patients in Changsha from January 21, 2020 to March 14, 2020 and relevant policies during the COVID-19 epidemic in Changsha. Established the SEIAR infectious disease dynamics model under natural conditions, and added isolation measures on this basis. Using Anylogic8.5, the COVID-19 epidemic in Changsha City was simulated under various conditions based on the established model.In this study we find that There were 242 COVID-19 patients in Changsha. including 121 males (50%) and 121 females (50%).Most cases occurred between February 6 and February 16. Through the calculation of the Rt during the epidemic in Changsha, it is found that it is reasonable to resume work on February 8, because the Rt value of Changsha dropped below 1 at this time.The simulation results show that reducing the contact rate of residents and reducing the success rate of virus transmission (wearing masks, disinfection, etc.) can effectively prevent the spread of COVID-19 and significantly reduce the number of peak patients.We believe that the disease is mainly spread by the respiratory tract. Therefore, the simulation results show that whether in the early or mid-stage of the epidemic, quarantining the names of residents or reducing the contact rate of residents is very effective in controlling the COVID-19 epidemic.

The psychological impact of COVID-19 outbreak on medical staff and the general public (preprint)

Purpose: To assess the psychological effects of the novel coronavirus disease (COVID-19) on medical staff and the general public.Methods: During the outbreak of COVID-19, an internet-based questionnaire included The Self-rating Depression Scale (SDS), Perceived Stress Scale (PSS-10), and Impact of Event Scale-Revised (IES-R) was used to assess the impact of the epidemic situation on the mental health of medical staff and general population in Wuhan and its surrounding areas.Results: The results suggest that the outbreak of COVID-19 has affected individuals significantly, the degree of which is related to age, sex, occupation and mental illness. There was a significant difference in PSS-10 and IES-R scores between the medical staff and the general population. The medical staff showed higher PSS-10 scores (16.813 ± 4.87) and IES-R scores (22.40 ± 12.12) compared to members of the general population PSS-10 (14.80 ± 5.60) and IES-R scores (17.89 ± 13.08). However, there was no statistically significant difference between the SDS scores of medical staff (44.52 ± 12.36) and the general public (43.08 ± 11.42). In terms of the need for psychological assistance, 50.97% of interviewees responded that they needed psychological counseling, of which medical staff accounted for 65.87% and non-medical staff accounted for 45.10%.Conclusion: During the ongoing COVID-19 outbreak, great attention should be paid to the mental health of the population, especially medical staff, and measures such as psychological intervention should be actively carried out for reducing the psychosocial effects.

Assessment of the prevention and control effects on the outbreak of COVID-19 in Hunan, China: Based on a SEIAR Dynamic Model (preprint)

Background A new infectious disease, Coronavirus disease 2019 (COVID-19) has been first reported during December 2019 in Wuhan, China, cases have been exported to other cities and abroad rapidly. Hunan is the neighboring province of Wuhan, a series of preventive and control measures were taken to control the outbreak of COVID-19. It is critical to assess these measures on the epidemic progression for the benefit of global expectation.Method: A Susceptible-exposed-infections/asymptomatic-removed (SEIAR) model was established to evaluate the effect of preventive measures. Berkeley Madonna 8.3.18 was employed for the model simulation and prediction, and the curve-fitting problem was solved by Runge-Kutta fourth-order method.Results In this study, we found that Rt was 2.71 from January 21 to 27 and reduced to 0.21 after January 27, 2020. If measures have not been fully launched, patients in Hunan would reach the maximum (8.96 million) on March 25, 2020, and end in about 208 days; when measures have been fully launched, patients in Hunan would just reach the maximum (699) on February 9, 2020, and end in about 56 days, which was very closed to the actual situation.Conclusion The outbreak of COVID-19 in Hunan, China has been well controlled under current measures, full implementation of measures could reduce the peak value, short the time to peak and duration of the outbreak effectively, which could provide a reference for controlling of COVID-19 for other countries.

Aveolar Macrophage Activation and Cytokine Storm in the Pathogenesis of Severe COVID-19 (preprint)

The coronavirus disease-19 (COVID-19) caused by SARS-CoV-2 infection can lead to a series of clinical settings from non-symptomatic viral carriers/spreaders to severe illness characterized by acute respiratory distress syndrome (ARDS)1,2. A sizable part of patients with COVID-19 have mild clinical symptoms at the early stage of infection, but the disease progression may become quite rapid in the later stage with ARDS as the common manifestation and followed by critical multiple organ failure, causing a high mortality rate of 7-10% in the elderly population with underlying chronic disease1-3. The pathological investigation in the lungs and other organs of fatal cases is fundamental for the mechanistic understanding of severe COVID-19 and the development of specific therapy in these cases. Gross anatomy and molecular markers allowed us to identify, in two fatal patients subject to necropsy, the main pathological features such as exudation and hemorrhage, epithelium injuries, infiltration of macrophages and fibrosis in the lungs. The mucous plug with fibrinous exudate in the alveoli and the activation of alveolar macrophages were characteristic abnormalities. These findings shed new insights into the pathogenesis of COVID-19 and justify the use of interleukin 6 (IL6) receptor antagonists and convalescent plasma with neutralizing antibodies against SARS-CoV-2 for severe patients.Authors Chaofu Wang, Jing Xie, Lei Zhao, Xiaochun Fei, Heng Zhang, and Yun Tan contributed equally to this work. Authors Chaofu Wang, Jun Cai, Rong Chen, Zhengli Shi, and Xiuwu Bian jointly supervised this work.

The added value of chest high-resolution CT findings of Corona Virus Disease 2019 in predicting severity of disease (preprint)

The aim of this study was to retrospectively analyze chest thin-section high-resolution CT (HRCT) findings for 32 patients with Corona Virus Disease 2019 (COVID-19) and clarify the correlation between CT data and laboratory results. 30 patients presented with abnormal initial CT scans. Of 30 patients, COVID-19 showed the involvement of bilateral lungs in 24 (80%), involvement of more than two lobes in 24 (80%), ground-glass opacities without consolidation in 27 (90%), ground-glass opacities with consolidation in 23 (76.7%), opacities with irregular intralobular lines in 26 (86.7%), opacities with round morphology in 25 (83.3%), and peripheral distribution in 30 (100%). Pleural effusion or mediastinal lymphadenopathy was relatively rare manifestations. Rapidly progression of the disease demonstrated by increasing number and range of ground glass opacities and appearance of consolidations at follow-up CT images in two patients. The CT lung severity score and No. of lobes involved were negatively correlated with lymphocyte count(r=-0.363, P=0.041; r=-0.367, P=0.039, respectively). Chest HRCT of COVID-19 predominantly manifests multiple, round, ground glass opacities with irregular intralobular lines, and peripheral distribution of bilateral lungs. HRCT is a potential tool for early screening, assessing progress, and predicting disease severity of COVID-19.Authors Jie Zhou and Jie Cao contributed equally to this work and are co-first authors.

Assessment and forecasting the spread of SARS-CoV-2 outbreak in Changsha, China: Based on a SEIAR Dynamic Model (preprint)

Background: A new human coronavirus named SARS-CoV-2 emerged during December 2019 in Wuhan, China. Cases have been exported to other Chinese cities and abroad, which may cause the global outbreak. Chang Sha is the nearest provincial capital city to Wuhan, the first case of COVID-19 in Changsha was diagnosed on January 21, 2020. Estimating the transmissibility and forecasting the trend of the outbreak of SARS-CoV-2 under the prevention and control measures in Changsha could inform evidence based decisions to policy makers.  Methods: Data were collected from the Health Commission of Changsha and Hunan Center for Disease Control and Prevention. A Susceptible-exposed-infections/ asymptomatic- removed (SEIAR) model was established to simulate the transmission of SARS-CoV-2 in Changsha. Berkeley Madonna 8.3.18 were employed for the model simulation and prediction, while the curve fitting problem was solved by the Runge-Kutta fourth-order method, with a tolerance of 0.001. Results: In this study, we found that Rt was 2.05 from January 21 to 27 and reduced to 0.2 after January 27, 2020 in Changsha. The prediction results showed that when no obvious prevention and control measures were applied, the total number of patients in Changsha would reach the maximum (2.27 million) on the 79th day after the outbreak, and end in about 240 days; When measures have not been fully launched, the total number of patients would reach the maximum (1.60 million) on the 28th day after the outbreak, and end in about 110 days; When measures have been fully launched, the total number of patients would reach the maximum (234) on the 23rd day after the outbreak, and end in about 60 days.  Conclusions: Outbreak of SARS-CoV-2 in Changsha is in a controllable stage under current prevention and control measures, it is predicted that the cumulative patients would reach the maximum of 234 on February 12, and the outbreak would be over on 20 March in Changsha. With the fully implementation of prevention and control measures, it could effectively reduce the peak value, short the time to peak and duration of the outbreak.

Estimating the case fatality ratio of the COVID-19 epidemic in China (preprint)

Background: Corona Virus Disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in Wuhan city and rapidly spread throughout China since late December 2019. Crude case fatality ratio (CFR) with dividing the number of known deaths by the number of confirmed cases does not represent the true CFR and might be off by orders of magnitude. We aim to provide a precise estimate of the CFR of COVID-19 using statistical models at the early stage of the epidemic. Methods: We extracted data from the daily released epidemic report published by the National Health Commission P. R. China from 20 Jan 2020, to 1 March 2020. Competing risk model was used to obtain the cumulative hazards for death, cure, and cure-death hazard ratio. Then the CFR was estimated based on the slope of the last piece in joinpoint regression model, which reflected the most recent trend of the epidemic. Results: As of 1 March 2020, totally 80,369 cases were diagnosed as COVID-19 in China. The CFR of COVID-19 were estimated to be 70.9% (95% CI: 66.8%-75.6%) during Jan 20-Feb 2, 20.2% (18.6%-22.1%) during Feb 3-14, 6.9% (6.4%-7.4%) during Feb 15-23, 1.5% (1.4%-1.6%) during Feb 24-March 1 in Hubei province, and 20.3% (17.0%-25.3%) during Jan 20-28, 1.9% (1.8%-2.1%) during Jan 29-Feb 12, 0.9% (0.8%-1.1%) during Feb 13-18, 0.4% (0.4%-0.5%) during Feb 19-March 1 in other areas of China, respectively. Conclusions: Based on analyses of public data, we found that the CFR in Hubei was much higher than that of other regions in China, over 3 times in all estimation. The CFR would follow a downwards trend based on our estimation from recently released data. Nevertheless, at early stage of outbreak, CFR estimates should be viewed cautiously because of limited data source on true onset and recovery time.