ABSTRACT
In order to solve the education problems caused by teachers and students' unavoidable absence in school during the COVID-19 pandemic, a series of online education activities were carried out by Nanjing University of Posts and Telecommunication in early March. To explore students and teachers' degree of satisfaction with distance education, this paper investigates multiple dimensions such as students' degree of satisfaction with teachers, the regional living standard, educational resources and negative factors that reduce the students' degree of satisfaction, etc. Furthermore, the attitude of teachers toward distance education may be partially reflected by the arrangement of live classes. All of the statistics are analyzed by comparing the distribution of votes. The results show that the degree of satisfaction by students and teachers with distance education is generally high but varies in areas with different living standards. In addition, we find that students are more sensitive to the lack of a learning atmosphere.
ABSTRACT
Objective To develop a dynamic model of susceptible(S), exposed people in the latent period(E), infective(I), quarantined(Q), confirmed(C), and recovered(R)(SEIQCR)to evaluate the role of interventions and control the coronavirus disease 2019(COVID-19)epidemic in Guangzhou. Methods Based on the SEIR propagation dynamics model, the modules of "quarantined" and "confirmed" cases were added to establish a new SEIQCR model. The epidemic data in Guangzhou from Jan. 13 to Mar. 17, 2020 were fitted to obtain the parameters of SEIQCR model. Results The number of predicted cases based on these parameters was highly consistent with the actual incidence(R2=0.93). Time-dependent reproduction number declined rapidly with the implementation of first level response to COVID-19, indicating that local transmission was effectively controlled. Conclusion The preventative and control measures were effective. Local government should continue strictly implementing the isolation system and cut off the transmission channels to curb the transmission of COVID-19. The SEIQCR model can provide methodological reference for intervention assessment in other regions.
ABSTRACT
Since December 2019, the COVID-19 caused by SARS-CoV-2 has been widely spread all over the world. It is reported that SARS-CoV-2 infection affects a series of human tissues, including lung, gastrointestinal tract, kidney, etc. ACE2 has been identified as the primary receptor of the SARS-CoV-2 Spike (S) protein. The relatively low expression level of this known receptor in the lungs, which is the predominantly infected organ in COVID-19, indicates that there may be some other co-receptors or alternative receptors of SARS-CoV-2 to work in coordination with ACE2. Here, we identified twenty-one candidate receptors of SARS-CoV-2, including ACE2-interactor proteins and SARS-CoV receptors. Then we investigated the protein expression levels of these twenty-one candidate receptors in different human tissues and found that five of which CAT, MME, L-SIGN, DC-SIGN, and AGTR2 were specifically expressed in SARS-CoV-2 affected tissues. Next, we performed molecular simulations of the above five candidate receptors with SARS-CoV-2 S protein, and found that the binding affinities of CAT, AGTR2, L-SIGN and DC-SIGN to S protein were even higher than ACE2. Interestingly, we also observed that CAT and AGTR2 bound to S protein in different regions with ACE2 conformationally, suggesting that these two proteins are likely capable of the co-receptors of ACE2. Conclusively, we considered that CAT, AGTR2, L-SIGN and DC-SIGN were the potential receptors of SARS-CoV-2. Moreover, AGTR2 and DC-SIGN tend to be highly expressed in the lungs of smokers, which is consistent with clinical phenomena of COVID-19, and further confirmed our conclusion. Besides, we also predicted the binding hot spots for these putative protein-protein interactions, which would help develop drugs against SARS-CoV-2.
Subject(s)
Severe Acute Respiratory Syndrome , COVID-19ABSTRACT
Background: An immediate unmet medical need exists to test and develop existing approved drugs against SARS-COV-2. Despite many efforts, very little progress has been made regarding finding low-cost oral medicines that can be made widely available worldwide to address the global pandemic. Methods: We sought to examine if a triple combination of nitazoxanide (using its active metabolite tizoxanide), ribavirin, and hydroxychloroquine would lead to a multiplicative effects on viral replication of SARS-COV-2 resulting in a significant reduction of virus yield using VERO E6 cells as a model of viral replication. Results: Virus yield measured in PFU/ml was ~ 2 logs lower with triple combination versus either drug alone, resulting in the prolongation of time to peak cytopathic effects (CPE). The time to produce 50% CPE increased from 2.8 days for viral controls versus 5.3 days for triple combination therapy. Finally, for each 1-log reduction in virus yield 24 hours post-infection, there was an additional 0.7-day delay in onset of CPE. Conclusions: A triple combination of tizoxanide, ribavirin, and hydroxychloroquine produced a reduction in SARS-COV-2 viral replication in Vero E6 cells, warranting exploration in additional cell lines as well as human clinical trials.
ABSTRACT
While transcriptome- and proteome-wide technologies to assess processes in protein biogenesis are now widely available, we still lack global approaches to assay post-ribosomal biogenesis events, in particular those occurring in the eukaryotic secretory system. We here developed a method, SECRiFY, to simultaneously assess the secretability of >105 protein fragments by two yeast species, S. cerevisiae and P. pastoris, using custom fragment libraries, surface display and a sequencing-based readout. Screening human proteome fragments with a median size of 50 - 100 amino acids, we generated datasets that enable datamining into protein features underlying secretability, revealing a striking role for intrinsic disorder and chain flexibility. SECRiFY is the first methodology that generates sufficient amounts of annotated data for advanced machine learning methods to deduce secretability predictors. The finding that secretability is indeed a learnable feature of protein sequences is of significant impact in the broad area of recombinant protein expression and de novo protein design.
Subject(s)
Sleep Disorders, IntrinsicABSTRACT
Since 2019, severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) causing coronavirus disease 2019 (COVID-19) has infected ten millions of people across the globe, and massive mutations in virus genome have occurred during the rapid spread of this novel coronavirus. Variance in protein sequence might lead to change in protein structure and interaction, then further affect the viral physiological characteristics, which could bring tremendous influence on the pandemic. In this study, we investigated 18 non-synonymous mutations in SARS-CoV-2 genome which incidence rates were all [≥]1% as of July 15th, 2020, then modeled the mutated protein structures and compared them with the reference ones. The results showed that four types of mutations could cause dramatic changes in protein structures (RMSD [≥]5.0 [A]), which were Q57H and G251V in open reading frames 3a (ORF3a), S194L and R203K/G204R in nucleocapsid (N). Next, we found that these mutations could affect the binding affinity of intraviral protein interactions. In addition, the hot spots within these docking complexes were altered, among which the mutation Q57H was involved in both Orf3a-Orf8 and Orf3a-S protein interactions. Besides, these mutations were widely distributed all over the world, and their occurrences fluctuated as time went on. Notably, the incidences of R203K/G204R in N and Q57H in Orf3a were both over 50% in some countries. Overall, our findings suggest that SARS-CoV-2 mutations can change viral protein structure, binding affinity and hot spots of the interface, thereby may have impacts on SARS-CoV-2 transmission, diagnosis and treatment of COVID-19.