Strategy and practice of online and offline effective blended teaching: taking Microbiology course as an example

Influenced by the epidemic of Covid-19 pneumonia, online and offline blended teaching has gradually become a new normal teaching mode. How to improve the teaching effect, ensure the substantive equivalence between online teaching and offline teaching, and realize effective teaching is the key. Adhering to the teaching philosophy of student-centered, result-oriented and continuous improvement, and aiming at the problems existing in the current Microbiology teaching, the course team used the effective teaching mode of O-AMAS to reconstruct the teaching process of Microbiology course based on the analysis and grasp of students' learning characteristics and cognitive style,construction of online teaching resources and optimization of teaching content. Our team adopted a diversified teaching mode, implemented effective evaluation and feedback, promoted deep learning through simple summary, and fully mobilized students' enthusiasm for autonomous learning, thus enhancing the teaching effect of the course.

Clinical value of admission blood glucose level on prognosis of COVID-19 patients

Objective: To explore the clinical value of admission blood glucose level on prognosis of COVID- 19 patients. Methods A total of 420 novel coronavirus pneumonia (COVID-19) patients admitted to Tongji Hospital of Tongji MedicalCollege from January 18, 2020 to February 26, 2020 were selected as the subjects of study. The data of diabetes or not, admissionblood glucose level(GLU), clinical severity grade were collected through the electronic medical record system, and the outcome, which defined as in-hospital motality, was also monitored. The patients were divided into diabetes group and non-diabetes groupin terms of the complication of diabetes, and then, firstly, stratified these two groups into survival subgroup and non-survivalsubgroup in according to the event of in-hospital motality, GLU between these two subgroups were compared. Secondly, according to the clinical severity grade, these two groups were stratified into moderate subgroup, severe subgroup and criticalsubgroup, and GLU among these subgroups were also compared. Thirdly, according to the admission blood glucose level, stratified these two groups into GLU 3.9~7.8 mmol /L subgroup, GLU 7.8~10.0 mmol/L subgroup and GLU>10.0 mmol/Lsubgroup, the in-hospital motality rates among these subgroups were compared. Finally, the multivariate logistic regression wasused to explore whether increased GLU were independent risk factor for in-hospital motality in diabetes group and non-diabetesgroup when adjusted for sex, age and underlying disease. Results In non-diabetes group, compared with Survival subgroup, GLUwas significantly elevated in non-Survival subgroup[6.96(5.95, 8.23)mmol/L vs 5.96 (5.32, 6.92) mmol/L], the difference wasstatistically significant(U=6047.0, P < 0.001), but in diabetes group, there was no significant difference between non-survivalsubgroup and Survival subgroup [12.42(8.41, 18.17) mmol/L vs 9.88 (7.79, 14.02) mmol/L], the difference was statisticallysignificant(U=1 200.5, P=0.059).In Non-diabetes group, GLU elevated remarkably along with the clinical severity gradeincreased, moderate subgroup, severe subgroup, critical subgroup GLU were 5.87(5.24, 6.69) mmol/L, 6.94(5.95, 7.90) mmol/L,9.73 (6.22, 11.64) mmol/L, the difference were statistically significant, respectively(U=723.0~4978.0, all P < 0.01). However indiabetes group, there was no significant difference on GLU when the clinical severity grade increased, moderate subgroup, severesubgroup, critical subgroup GLU were 9.88(7.81, 11.93)mmol/L, 12.42(8.43, 16.94)mmol/L, 11.43(7.89, 18.76)mmol/L, the difference were statistically significant, respectively (U=262.0~946.5, all P>0.05).In non-diabetes group, GLU> 10.0 mmol/L subgroup had the hightest in-hospital motality rate (72.0%) among all three subgroups, the differences were statisticallysignificant(X2=24.607, 9.625, all P < 0.01), when compared between GLU 3.9~7.8 mmol/L subgroup (in-hospital motality rate24.8%) and GLU 7.8~10.0 mmol/L subgroup (in-hospital motality rate 30.0%), there was no significant difference on in-hospitalmotality rate (X2=0.383, P > 0.05). However, in diabetes group, along with GLU increased, it had no significant difference on inhospitalmotality rate, GLU 3.9~7.8 mmol/L subgroup, GLU 7.8~10.0 mmol/L subgroup, GLU> 10.0 mmol/L subgroup, the inhospitalmotality rate were 34.8%, 41.4%, 49.2%, respectively(X2=0.236~1.380, all P> 0.05). Multivariate logistic regressionshowed, in non-diabets group, GLU>10.0 mmol/L was the independent risk factor when adjusted for sex, age and underlyingdisease, odds ratio was 7.969, and 95% confidence interval was 3.022~21.013, but in diabets group.It seemed that GLU>10.0 mmol/L was not the independent risk factor. Conclusion Admission blood glucose is a good predictor for disease severity andoutcome in non-diabetes patients with COVID-19. When admission hyperglycemia occurs, it tends to predict a poor prognosis.

Discovery of Potential Therapeutic Drugs for COVID-19 Through Logistic Matrix Factorization With Kernel Diffusion.

Coronavirus disease 2019 (COVID-19) is rapidly spreading. Researchers around the world are dedicated to finding the treatment clues for COVID-19. Drug repositioning, as a rapid and cost-effective way for finding therapeutic options from available FDA-approved drugs, has been applied to drug discovery for COVID-19. In this study, we develop a novel drug repositioning method (VDA-KLMF) to prioritize possible anti-SARS-CoV-2 drugs integrating virus sequences, drug chemical structures, known Virus-Drug Associations, and Logistic Matrix Factorization with Kernel diffusion. First, Gaussian kernels of viruses and drugs are built based on known VDAs and nearest neighbors. Second, sequence similarity kernel of viruses and chemical structure similarity kernel of drugs are constructed based on biological features and an identity matrix. Third, Gaussian kernel and similarity kernel are diffused. Forth, a logistic matrix factorization model with kernel diffusion is proposed to identify potential anti-SARS-CoV-2 drugs. Finally, molecular dockings between the inferred antiviral drugs and the junction of SARS-CoV-2 spike protein-ACE2 interface are implemented to investigate the binding abilities between them. VDA-KLMF is compared with two state-of-the-art VDA prediction models (VDA-KATZ and VDA-RWR) and three classical association prediction methods (NGRHMDA, LRLSHMDA, and NRLMF) based on 5-fold cross validations on viruses, drugs, and VDAs on three datasets. It obtains the best recalls, AUCs, and AUPRs, significantly outperforming other five methods under the three different cross validations. We observe that four chemical agents coming together on any two datasets, that is, remdesivir, ribavirin, nitazoxanide, and emetine, may be the clues of treatment for COVID-19. The docking results suggest that the key residues K353 and G496 may affect the binding energies and dynamics between the inferred anti-SARS-CoV-2 chemical agents and the junction of the spike protein-ACE2 interface. Integrating various biological data, Gaussian kernel, similarity kernel, and logistic matrix factorization with kernel diffusion, this work demonstrates that a few chemical agents may assist in drug discovery for COVID-19.

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares.

BACKGROUND: A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. METHODS: In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. RESULTS: In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. CONCLUSIONS: Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Screening Potential Drugs for COVID-19 Based on Bound Nuclear Norm Regularization.

The novel coronavirus pneumonia COVID-19 infected by SARS-CoV-2 has attracted worldwide attention. It is urgent to find effective therapeutic strategies for stopping COVID-19. In this study, a Bounded Nuclear Norm Regularization (BNNR) method is developed to predict anti-SARS-CoV-2 drug candidates. First, three virus-drug association datasets are compiled. Second, a heterogeneous virus-drug network is constructed. Third, complete genomic sequences and Gaussian association profiles are integrated to compute virus similarities; chemical structures and Gaussian association profiles are integrated to calculate drug similarities. Fourth, a BNNR model based on kernel similarity (VDA-GBNNR) is proposed to predict possible anti-SARS-CoV-2 drugs. VDA-GBNNR is compared with four existing advanced methods under fivefold cross-validation. The results show that VDA-GBNNR computes better AUCs of 0.8965, 0.8562, and 0.8803 on the three datasets, respectively. There are 6 anti-SARS-CoV-2 drugs overlapping in any two datasets, that is, remdesivir, favipiravir, ribavirin, mycophenolic acid, niclosamide, and mizoribine. Molecular dockings are conducted for the 6 small molecules and the junction of SARS-CoV-2 spike protein and human angiotensin-converting enzyme 2. In particular, niclosamide and mizoribine show higher binding energy of -8.06 and -7.06 kcal/mol with the junction, respectively. G496 and K353 may be potential key residues between anti-SARS-CoV-2 drugs and the interface junction. We hope that the predicted results can contribute to the treatment of COVID-19.

Prioritizing antiviral drugs against SARS-CoV-2 by integrating viral complete genome sequences and drug chemical structures.

The outbreak of a novel febrile respiratory disease called COVID-19, caused by a newfound coronavirus SARS-CoV-2, has brought a worldwide attention. Prioritizing approved drugs is critical for quick clinical trials against COVID-19. In this study, we first manually curated three Virus-Drug Association (VDA) datasets. By incorporating VDAs with the similarity between drugs and that between viruses, we constructed a heterogeneous Virus-Drug network. A novel Random Walk with Restart method (VDA-RWR) was then developed to identify possible VDAs related to SARS-CoV-2. We compared VDA-RWR with three state-of-the-art association prediction models based on fivefold cross-validations (CVs) on viruses, drugs and virus-drug associations on three datasets. VDA-RWR obtained the best AUCs for the three fivefold CVs, significantly outperforming other methods. We found two small molecules coming together on the three datasets, that is, remdesivir and ribavirin. These two chemical agents have higher molecular binding energies of - 7.0 kcal/mol and - 6.59 kcal/mol with the domain bound structure of the human receptor angiotensin converting enzyme 2 (ACE2) and the SARS-CoV-2 spike protein, respectively. Interestingly, for the first time, experimental results suggested that navitoclax could be potentially applied to stop SARS-CoV-2 and remains to further validation.

Analysis of the characteristics of hematological parameters in the early stages of COVID-19

The aim of the article was to analyze the characteristics of early peripheral blood laboratory examination results of patients with new coronavirus pneumonia (coronavirus disease 2019, COVID-19), and provide references for early clinical identification. From January 11, 2020 to February 18, 2020, all 626 patients who attended the fever clinic of Tongji Hospital affiliated to Tongji Medical College of Huazhong University of Science and Technology and tested positive for the new coronavirus (SARS-CoV-2) nucleic acid were selected as the research group In addition, 254 suspected patients who visited the fever clinic during the same period and the SARS-CoV-2 nucleic acid test was negative for two or more consecutive times were selected as the control group, and analyzed the blood cell test, biochemical routine, and inflammation markers of the two groups of patients at the fever clinic for the first time. The characteristics of 31 hematological indicators. Compared with the control group, the white blood cell (WBC), lymphocyte (LYMPH), platelet (PLT), serum calcium (serum calcium, Ca) of the study group were significantly reduced, and the hypersensitive C-reactive protein (hypersensitive C-reactive protein, hsCRP) significantly increased, the difference was statistically significant, and there was a difference in the distribution of results. In the study group, WBC was mostly normal or decreased. WBC was normal in 85.3%, decreased in 9.4%, LYMPH decreased in 43.1%, PLT decreased in 12.8%, Ca decreased in 61.8%, hsCRP was higher than 10mg/L accounted for 66.2%. The remaining 26 hematological indicators (Cl, Na, K, HCO3, Urea, UA, Cr, TBA, CHE, ALB, ALT, ALP, LDH, TP, PCT, DBIL, GLB, IBIL, TBIL, P-GGT, TCHOL, AST, Hb, RBC, NEUT, MON) There was no statistically significant difference between the two groups. WBC, LYMPH, PLT, Ca and hsCRP have significant changes in the early stage of COVID-19 patients. Joint detection and observation of the above indicators can provide important references for early clinical identification.

Identifying Effective Antiviral Drugs Against SARS-CoV-2 by Drug Repositioning Through Virus-Drug Association Prediction.

A new coronavirus called SARS-CoV-2 is rapidly spreading around the world. Over 16,558,289 infected cases with 656,093 deaths have been reported by July 29th, 2020, and it is urgent to identify effective antiviral treatment. In this study, potential antiviral drugs against SARS-CoV-2 were identified by drug repositioning through Virus-Drug Association (VDA) prediction. 96 VDAs between 11 types of viruses similar to SARS-CoV-2 and 78 small molecular drugs were extracted and a novel VDA identification model (VDA-RLSBN) was developed to find potential VDAs related to SARS-CoV-2. The model integrated the complete genome sequences of the viruses, the chemical structures of drugs, a regularized least squared classifier (RLS), a bipartite local model, and the neighbor association information. Compared with five state-of-the-art association prediction methods, VDA-RLSBN obtained the best AUC of 0.9085 and AUPR of 0.6630. Ribavirin was predicted to be the best small molecular drug, with a higher molecular binding energy of -6.39 kcal/mol with human angiotensin-converting enzyme 2 (ACE2), followed by remdesivir (-7.4 kcal/mol), mycophenolic acid (-5.35 kcal/mol), and chloroquine (-6.29 kcal/mol). Ribavirin, remdesivir, and chloroquine have been under clinical trials or supported by recent works. In addition, for the first time, our results suggested several antiviral drugs, such as FK506, with molecular binding energies of -11.06 and -10.1 kcal/mol with ACE2 and the spike protein, respectively, could be potentially used to prevent SARS-CoV-2 and remains to further validation. Drug repositioning through virus-drug association prediction can effectively find potential antiviral drugs against SARS-CoV-2.

FAST: a Feasible, Accurate and Speedy Test Strategy for COVID-19 (preprint)

As the COVID-19 pandemic continues worldwide, there is an urgent need to detect infected patients as quickly and accurately as possible. Group testing proposed by Technion [1][2] could improve efficiency greatly. However, the false negative rate (FNR) would be doubled. Using USA as an example, group testing would have over 70,000 false negatives, compared to 35,000 false negatives by individual testing. In this paper, we propose a Flexible, Accurate and Speedy Test (FAST), which is faster and more accurate than any existing tests. FAST first forms small close contact subgroups, e.g. families and friends. It then pools subgroups to form larger groups before RT-PCR test is done. FAST needs a similar number of tests to Technion's method, but sharply reduces the FNR to a negligible level. For example, FAST brings down the number of false negatives in USA to just 2000, and it is seven times faster than individual testing.