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1.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-324495

ABSTRACT

Background: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a global pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. Results: Of the 2,169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed with severe illness, and 75 patients died. Obvious differences in demographics, clinical characteristics and laboratory examinations were found between survivors and non-survivors. A decision tree classifier, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in train dataset and test dataset. The accuracy of this model was 0.98 and 0.98, respectively. Conclusion: The machine learning model was robust and effective in predicting the death outcome in severe COVID-19 patients.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-315249

ABSTRACT

Aim: Coronavirus disease 2019 (COVID-19) has caused an unprecedented healthcare crisis. We aim to develop and validate a nomogram for predicting disease progression based on a large cohort of hospitalized COVID-19 patients. Methods: This is a multicenter retrospective cohort study, with a total of 4,086 hospitalized COVID-19 patients enrolled from two hospitals in Wuhan, China between February 3 rd and Apr 10 th . Nomogram was developed based on a cohort of 3, 022 patients from one hospital, and externally validated in another cohort of 1,064 patients from the other hospital. The calibration was assessed by a calibration plot and the HL test to evaluate the goodness of fit, and the Area under the ROC Curve (AUROC) as a measure of discriminative ability. Results: Six independent predictors, including age, dyspnea, platelet count, lactate dehydrogenase, D-dimer and cardiovascular disease, were finally identified for construction of the nomogram for predicting disease progression of COVID-19 patients during hospitalization. The AUROC was 0.877 and 0.817 for development cohort and validation cohort, respectively. The calibration plots AND Hosmer-Lemeshow test showed optimal agreement between nomogram prediction and actual observation. The decision curve analysis showed the performance of the nomograms were better than all univariable models, and had greater net benefit. Next, a predictive nomogram for disease severity on admission was formulated and the six independent factors used were similar to that of the nomogram for disease progression, which indicates that those factors play important roles in determining disease severity and the risk of disease progression. Conclusion: In the current study, a nomogram was developed based on generally readily available variables at hospital admission to help predict disease progression of COVID-19.

3.
BMC Infect Dis ; 21(1): 783, 2021 Aug 09.
Article in English | MEDLINE | ID: covidwho-1350140

ABSTRACT

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) spreads rapidly among people and causes a pandemic. It is of great clinical significance to identify COVID-19 patients with high risk of death. METHODS: A total of 2169 adult COVID-19 patients were enrolled from Wuhan, China, from February 10th to April 15th, 2020. Difference analyses of medical records were performed between severe and non-severe groups, as well as between survivors and non-survivors. In addition, we developed a decision tree model to predict death outcome in severe patients. RESULTS: Of the 2169 COVID-19 patients, the median age was 61 years and male patients accounted for 48%. A total of 646 patients were diagnosed as severe illness, and 75 patients died. An older median age and a higher proportion of male patients were found in severe group or non-survivors compared to their counterparts. Significant differences in clinical characteristics and laboratory examinations were found between severe and non-severe groups, as well as between survivors and non-survivors. A decision tree, including three biomarkers, neutrophil-to-lymphocyte ratio, C-reactive protein and lactic dehydrogenase, was developed to predict death outcome in severe patients. This model performed well both in training and test datasets. The accuracy of this model were 0.98 in both datasets. CONCLUSION: We performed a comprehensive analysis of COVID-19 patients from the outbreak in Wuhan, China, and proposed a simple and clinically operable decision tree to help clinicians rapidly identify COVID-19 patients at high risk of death, to whom priority treatment and intensive care should be given.


Subject(s)
COVID-19 , Adult , China/epidemiology , Decision Trees , Humans , Infant, Newborn , Male , Retrospective Studies , Risk Factors , SARS-CoV-2
4.
Virol Sin ; 36(6): 1421-1430, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1296519

ABSTRACT

Coronaviruses (CoVs) are important human and animal pathogens that cause respiratory and gastrointestinal diseases. Porcine epidemic diarrhoea (PED), characterized by severe diarrhoea and vomiting in pigs, is a highly lethal disease caused by porcine epidemic diarrhoea virus (PEDV) and causes substantial losses in the swine industry worldwide. However, currently available commercial drugs have not shown great therapeutic effects. In this study, a fluorescence resonance energy transfer (FRET)-based assay was applied to screen a library containing 1,590 compounds and identified two compounds, 3-(aminocarbonyl)-1-phenylpyridinium and 2,3-dichloronaphthoquinone, that target the 3C-like protease (3CLpro) of PEDV. These compounds are of low molecular weight (MW) and greatly inhibited the activity of this enzyme (IC50 values were obtained in this study). Furthermore, these compounds exhibited antiviral capacity against another member of the CoV family, feline infectious peritonitis virus (FIPV). Here, the inhibitory effects of these compounds against CoVs on Vero cells and feline kidney cells were identified (with EC50 values) and cell viability assays were performed. The results of putative molecular docking models indicate that these compounds, labeled compound 1 and compound 2, contact the conserved active sites (Cys144, Glu165, Gln191) of 3CLpro via hydrogen bonds. These findings provide insight into the antiviral activities of compounds 1 and 2 that may facilitate future research on anti-CoV drugs.


Subject(s)
Antiviral Agents/pharmacology , Coronavirus 3C Proteases/antagonists & inhibitors , Coronavirus Infections , Coronavirus, Feline , Swine Diseases , Animals , Cats , Chlorocebus aethiops , Coronavirus Infections/drug therapy , Coronavirus Infections/veterinary , Coronavirus, Feline/drug effects , Molecular Docking Simulation , Swine , Swine Diseases/virology , Vero Cells
5.
Aging (Albany NY) ; 13(3): 3176-3189, 2021 02 09.
Article in English | MEDLINE | ID: covidwho-1076957

ABSTRACT

To establish an effective nomogram for predicting in-hospital mortality of COVID-19, a retrospective cohort study was conducted in two hospitals in Wuhan, China, with a total of 4,086 hospitalized COVID-19 cases. All patients have reached therapeutic endpoint (death or discharge). First, a total of 3,022 COVID-19 cases in Wuhan Huoshenshan hospital were divided chronologically into two sets, one (1,780 cases, including 47 died) for nomogram modeling and the other (1,242 cases, including 22 died) for internal validation. We then enrolled 1,064 COVID-19 cases (29 died) in Wuhan Taikang-Tongji hospital for external validation. Independent factors included age (HR for per year increment: 1.05), severity at admission (HR for per rank increment: 2.91), dyspnea (HR: 2.18), cardiovascular disease (HR: 3.25), and levels of lactate dehydrogenase (HR: 4.53), total bilirubin (HR: 2.56), blood glucose (HR: 2.56), and urea (HR: 2.14), which were finally selected into the nomogram. The C-index for the internal resampling (0.97, 95% CI: 0.95-0.98), the internal validation (0.96, 95% CI: 0.94-0.98), and the external validation (0.92, 95% CI: 0.86-0.98) demonstrated the fair discrimination ability. The calibration plots showed optimal agreement between nomogram prediction and actual observation. We established and validated a novel prognostic nomogram that could predict in-hospital mortality of COVID-19 patients.


Subject(s)
COVID-19 , Hospital Mortality , Nomograms , Age Factors , Aged , Blood Chemical Analysis/methods , Blood Chemical Analysis/statistics & numerical data , COVID-19/blood , COVID-19/diagnosis , COVID-19/mortality , COVID-19/physiopathology , Cardiovascular Diseases/epidemiology , China/epidemiology , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Survival Analysis , Symptom Assessment/methods , Symptom Assessment/statistics & numerical data
6.
Emerg Microbes Infect ; 10(1): 66-80, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-979439

ABSTRACT

Coronaviruses (CoVs) are potential pandemic pathogens that can infect a variety of hosts and cause respiratory, enteric, hepatic and neurological diseases. Nonstructural protein 3 (nsp3), an essential component of the replication/transcription complex, is one of the most important antiviral targets. Here, we report the first crystal structure of multiple functional domains from porcine delta-coronavirus (PDCoV) nsp3, including the macro domain (Macro), ubiquitin-like domain 2 (Ubl2) and papain-like protease (PLpro) catalytic domain. In the asymmetric unit, two of the subunits form the head-to-tail homodimer with an interaction interface between Macro and PLpro. However, PDCoV Macro-Ubl2-PLpro mainly exists as a monomer in solution. Then, we conducted fluorescent resonance energy transfer-based protease assays and found that PDCoV PLpro can cleave a peptide by mimicking the cognate nsp2/nsp3 cleavage site in peptide substrates and exhibits deubiquitinating and de-interferon stimulated gene(deISGylating) activities by hydrolysing ubiquitin-7-amino-4-methylcoumarin (Ub-AMC) and ISG15-AMC substrates. Moreover, the deletion of Macro or Macro-Ubl2 decreased the enzyme activity of PLpro, indicating that Macro and Ubl2 play important roles in maintaining the stability of the PLpro domain. Two active sites of PLpro, Cys260 and His398, were determined; unexpectedly, the conserved site Asp412 was not the third active site. Furthermore, the motif "NGYDT" (amino acids 409-413) was important for stabilizing the enzyme activity of PLpro, and the N409A mutant significantly decreased the enzyme activity of PLpro. These results provide novel insights into the replication mechanism of CoV and new clues for future drug design.


Subject(s)
Coronavirus Papain-Like Proteases/chemistry , Catalytic Domain , Coronavirus Papain-Like Proteases/physiology , Crystallization , HeLa Cells , Humans , Protein Domains , Protein Multimerization , Ubiquitination
7.
J Gen Virol ; 102(1)2021 01.
Article in English | MEDLINE | ID: covidwho-910383

ABSTRACT

The emerging pathogen severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused social and economic disruption worldwide, infecting over 9.0 million people and killing over 469 000 by 24 June 2020. Unfortunately, no vaccine or antiviral drug that completely eliminates the transmissible disease coronavirus disease 2019 (COVID-19) has been developed to date. Given that coronavirus nonstructural protein 1 (nsp1) is a good target for attenuated vaccines, it is of great significance to explore the detailed characteristics of SARS-CoV-2 nsp1. Here, we first confirmed that SARS-CoV-2 nsp1 had a conserved function similar to that of SARS-CoV nsp1 in inhibiting host-protein synthesis and showed greater inhibition efficiency, as revealed by ribopuromycylation and Renilla luciferase (Rluc) reporter assays. Specifically, bioinformatics and biochemical experiments showed that by interacting with 40S ribosomal subunit, the lysine located at amino acid 164 (K164) was the key residue that enabled SARS-CoV-2 nsp1 to suppress host gene expression. Furthermore, as an inhibitor of host-protein expression, SARS-CoV-2 nsp1 contributed to cell-cycle arrest in G0/G1 phase, which might provide a favourable environment for virus production. Taken together, this research uncovered the detailed mechanism by which SARS-CoV-2 nsp1 K164 inhibited host gene expression, laying the foundation for the development of attenuated vaccines based on nsp1 modification.


Subject(s)
Host-Pathogen Interactions/genetics , Lysine/genetics , Ribosomal Proteins/genetics , Ribosome Subunits, Small, Eukaryotic/genetics , SARS-CoV-2/genetics , Viral Nonstructural Proteins/genetics , Amino Acid Sequence , Amino Acid Substitution , Computational Biology/methods , G1 Phase Cell Cycle Checkpoints/genetics , Gene Expression Regulation , Genes, Reporter , HEK293 Cells , Humans , Luciferases/genetics , Luciferases/metabolism , Lysine/metabolism , Mutation , Ribosomal Proteins/antagonists & inhibitors , Ribosomal Proteins/metabolism , Ribosome Subunits, Small, Eukaryotic/metabolism , Ribosome Subunits, Small, Eukaryotic/virology , SARS Virus/genetics , SARS Virus/metabolism , SARS-CoV-2/metabolism , Sequence Alignment , Sequence Homology, Amino Acid , Signal Transduction , Viral Nonstructural Proteins/metabolism
8.
J Virol ; 94(15)2020 07 16.
Article in English | MEDLINE | ID: covidwho-831394

ABSTRACT

Currently, an effective therapeutic treatment for porcine reproductive and respiratory syndrome virus (PRRSV) remains elusive. PRRSV helicase nsp10 is an important component of the replication transcription complex that plays a crucial role in viral replication, making nsp10 an important target for drug development. Here, we report the first crystal structure of full-length nsp10 from the arterivirus PRRSV, which has multiple domains: an N-terminal zinc-binding domain (ZBD), a 1B domain, and helicase core domains 1A and 2A. Importantly, our structural analyses indicate that the conformation of the 1B domain from arterivirus nsp10 undergoes a dynamic transition. The polynucleotide substrate channel formed by domains 1A and 1B adopts an open state, which may create enough space to accommodate and bind double-stranded RNA (dsRNA) during unwinding. Moreover, we report a unique C-terminal domain structure that participates in stabilizing the overall helicase structure. Our biochemical experiments also showed that deletion of the 1B domain and C-terminal domain significantly reduced the helicase activity of nsp10, indicating that the four domains must cooperate to contribute to helicase function. In addition, our results indicate that nidoviruses contain a conserved helicase core domain and key amino acid sites affecting helicase function, which share a common mechanism of helicase translocation and unwinding activity. These findings will help to further our understanding of the mechanism of helicase function and provide new targets for the development of antiviral drugs.IMPORTANCE Porcine reproductive and respiratory syndrome virus (PRRSV) is a major respiratory disease agent in pigs that causes enormous economic losses to the global swine industry. PRRSV helicase nsp10 is a multifunctional protein with translocation and unwinding activities and plays a vital role in viral RNA synthesis. Here, we report the first structure of full-length nsp10 from the arterivirus PRRSV at 3.0-Å resolution. Our results show that the 1B domain of PRRSV nsp10 adopts a novel open state and has a unique C-terminal domain structure, which plays a crucial role in nsp10 helicase activity. Furthermore, mutagenesis and structural analysis revealed conservation of the helicase catalytic domain across the order Nidovirales (families Arteriviridae and Coronaviridae). Importantly, our results will provide a structural basis for further understanding the function of helicases in the order Nidovirales.


Subject(s)
Porcine respiratory and reproductive syndrome virus/enzymology , RNA Helicases/chemistry , RNA, Double-Stranded/chemistry , RNA, Viral/chemistry , Viral Proteins/chemistry , Porcine respiratory and reproductive syndrome virus/genetics , Protein Domains , RNA Helicases/genetics , RNA, Double-Stranded/genetics , RNA, Viral/genetics , Viral Proteins/genetics
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