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1.
Clin Infect Dis ; 2020 Aug 03.
Article in English | MEDLINE | ID: covidwho-693303

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a global pandemic with no licensed vaccine or specific antiviral agents for therapy. Little is known about the longitudinal dynamics of SARS-CoV-2-specific neutralizing antibodies (NAbs) in COVID-19 patients. METHODS: Blood samples (n=173) were collected from 30 COVID-19 patients over a 3-month period after symptom onset and analyzed for SARS-CoV-2-specific NAbs, using the lentiviral pseudotype assay, coincident with the levels of IgG and proinflammatory cytokines. RESULTS: SARS-CoV-2-specific NAb titers were low for the first 7-10 d after symptom onset and increased after 2-3 weeks. The median peak time for NAbs was 33 d (IQR 24-59 d) after symptom onset. NAb titers in 93·3% (28/30) of the patients declined gradually over the 3-month study period, with a median decrease of 34·8% (IQR 19·6-42·4%). NAb titers increased over time in parallel with the rise in IgG antibody levels, correlating well at week 3 (r = 0·41, p & 0·05). The NAb titers also demonstrated a significant positive correlation with levels of plasma proinflammatory cytokines, including SCF, TRAIL, and M-CSF. CONCLUSIONS: These data provide useful information regarding dynamic changes in NAbs in COVID-19 patients during the acute and convalescent phases.

2.
Chin J Integr Med ; 2020 Aug 01.
Article in English | MEDLINE | ID: covidwho-691259

ABSTRACT

OBJECTIVE: To select potential molecules that can target viral spike proteins, which may potentially interrupt the interaction between the human angiotension-converting enzyme 2 (ACE2) receptor and viral spike protein by virtual screening. METHODS: The three-dimensional (3D)-coordinate file of the receptor-binding domain (RBD)-ACE2 complex for searching a suitable docking pocket was firstly downloaded and prepared. Secondly, approximately 15,000 molecular candidates were prepared, including US Food and Drug Administration (FDA)-approved drugs from DrugBank and natural compounds from Traditional Chinese Medicine Systems Pharmacology (TCMSP), for the docking process. Then, virtual screening was performed and the binding energy in Autodock Vina was calculated. Finally, the top 20 molecules with high binding energy and their Chinese medicine (CM) herb sources were listed in this paper. RESULTS: It was found that digitoxin, a cardiac glycoside in DrugBank and bisindigotin in TCMSP had the highest docking scores. Interestingly, two of the CM herbs containing the natural compounds that had relatively high binding scores, Forsythiae fructus and Isatidis radix, are components of Lianhua Qingwen (), a CM formula reportedly exerting activity against severe acute respiratory syndrome (SARS)-Cov-2. Moreover, raltegravir, an HIV integrase inhibitor, was found to have a relatively high binding score. CONCLUSIONS: A class of compounds, which are from FDA-approved drugs and CM natural compounds, that had high binding energy with RBD of the viral spike protein. Our work provides potential candidates for other researchers to identify inhibitors to prevent SARS-CoV-2 infection, and highlights the importance of CM and integrative application of CM and Western medicine on treating COVID-19.

3.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-657750

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
4.
J Infect Dis ; 222(2): 189-193, 2020 06 29.
Article in English | MEDLINE | ID: covidwho-643587

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel ß-coronavirus, causes severe pneumonia and has spread throughout the globe rapidly. The disease associated with SARS-CoV-2 infection is named coronavirus disease 2019 (COVID-19). To date, real-time reverse-transcription polymerase chain reaction (RT-PCR) is the only test able to confirm this infection. However, the accuracy of RT-PCR depends on several factors; variations in these factors might significantly lower the sensitivity of detection. METHODS: In this study, we developed a peptide-based luminescent immunoassay that detected immunoglobulin (Ig)G and IgM. The assay cutoff value was determined by evaluating the sera from healthy and infected patients for pathogens other than SARS-CoV-2. RESULTS: To evaluate assay performance, we detected IgG and IgM in the sera from confirmed patients. The positive rate of IgG and IgM was 71.4% and 57.2%, respectively. CONCLUSIONS: Therefore, combining our immunoassay with real-time RT-PCR might enhance the diagnostic accuracy of COVID-19.


Subject(s)
Antibodies, Viral/blood , Betacoronavirus/immunology , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Immunoenzyme Techniques/methods , Pneumonia, Viral/diagnosis , Serologic Tests/methods , Adult , Coronavirus Infections/immunology , Female , Humans , Immunoglobulin G/blood , Immunoglobulin M/blood , Luminescent Measurements , Male , Middle Aged , Pandemics , Peptides/immunology , Pneumonia, Viral/immunology , Real-Time Polymerase Chain Reaction , Reverse Transcriptase Polymerase Chain Reaction , Sensitivity and Specificity , Viral Proteins/immunology
5.
Environ. Hazards ; 2020.
Article in English | ELSEVIER | ID: covidwho-245137

ABSTRACT

The outbreak of the coronavirus disease (Covid-19) in January 2020 in Wuhan has had a significant impact on the Chinese economy, and especially on the small and medium-sized enterprises (SMEs). In February 2020 an online questionnaire and follow-up interviews were conducted on 4807 SMEs in Sichuan to assess the challenges associated with work resumption and the associated policy requirements. It was found that most SMEs were unable to resume work because of a shortage of epidemic mitigation materials, the inability of employees to return to work, disrupted supply chains, and reduced market demand. Many SMEs were also facing cash flow risks as they had to continue to pay for various fixed expenditures even though they had little or no revenue. As these delays in work resumption have put unprecedented pressures on the survival of many SMEs, recommendations relevant to China and other affected countries regarding cash flow relief, work resumption and consumption stimulation are given to assist SME survival and economic recovery from the disaster situation.

6.
J Infect Dis ; 222(2): 189-193, 2020 06 29.
Article in English | MEDLINE | ID: covidwho-209820

ABSTRACT

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel ß-coronavirus, causes severe pneumonia and has spread throughout the globe rapidly. The disease associated with SARS-CoV-2 infection is named coronavirus disease 2019 (COVID-19). To date, real-time reverse-transcription polymerase chain reaction (RT-PCR) is the only test able to confirm this infection. However, the accuracy of RT-PCR depends on several factors; variations in these factors might significantly lower the sensitivity of detection. METHODS: In this study, we developed a peptide-based luminescent immunoassay that detected immunoglobulin (Ig)G and IgM. The assay cutoff value was determined by evaluating the sera from healthy and infected patients for pathogens other than SARS-CoV-2. RESULTS: To evaluate assay performance, we detected IgG and IgM in the sera from confirmed patients. The positive rate of IgG and IgM was 71.4% and 57.2%, respectively. CONCLUSIONS: Therefore, combining our immunoassay with real-time RT-PCR might enhance the diagnostic accuracy of COVID-19.


Subject(s)
Antibodies, Viral/blood , Betacoronavirus/immunology , Clinical Laboratory Techniques/methods , Coronavirus Infections/diagnosis , Immunoenzyme Techniques/methods , Pneumonia, Viral/diagnosis , Serologic Tests/methods , Adult , Coronavirus Infections/immunology , Female , Humans , Immunoglobulin G/blood , Immunoglobulin M/blood , Luminescent Measurements , Male , Middle Aged , Pandemics , Peptides/immunology , Pneumonia, Viral/immunology , Real-Time Polymerase Chain Reaction , Reverse Transcriptase Polymerase Chain Reaction , Sensitivity and Specificity , Viral Proteins/immunology
8.
Radiology ; 296(2): E65-E71, 2020 08.
Article in English | MEDLINE | ID: covidwho-10509

ABSTRACT

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic and accurate detection of COVID-19 using chest CT. Purpose To develop a fully automatic framework to detect COVID-19 using chest CT and evaluate its performance. Materials and Methods In this retrospective and multicenter study, a deep learning model, the COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT scans for the detection of COVID-19. CT scans of community-acquired pneumonia (CAP) and other non-pneumonia abnormalities were included to test the robustness of the model. The datasets were collected from six hospitals between August 2016 and February 2020. Diagnostic performance was assessed with the area under the receiver operating characteristic curve, sensitivity, and specificity. Results The collected dataset consisted of 4352 chest CT scans from 3322 patients. The average patient age (±standard deviation) was 49 years ± 15, and there were slightly more men than women (1838 vs 1484, respectively; P = .29). The per-scan sensitivity and specificity for detecting COVID-19 in the independent test set was 90% (95% confidence interval [CI]: 83%, 94%; 114 of 127 scans) and 96% (95% CI: 93%, 98%; 294 of 307 scans), respectively, with an area under the receiver operating characteristic curve of 0.96 (P < .001). The per-scan sensitivity and specificity for detecting CAP in the independent test set was 87% (152 of 175 scans) and 92% (239 of 259 scans), respectively, with an area under the receiver operating characteristic curve of 0.95 (95% CI: 0.93, 0.97). Conclusion A deep learning model can accurately detect coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions. © RSNA, 2020 Online supplemental material is available for this article.


Subject(s)
Artificial Intelligence , Betacoronavirus , Coronavirus Infections/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Adult , Aged , Clinical Laboratory Techniques/methods , Community-Acquired Infections/diagnostic imaging , Coronavirus Infections/diagnosis , Deep Learning , Diagnosis, Differential , Female , Humans , Imaging, Three-Dimensional/methods , Male , Middle Aged , Pandemics , ROC Curve , Radiographic Image Interpretation, Computer-Assisted/methods , Retrospective Studies , Sensitivity and Specificity , Tomography, X-Ray Computed/methods
9.
J Med Virol ; 2020 Mar 17.
Article in English | MEDLINE | ID: covidwho-9079

ABSTRACT

During an outbreak of respiratory diseases including atypical pneumonia in Wuhan, a previously unknown ß-coronavirus was detected in patients. The newly discovered coronavirus is similar to some ß-coronaviruses found in bats but different from previously known SARS-CoV and MERS-CoV. High sequence identities and similarities between 2019-nCoV and SARS-CoV were found. In this study, we searched the homologous templates of all nonstructural and structural proteins of 2019-nCoV. Among the nonstructural proteins, the leader protein (nsp1), the papain-like protease (nsp3), the nsp4, the 3C-like protease (nsp5), the nsp7, the nsp8, the nsp9, the nsp10, the RNA-directed RNA polymerase (nsp12), the helicase (nsp13), the guanine-N7 methyltransferase (nsp14), the uridylate-specific endoribonuclease (nsp15), the 2'-O-methyltransferase (nsp16), and the ORF7a protein could be built on the basis of homology templates. Among the structural proteins, the spike protein (S-protein), the envelope protein (E-protein), and the nucleocapsid protein (N-protein) can be constructed based on the crystal structures of the proteins from SARS-CoV. It is known that PL-Pro, 3CL-Pro, and RdRp are important targets for design antiviral drugs against 2019-nCoV. And S protein is a critical target candidate for inhibitor screening or vaccine design against 2019-nCoV because coronavirus replication is initiated by the binding of S protein to cell surface receptors. It is believed that these proteins should be useful for further structure-based virtual screening and related computer-aided drug development and vaccine design.

10.
Chin Med J (Engl) ; 133(9): 1044-1050, 2020 May 05.
Article in English | MEDLINE | ID: covidwho-3436

ABSTRACT

BACKGROUND: The ongoing new coronavirus pneumonia (Corona Virus Disease 2019, COVID-19) outbreak is spreading in China, but it has not yet reached its peak. Five million people emigrated from Wuhan before lockdown, potentially representing a source of virus infection. Determining case distribution and its correlation with population emigration from Wuhan in the early stage of the epidemic is of great importance for early warning and for the prevention of future outbreaks. METHODS: The official case report on the COVID-19 epidemic was collected as of January 30, 2020. Time and location information on COVID-19 cases was extracted and analyzed using ArcGIS and WinBUGS software. Data on population migration from Wuhan city and Hubei province were extracted from Baidu Qianxi, and their correlation with the number of cases was analyzed. RESULTS: The COVID-19 confirmed and death cases in Hubei province accounted for 59.91% (5806/9692) and 95.77% (204/213) of the total cases in China, respectively. Hot spot provinces included Sichuan and Yunnan, which are adjacent to Hubei. The time risk of Hubei province on the following day was 1.960 times that on the previous day. The number of cases in some cities was relatively low, but the time risk appeared to be continuously rising. The correlation coefficient between the provincial number of cases and emigration from Wuhan was up to 0.943. The lockdown of 17 cities in Hubei province and the implementation of nationwide control measures efficiently prevented an exponential growth in the number of cases. CONCLUSIONS: The population that emigrated from Wuhan was the main infection source in other cities and provinces. Some cities with a low number of cases showed a rapid increase in case load. Owing to the upcoming Spring Festival return wave, understanding the risk trends in different regions is crucial to ensure preparedness at both the individual and organization levels and to prevent new outbreaks.


Subject(s)
Betacoronavirus , Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , China/epidemiology , Emigration and Immigration , Epidemics , Humans , Pandemics
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