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1.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324235

ABSTRACT

Background: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. Methods: : This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID­19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. Results: : Patients with severe/critical symptoms were older (p<0.001) and more often male (p=0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (>11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 ( 95% confidence interval: 0.811-0.902). Conclusions: : Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (>11) was associated with severe illness.

2.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-328841

ABSTRACT

Background: The lack of the rehabilitation professionals is a global issue and it is becoming more serious during COVID-19. An Augmented Reality Rehabilitation System (AR Rehab) was developed for virtual training delivery. The virtual training was integrated into the participants’ usual care to reduce the human trainers’ effort so that the manpower scarcity can be eased. This also resulted in the reduction of the contact rate in pandemics. Objective: To investigate the feasibility of the AR Rehab-based virtual training when integrated into the usual care in a real-world pandemic setting. Methods: : Chronic stroke participants were randomly assigned to either a centre-based group (AR-Centre) or a home-based group (AR-Home) for a trial consisting of 20 sessions delivered in a human-machine integrated intervention. The trial of the AR-Centre was human training intensive with 3/4 of each session delivered by human trainers (PTs/OTs/Assistants) and 1/4 delivered by the virtual trainer (AR Rehab). The trial of the AR-Home was virtual training intensive with 1/4 and 3/4 of each session delivered by human and virtual trainers, respectively. Functional assessments including Fugl-Meyer Assessment for Upper Extremity (FMA-UE) and Lower Extremity (FMA-LE), Functional Ambulation Category (FAC), Berg Balance Scale (BBS), Barthel Index (BI) of Activities of Daily Living (ADL), and Physical Component Summary (SF-12v2 PCS) and Mental Component Summary (SF-12v2 MCS) of the 12-Item Short Form Health Survey (SF-12v2), were conducted before and after the intervention. User experience (UX) using questionnaires were collected after the intervention. Results: : There were 129 patients from 10 rehabilitation centres enrolled in the integrated program with 39 of them were selected for investigation. Significant functional improvement in FMA-UE (AR-Centre: p = 0.0022, AR-Home: p = 0.0043), FMA-LE (AR-Centre: p = 0.0007, AR-Home: p = 0.0052), SF-12v2 PCS (AR-Centre: p = 0.027, AR-Home: p = 0.036) were observed in both groups. Significant improvement in balance ability (BBS: p = 0.0438), and mental components (SF-12v2 MCS: p = 0.017) were found in AR-Centre group, while activities of daily living (BI: p = 0.0007) was found in AR-Home group. Contact rate was reduced by 30.75% − 72.30% within AR-All, 0.00% − 60.00% within AR-Centre, and 75.00% − 90.00% within AR-Home. Conclusion: The human-machine integrated mode was effective and efficient to reduce the human rehabilitation professionals’ effort while fulfilling the training goals. It eased the scarcity of manpower and reduced the contact rate during the pandemics.

3.
Mol Cell Proteomics ; 20: 100058, 2021.
Article in English | MEDLINE | ID: covidwho-1199368

ABSTRACT

The glycoprotein spike (S) on the surface of severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a determinant for viral invasion and host immune response. Herein, we characterized the site-specific N-glycosylation of S protein at the level of intact glycopeptides. All 22 potential N-glycosites were identified in the S-protein protomer and were found to be preserved among the 753 SARS-CoV-2 genome sequences. The glycosites exhibited glycoform heterogeneity as expected for a human cell-expressed protein subunit. We identified masses that correspond to 157 N-glycans, primarily of the complex type. In contrast, the insect cell-expressed S protein contained 38 N-glycans, completely of the high-mannose type. Our results revealed that the glycan types were highly determined by the differential processing of N-glycans among human and insect cells, regardless of the glycosites' location. Moreover, the N-glycan compositions were conserved among different sizes of subunits. Our study indicates that the S protein N-glycosylation occurs regularly at each site, albeit the occupied N-glycans were diverse and heterogenous. This N-glycosylation landscape and the differential N-glycan patterns among distinct host cells are expected to shed light on the infection mechanism and present a positive view for the development of vaccines and targeted drugs.


Subject(s)
Polysaccharides/metabolism , Recombinant Proteins/metabolism , Spike Glycoprotein, Coronavirus/metabolism , Animals , Glycosylation , Humans , Insecta/cytology , Polysaccharides/chemistry , Recombinant Proteins/genetics , Spike Glycoprotein, Coronavirus/genetics , Tandem Mass Spectrometry
4.
Vaccine ; 39(20): 2746-2754, 2021 05 12.
Article in English | MEDLINE | ID: covidwho-1174522

ABSTRACT

BACKGROUND: This study examined the safety and immunogenicity of an inactivated SARS-CoV-2 vaccine. METHOD: In a phase I randomized, double-blinded, placebo-controlled trial involving 192 healthy adults 18-59 years old, two injections of three doses (50 EU, 100 EU, 150 EU) of an inactivated SARS-CoV-2 vaccine or placebo were administered intramuscularly at a 2- or 4-week interval. The safety and immunogenicity of the vaccine were evaluated. RESULTS: Vaccination was completed in 191 subjects. Forty-four adverse reactions occurred within 28 days, most commonly mild pain and redness at the injection site or slight fatigue. At days 14 and 28, the seroconversion rates were 87.5% and 79.2% (50 EU), 100% and 95.8% (100 EU), and 95.8% and 87.5% (150 EU), respectively, with geometric mean titers (GMTs) of 18.1 and 10.6, 54.5 and 15.4, and 37.1 and 18.5, respectively, for the schedules with 2-week and 4-week intervals. Seroconversion was associated with synchronous upregulation of antibodies against the S protein, N protein and virion and a cytotoxic T lymphocyte (CTL) response. No cytokines and immune cells related to immunopathology were observed. Transcriptome analysis revealed the genetic diversity of immune responses induced by the vaccine. INTERPRETATION: In a population aged 18-59 years in this trial, this inactivated SARS-CoV-2 vaccine was safe and immunogenic. TRIAL REGISTRATION: CTR20200943 and NCT04412538.


Subject(s)
COVID-19 Vaccines , COVID-19 , Vaccines , Adolescent , Adult , Antibodies, Viral , China , Double-Blind Method , Humans , Immunogenicity, Vaccine , Middle Aged , SARS-CoV-2 , Young Adult
5.
Front Mol Biosci ; 7: 591873, 2020.
Article in English | MEDLINE | ID: covidwho-1000111

ABSTRACT

The ongoing outbreak of COVID-19 has been a serious threat to human health worldwide. The virus SARS-CoV-2 initiates its infection to the human body via the interaction of its spike (S) protein with the human Angiotensin-Converting Enzyme 2 (ACE2) of the host cells. Therefore, understanding the fundamental mechanisms of how SARS-CoV-2 S protein receptor binding domain (RBD) binds to ACE2 is highly demanded for developing treatments for COVID-19. Here we implemented multi-scale computational approaches to study the binding mechanisms of human ACE2 and S proteins of both SARS-CoV and SARS-CoV-2. Electrostatic features, including electrostatic potential, electric field lines, and electrostatic forces of SARS-CoV and SARS-CoV-2 were calculated and compared in detail. The results demonstrate that SARS-CoV and SARS-CoV-2 S proteins are both attractive to ACE2 by electrostatic forces even at different distances. However, the residues contributing to the electrostatic features are quite different due to the mutations between SARS-CoV S protein and SARS-CoV-2 S protein. Such differences are analyzed comprehensively. Compared to SARS-CoV, the SARS-CoV-2 binds with ACE2 using a more robust strategy: The electric field line related residues are distributed quite differently, which results in a more robust binding strategy of SARS-CoV-2. Also, SARS-CoV-2 has a higher electric field line density than that of SARS-CoV, which indicates stronger interaction between SARS-CoV-2 and ACE2, compared to that of SARS-CoV. Key residues involved in salt bridges and hydrogen bonds are identified in this study, which may help the future drug design against COVID-19.

6.
BMC Infect Dis ; 20(1): 953, 2020 Dec 11.
Article in English | MEDLINE | ID: covidwho-971572

ABSTRACT

BACKGROUND: The Coronavirus Disease 2019 (COVID-19) pandemic is a world-wide health crisis. Limited information is available regarding which patients will experience more severe disease symptoms. We evaluated hospitalized patients who were initially diagnosed with moderate COVID-19 for clinical parameters and radiological feature that showed an association with progression to severe/critical symptoms. METHODS: This study, a retrospective single-center study at the Central Hospital of Wuhan, enrolled 243 patients with confirmed COVID-19 pneumonia. Forty of these patients progressed from moderate to severe/critical symptoms during follow up. Demographic, clinical, laboratory, and radiological data were extracted from electronic medical records and compared between moderate- and severe/critical-type symptoms. Univariable and multivariable logistic regressions were used to identify the risk factors associated with symptom progression. RESULTS: Patients with severe/critical symptoms were older (p < 0.001) and more often male (p = 0.046). A combination of chronic obstructive pulmonary disease (COPD) and high maximum chest computed tomography (CT) score was associated with disease progression. Maximum CT score (> 11) had the greatest predictive value for disease progression. The area under the receiver operating characteristic curve was 0.861 (95% confidence interval: 0.811-0.902). CONCLUSIONS: Maximum CT score and COPD were associated with patient deterioration. Maximum CT score (> 11) was associated with severe illness.


Subject(s)
COVID-19/diagnostic imaging , Radiography, Thoracic/statistics & numerical data , Tomography, X-Ray Computed/statistics & numerical data , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19/epidemiology , China/epidemiology , Coronavirus Infections/epidemiology , Disease Progression , Female , Humans , Logistic Models , Male , Middle Aged , Pandemics , Pneumonia, Viral/epidemiology , ROC Curve , Radiography, Thoracic/methods , Retrospective Studies , Risk Factors , SARS-CoV-2 , Tomography, X-Ray Computed/methods , Young Adult
7.
Comput Sci Eng ; 22(6): 21-29, 2020.
Article in English | MEDLINE | ID: covidwho-833097

ABSTRACT

A large population in the world has been infected by COVID-19. Understanding the mechanisms of Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) is important for management and treatment of the COVID-19. When it comes to the infection process, one of the most important proteins in SARS-CoV-2 is the spike (S) protein, which is able to bind to human Angiotensin-Converting Enzyme 2 (ACE2) and initializes the entry of the host cell. In this study, we implemented multi-scale computational approaches to study the electrostatic features of the interfaces of the SARS-CoV-2 S protein Receptor Binding Domain (RBD) and ACE2. The simulations and analyses were performed on high-performance computing resources in Texas Advanced Computing Center (TACC). Our study identified key residues on the SARS-CoV-2, which can be used as targets for future drug design. The results shed lights on future drug design and therapeutic targets for COVID-19.

9.
Eur Respir J ; 56(2)2020 08.
Article in English | MEDLINE | ID: covidwho-744960

ABSTRACT

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.


Subject(s)
Coronavirus Infections/diagnosis , Hospital Mortality/trends , Machine Learning , Pneumonia, Viral/diagnosis , Triage/methods , Adult , Age Factors , Aged , Area Under Curve , Belgium , COVID-19 , COVID-19 Testing , China , Clinical Laboratory Techniques , Cohort Studies , Coronavirus Infections/epidemiology , Decision Support Systems, Clinical , Female , Hospitalization/statistics & numerical data , Humans , Internationality , Italy , Male , Middle Aged , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Predictive Value of Tests , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment , Severity of Illness Index , Sex Factors , Survival Analysis
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