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1.
Sci Rep ; 10(1): 12567, 2020 07 24.
Article in English | MEDLINE | ID: covidwho-672142

ABSTRACT

A question central to the Covid-19 pandemic is why the Covid-19 mortality rate varies so greatly across countries. This study aims to investigate factors associated with cross-country variation in Covid-19 mortality. Covid-19 mortality rate was calculated as number of deaths per 100 Covid-19 cases. To identify factors associated with Covid-19 mortality rate, linear regressions were applied to a cross-sectional dataset comprising 169 countries. We retrieved data from the Worldometer website, the Worldwide Governance Indicators, World Development Indicators, and Logistics Performance Indicators databases. Covid-19 mortality rate was negatively associated with Covid-19 test number per 100 people (RR = 0.92, P = 0.001), government effectiveness score (RR = 0.96, P = 0.017), and number of hospital beds (RR = 0.85, P < 0.001). Covid-19 mortality rate was positively associated with proportion of population aged 65 or older (RR = 1.12, P < 0.001) and transport infrastructure quality score (RR = 1.08, P = 0.002). Furthermore, the negative association between Covid-19 mortality and test number was stronger among low-income countries and countries with lower government effectiveness scores, younger populations and fewer hospital beds. Predicted mortality rates were highly associated with observed mortality rates (r = 0.77; P < 0.001). Increasing Covid-19 testing, improving government effectiveness and increasing hospital beds may have the potential to attenuate Covid-19 mortality.

2.
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
3.
J Med Internet Res ; 22(8): e19551, 2020 Aug 04.
Article in English | MEDLINE | ID: covidwho-656151

ABSTRACT

BACKGROUND: Coronavirus disease (COVID-19) has become a global threat to human health. Internet hospitals have emerged as a critical technology to bring epidemic-related web-based services and medical support to the public. However, only a few very recent scientific literature reports have explored the effects of internet hospitals on psychological burden and disease knowledge in major public health emergencies such as the COVID-19 pandemic. OBJECTIVE: The aim of this study was to explore the role of internet hospitals in relieving psychological burden and increasing disease knowledge during the early outbreak of the COVID-19 pandemic. METHODS: This survey was conducted from January 26 to February 1, 2020, during the early outbreak of COVID-19 in China. The platform used for the consultation was the WeChat public account of our hospital. To participate in the study, the patient was required to answer a list of questions to exclude the possibility of COVID-19 infection and confirm their willingness to participate voluntarily. Next, the participant was directed to complete the self-report questionnaire. After the internet consultation, the participant was directed to complete the self-report questionnaire again. The questionnaire included sections on general information, the General Health Questionnaire-28 (GHQ-28), and the participant's worries, disease knowledge, and need for hospital treatment. RESULTS: The total number of internet consultations was 4120. The consultation topics mainly included respiratory symptoms such as cough, expectoration, and fever (2489/4120, 60.4%) and disease knowledge, anxiety, and fear (1023/4120, 24.8%). A total of 1530 people filled out the questionnaires before and after the internet consultation. Of these people, 1398/1530 (91.4%) experienced psychological stress before the internet consultation, which significantly decreased after consultation (260/1530, 17.0%) (χ21=1704.8, P<.001). There was no significant difference in the number of people who expressed concern about the COVID-19 pandemic before and after the internet consultation (χ21=0.7, P=.43). However, the degree of concern after the internet consultation was significantly alleviated (t2699=90.638, P<.001). The main worries before and after consultation were the dangers posed by the disease and the risk of infection of family members. The scores of the self-assessment risk after the internet consultation were significantly lower than those before consultation (t3058=95.694, P<.001). After the consultation, the participants' knowledge of the symptoms, transmission routes, and preventive measures of COVID-19 was significantly higher than before the consultation (t3058=-106.105, -80.456, and -152.605, respectively; all P<.001). The hospital treatment need score after the internet consultation decreased from 3.3 (SD 1.2) to 1.6 (SD 0.8), and the difference was statistically significant (t3058=45.765, P<.001). CONCLUSIONS: During the early outbreak of COVID-19, internet hospitals could help relieve psychological burdens and increase disease awareness through timely and rapid spread of knowledge regarding COVID-19 prevention and control. Internet hospitals should be an important aspect of a new medical model in public health emergency systems.

4.
Sci Rep ; 10(1): 11336, 2020 07 09.
Article in English | MEDLINE | ID: covidwho-638242

ABSTRACT

This study aimed to compare the chest computed tomography (CT) findings between survivors and non-survivors with Coronavirus Disease 2019 (COVID-19). Between 12 January 2020 and 20 February 2020, the records of 124 consecutive patients diagnosed with COVID-19 were retrospectively reviewed and divided into survivor (83/124) and non-survivor (41/124) groups. Chest CT findings were qualitatively compared on admission and serial chest CT scans were semi-quantitively evaluated between two groups using curve estimations. On admission, significantly more bilateral (97.6% vs. 73.5%, p = 0.001) and diffuse lesions (39.0% vs. 8.4%, p < 0.001) with higher total CT score (median 10 vs. 4, p < 0.001) were observed in non-survivor group compared with survivor group. Besides, crazy-paving pattern was more predominant in non-survivor group than survivor group (39.0% vs. 12.0%, p < 0.001). From the prediction of curve estimation, in survivor group total CT score increased in the first 20 days reaching a peak of 6 points and then gradually decreased for more than other 40 days (R2 = 0.545, p < 0.001). In non-survivor group, total CT score rapidly increased over 10 points in the first 10 days and gradually increased afterwards until ARDS occurred with following death events (R2 = 0.711, p < 0.001). In conclusion, persistent progression with predominant crazy-paving pattern was the major manifestation of COVID-19 in non-survivors. Understanding this CT feature could help the clinical physician to predict the prognosis of the patients.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Adult , Aged , Coronavirus Infections/mortality , Disease Progression , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Prognosis , Retrospective Studies , Survivors , Treatment Outcome
5.
Int J Med Sci ; 17(9): 1281-1292, 2020.
Article in English | MEDLINE | ID: covidwho-602629

ABSTRACT

Rationale: Up to date, the exploration of clinical features in severe COVID-19 patients were mostly from the same center in Wuhan, China. The clinical data in other centers is limited. This study aims to explore the feasible parameters which could be used in clinical practice to predict the prognosis in hospitalized patients with severe coronavirus disease-19 (COVID-19). Methods: In this case-control study, patients with severe COVID-19 in this newly established isolation center on admission between 27 January 2020 to 19 March 2020 were divided to discharge group and death event group. Clinical information was collected and analyzed for the following objectives: 1. Comparisons of basic characteristics between two groups; 2. Risk factors for death on admission using logistic regression; 3. Dynamic changes of radiographic and laboratory parameters between two groups in the course. Results: 124 patients with severe COVID-19 on admission were included and divided into discharge group (n=35) and death event group (n=89). Sex, SpO2, breath rate, diastolic pressure, neutrophil, lymphocyte, C-reactive protein (CRP), procalcitonin (PCT), lactate dehydrogenase (LDH), and D-dimer were significantly correlated with death events identified using bivariate logistic regression. Further multivariate logistic regression demonstrated a significant model fitting with C-index of 0.845 (p<0.001), in which SpO2≤89%, lymphocyte≤0.64×109/L, CRP>77.35mg/L, PCT>0.20µg/L, and LDH>481U/L were the independent risk factors with the ORs of 2.959, 4.015, 2.852, 3.554, and 3.185, respectively (p<0.04). In the course, persistently lower lymphocyte with higher levels of CRP, PCT, IL-6, neutrophil, LDH, D-dimer, cardiac troponin I (cTnI), brain natriuretic peptide (BNP), and increased CD4+/CD8+ T-lymphocyte ratio and were observed in death events group, while these parameters stayed stable or improved in discharge group. Conclusions: On admission, the levels of SpO2, lymphocyte, CRP, PCT, and LDH could predict the prognosis of severe COVID-19 patients. Systematic inflammation with induced cardiac dysfunction was likely a primary reason for death events in severe COVID-19 except for acute respiratory distress syndrome.


Subject(s)
Betacoronavirus/isolation & purification , Cause of Death , Coronavirus Infections/mortality , Heart Failure/mortality , Pneumonia, Viral/mortality , Systemic Inflammatory Response Syndrome/mortality , Aged , Betacoronavirus/pathogenicity , Biomarkers/blood , C-Reactive Protein/analysis , Case-Control Studies , China/epidemiology , Coronavirus Infections/blood , Coronavirus Infections/complications , Coronavirus Infections/virology , Female , Fibrin Fibrinogen Degradation Products/analysis , Heart Failure/blood , Heart Failure/virology , Humans , L-Lactate Dehydrogenase/blood , Lymphocyte Count , Male , Middle Aged , Neutrophils , Oximetry , Oxygen/blood , Pandemics , Pneumonia, Viral/blood , Pneumonia, Viral/complications , Pneumonia, Viral/virology , Procalcitonin/blood , Prognosis , ROC Curve , Risk Factors , Systemic Inflammatory Response Syndrome/blood , Systemic Inflammatory Response Syndrome/virology
7.
Emerg Microbes Infect ; 9(1): 1489-1496, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-599991

ABSTRACT

In December 2019, Wuhan, China suffered a serious outbreak of a novel coronavirus infectious disease (COVID) caused by novel severe acute respiratory syndrome-related coronavirus (SARS-CoV 2). To quickly identify the pathogen, we designed and screened primer sets, and established a sensitive and specific qRT-PCR assay for SARS-CoV 2; the lower limit of detection (LOD) was 14.8 (95% CI: 9.8-21) copies per reaction. We combined this qRT-PCR assay with an automatic integration system for nucleic acid extraction and amplification, thereby establishing an automatic integrated gene detection system (AIGS) for SARS-CoV 2. Cross reactive analysis performed in 20 other respiratory viruses and 37 nasopharyngeal swabs confirmed a 100% specificity of the assay. Using two fold diluted SARS-CoV 2 culture, the LOD of AIGS was confirmed to be 365 copies/ml (95% CI: 351-375), which was Comparable to that of conventional q RT-PCR (740 copies/ml, 95% CI: 689-750). Clinical performances of AIGS assay were assessed in 266 suspected COVID-19 clinical respiratory tract samples tested in parallel with a commercial kit. The clinical sensitivity of the AIGS test was 97.62% (95% CI: 0.9320-0.9951) based on the commercial kit test result, and concordance analysis showed a high agreement in SARS-CoV-2 detection between the two assays, Pearson R was 0.9623 (95% CI: 0.9523-0.9703). The results indicated that this AIGS could be used for rapid detection of SARS-CoV 2. With the advantage of simple operation and less time consuming, AIGS could be suitable for SARS-CoV2 detection in primary medical institutions, thus would do a great help to improve detection efficiency and control the spread of COVID-19.


Subject(s)
Betacoronavirus/isolation & purification , Coronavirus Infections/diagnosis , Pneumonia, Viral/diagnosis , Real-Time Polymerase Chain Reaction/methods , Automation, Laboratory , China , DNA Primers , Humans , Limit of Detection , Pandemics , RNA, Viral/analysis , Sensitivity and Specificity , Virus Cultivation
8.
Biomed Res Int ; 2020: 7413673, 2020.
Article in English | MEDLINE | ID: covidwho-619953

ABSTRACT

Some patients with coronavirus disease 2019 (COVID-19) show abnormal changes in laboratory myocardial injury markers, suggesting that patients with myocardial injury have a higher mortality rate than those without myocardial injury. This article reviews the possible mechanism of myocardial injury in patients with COVID-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) affects the patients with COVID-19 in aspects of direct infection of myocardial injury, specific binding to functional receptors on cardiomyocytes, and immune-mediated myocardial injury. During hospitalization, the monitoring of laboratory myocardial injury markers in patients of COVID-19 should be strengthened.


Subject(s)
Betacoronavirus , Coronavirus Infections/blood , Coronavirus Infections/complications , Heart Injuries/blood , Heart Injuries/etiology , Pneumonia, Viral/blood , Pneumonia, Viral/complications , Biomarkers/blood , Biomarkers/metabolism , Coronavirus Infections/metabolism , Cytokines/blood , Cytokines/immunology , Heart Injuries/metabolism , Humans , Inflammation Mediators/blood , Inflammation Mediators/immunology , Models, Cardiovascular , Models, Immunological , Myocytes, Cardiac/immunology , Myocytes, Cardiac/metabolism , Pandemics , Peptidyl-Dipeptidase A/metabolism , Pneumonia, Viral/metabolism
9.
Respir Res ; 21(1): 125, 2020 May 24.
Article in English | MEDLINE | ID: covidwho-343502

ABSTRACT

BACKGROUND: A cluster of patients with coronavirus disease 2019 (COVID-19) pneumonia were discharged from hospitals in Wuhan, China. We aimed to determine the cumulative percentage of complete radiological resolution at each time point, to explore the relevant affecting factors, and to describe the chest CT findings at different time points after hospital discharge. METHODS: Patients with COVID-19 pneumonia confirmed by RT-PCR who were discharged consecutively from the hospital between 5 February 2020 and 10 March 2020 and who underwent serial chest CT scans on schedule were enrolled. The radiological characteristics of all patients were collected and analysed. The total CT score was the sum of non-GGO involvement determined at discharge. Afterwards, all patients underwent chest CT scans during the 1st, 2nd, and 3rd weeks after discharge. Imaging features and distributions were analysed across different time points. RESULTS: A total of 149 patients who completed all CT scans were evaluated; there were 67 (45.0%) men and 82 (55.0%) women, with a median age of 43 years old (IQR 36-56). The cumulative percentage of complete radiological resolution was 8.1% (12 patients), 41.6% (62), 50.3% (75), and 53.0% (79) at discharge and during the 1st, 2nd, and 3rd weeks after discharge, respectively. Patients ≤44 years old showed a significantly higher cumulative percentage of complete radiological resolution than patients > 44 years old at the 3-week follow-up. The predominant patterns of abnormalities observed at discharge were ground-glass opacity (GGO) (125 [83.9%]), fibrous stripe (81 [54.4%]), and thickening of the adjacent pleura (33 [22.1%]). The positive count of GGO, fibrous stripe and thickening of the adjacent pleura gradually decreased, while GGO and fibrous stripe showed obvious resolution during the first week and the third week after discharge, respectively. "Tinted" sign and bronchovascular bundle distortion as two special features were discovered during the evolution. CONCLUSION: Lung lesions in COVID-19 pneumonia patients can be absorbed completely during short-term follow-up with no sequelae. Two weeks after discharge might be the optimal time point for early radiological estimation.


Subject(s)
Coronavirus Infections/complications , Lung Diseases/etiology , Lung Diseases/therapy , Lung/diagnostic imaging , Pneumonia, Viral/complications , Adult , Age Factors , Bronchi/diagnostic imaging , Coronavirus Infections/diagnostic imaging , Female , Follow-Up Studies , Humans , Lung Diseases/diagnostic imaging , Male , Middle Aged , Pandemics , Patient Discharge , Pleura/diagnostic imaging , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed , Treatment Outcome , Young Adult
10.
J Infect Dis ; 222(2): 203-205, 2020 06 29.
Article in English | MEDLINE | ID: covidwho-306065

ABSTRACT

The detection of SARS-CoV-2 infection is the premise of quarantine. In many countries or areas, samples need to be shipped or inactivated before SARS-CoV-2 testing. In this study, we checked the influence of sample storage conditions on SARS-CoV-2 nucleic acid testing results, including sample inactivation time, storage temperature, and storage time. All of these conditions caused an increase in the cycle threshold values of the nucleic acid tests and led to the misclassification of at least 10.2% of positive cases as negative or suspected. The results highlight the importance of immediate testing of samples for SARS-CoV-2 nucleic acid detection.


Subject(s)
Betacoronavirus , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Pharynx/virology , Pneumonia, Viral/diagnosis , Specimen Handling/methods , Betacoronavirus/genetics , Cryopreservation , Freezing , Humans , Pandemics , Refrigeration , Reverse Transcriptase Polymerase Chain Reaction , Temperature , Time Factors , Virus Inactivation
11.
Cytokine Growth Factor Rev ; 53: 66-70, 2020 06.
Article in English | MEDLINE | ID: covidwho-197442

ABSTRACT

The outbreak of the novel SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) responsible for coronavirus disease 2019 (COVID-19) has developed into an unprecedented global pandemic. Clinical investigations in patients with COVID-19 has shown a strong upregulation of cytokine and interferon production in SARS-CoV2- induced pneumonia, with an associated cytokine storm syndrome. Thus, the identification of existing approved therapies with proven safety profiles to treat hyperinflammation is a critical unmet need in order to reduce COVI-19 associated mortality. To date, no specific therapeutic drugs or vaccines are available to treat COVID-19 patients. This review evaluates several options that have been proposed to control SARS-CoV2 hyperinflammation and cytokine storm, eincluding antiviral drugs, vaccines, small-molecules, monoclonal antibodies, oligonucleotides, peptides, and interferons (IFNs).


Subject(s)
Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , Antiviral Agents/therapeutic use , Betacoronavirus/drug effects , Coronavirus Infections/drug therapy , Coronavirus Infections/pathology , Interferons/therapeutic use , Pneumonia, Viral/drug therapy , Pneumonia, Viral/pathology , Adenosine Monophosphate/therapeutic use , Alanine/therapeutic use , Antibodies, Monoclonal/therapeutic use , Cytokine Release Syndrome/drug therapy , Cytokine Release Syndrome/pathology , Cytokines/blood , Drug Therapy, Combination , Humans , Inflammation/drug therapy , Inflammation/pathology , Oligonucleotides/therapeutic use , Pandemics , Viral Vaccines/therapeutic use
12.
J Clin Virol ; 127: 104370, 2020 06.
Article in English | MEDLINE | ID: covidwho-160257

ABSTRACT

BACKGROUND: The inflammatory response plays a critical role in coronavirus disease 2019 (COVID-19), and inflammatory cytokine storm increases the severity of COVID-19. OBJECTIVE: To investigate the ability of interleukin-6 (IL-6), C-reactive protein (CRP), and procalcitonin (PCT) to predict mild and severe cases of COVID-19. STUDY DESIGN: This retrospective cohort study included 140 patients diagnosed with COVID-19 from January 18, 2020, to March 12, 2020. The study population was divided into two groups according to disease severity: a mild group (MG) (n = 107) and a severe group (SG) (n = 33). Data on demographic characteristics, baseline clinical characteristics, and the levels of IL-6, CRP, and PCT on admission were collected. RESULTS: Among the 140 patients, the levels of IL-6, CRP, and PCT increased in 95 (67.9 %), 91 (65.0 %), and 8 (5.7 %) patients on admission, respectively. The proportion of patients with increased IL-6, CRP, and PCT levels was significantly higher in the SG than in the MG. Cox proportional hazard model showed that IL-6 and CRP could be used as independent factors to predict the severity of COVID-19. Furthermore, patients with IL-6 > 32.1 pg/mL or CRP > 41.8 mg/L were more likely to have severe complications. CONCLUSION: The serum levels of IL-6 and CRP can effectively assess disease severity and predict outcome in patients with COVID-19.


Subject(s)
C-Reactive Protein/analysis , Coronavirus Infections/blood , Coronavirus Infections/diagnosis , Interleukin-6/blood , Pneumonia, Viral/blood , Pneumonia, Viral/diagnosis , Procalcitonin/analysis , Adult , Aged , Aged, 80 and over , Betacoronavirus , Biomarkers/blood , China , Female , Humans , Male , Middle Aged , Pandemics , Prognosis , Proportional Hazards Models , Retrospective Studies , Severity of Illness Index , Young Adult
13.
Infect Dis Poverty ; 9(1): 45, 2020 Apr 28.
Article in English | MEDLINE | ID: covidwho-133403

ABSTRACT

BACKGROUND: Since its discovery in December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 2 180 000 people worldwide and has caused more than 150 000 deaths as of April 16, 2020. SARS-CoV-2, which is the virus causing coronavirus disease 2019 (COVID-19), uses the angiotensin-converting enzyme 2 (ACE2) as a cell receptor to invade human cells. Thus, ACE2 is the key to understanding the mechanism of SARS-CoV-2 infection. This study is to investigate the ACE2 expression in various human tissues in order to provide insights into the mechanism of SARS-CoV-2 infection. METHODS: We compared ACE2 expression levels across 31 normal human tissues between males and females and between younger (ages ≤ 49 years) and older (ages > 49 years) persons using two-sided Student's t test. We also investigated the correlations between ACE2 expression and immune signatures in various tissues using Pearson's correlation test. RESULTS: ACE2 expression levels were the highest in the small intestine, testis, kidneys, heart, thyroid, and adipose tissue, and were the lowest in the blood, spleen, bone marrow, brain, blood vessels, and muscle. ACE2 showed medium expression levels in the lungs, colon, liver, bladder, and adrenal gland. ACE2 was not differentially expressed between males and females or between younger and older persons in any tissue. In the skin, digestive system, brain, and blood vessels, ACE2 expression levels were positively associated with immune signatures in both males and females. In the thyroid and lungs, ACE2 expression levels were positively and negatively associated with immune signatures in males and females, respectively, and in the lungs they had a positive and a negative correlation in the older and younger groups, respectively. CONCLUSIONS: Our data indicate that SARS-CoV-2 may infect other tissues aside from the lungs and infect persons with different sexes, ages, and races equally. The different host immune responses to SARS-CoV-2 infection may partially explain why males and females, young and old persons infected with this virus have markedly distinct disease severity. This study provides new insights into the role of ACE2 in the SARS-CoV-2 pandemic.


Subject(s)
Betacoronavirus , Peptidyl-Dipeptidase A/genetics , Receptors, Virus/genetics , Adult , Age Factors , Aged , Brain/enzymology , Cardiovascular System/enzymology , Cardiovascular System/immunology , Digestive System/enzymology , Digestive System/immunology , Endocrine Glands/enzymology , Endocrine Glands/immunology , Female , Gene Expression Profiling , Humans , Immune System/enzymology , Interferons/immunology , Lung/enzymology , Lung/immunology , Lymphocytes/immunology , Male , Middle Aged , Organ Specificity , Peptidyl-Dipeptidase A/blood , RNA-Seq , Receptors, Virus/blood , Sex Factors , Urogenital System/enzymology
16.
Emerg Infect Dis ; 26(7): 1583-1591, 2020 07.
Article in English | MEDLINE | ID: covidwho-47270

ABSTRACT

To determine distribution of severe acute respiratory syndrome coronavirus 2 in hospital wards in Wuhan, China, we tested air and surface samples. Contamination was greater in intensive care units than general wards. Virus was widely distributed on floors, computer mice, trash cans, and sickbed handrails and was detected in air ≈4 m from patients.


Subject(s)
Air Microbiology , Betacoronavirus/isolation & purification , Coronavirus Infections/transmission , Pneumonia, Viral/transmission , Aerosols , Hospitals , Humans , Intensive Care Units , Pandemics
17.
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
18.
AJR Am J Roentgenol ; 215(1): 127-132, 2020 07.
Article in English | MEDLINE | ID: covidwho-9461

ABSTRACT

OBJECTIVE. The purpose of this study was to describe the clinical manifestations and CT features of coronavirus disease (COVID-19) pneumonia in 15 pregnant women and to provide some initial evidence that can be used for guiding treatment of pregnant women with COVID-19 pneumonia. MATERIALS AND METHODS. We reviewed the clinical data and CT examinations of 15 consecutive pregnant women with COVID-19 pneumonia in our hospital from January 20, 2020, to February 10, 2020. A semiquantitative CT scoring system was used to estimate pulmonary involvement and the time course of changes on chest CT. Symptoms and laboratory results were analyzed, treatment experiences were summarized, and clinical outcomes were tracked. RESULTS. Eleven patients had successful delivery (10 cesarean deliveries and one vaginal delivery) during the study period, and four patients were still pregnant (three in the second trimester and one in the third trimester) at the end of the study period. No cases of neonatal asphyxia, neonatal death, stillbirth, or abortion were reported. The most common early finding on chest CT was ground-glass opacity (GGO). With disease progression, crazy paving pattern and consolidations were seen on CT. The abnormalities showed absorptive changes at the end of the study period for all patients. The most common onset symptoms of COVID-19 pneumonia in pregnant women were fever (13/15 patients) and cough (9/15 patients). The most common abnormal laboratory finding was lymphocytopenia (12/15 patients). CT images obtained before and after delivery showed no signs of pneumonia aggravation after delivery. The four patients who were still pregnant at the end of the study period were not treated with antiviral drugs but had achieved good recovery. CONCLUSION. Pregnancy and childbirth did not aggravate the course of symptoms or CT features of COVID-19 pneumonia. All the cases of COVID-19 pneumonia in the pregnant women in our study were the mild type. All the women in this study-some of whom did not receive antiviral drugs-achieved good recovery from COVID-19 pneumonia.


Subject(s)
Betacoronavirus , Coronavirus Infections/therapy , Pneumonia, Viral/therapy , Pregnancy Complications, Infectious/therapy , Pregnancy Complications, Infectious/virology , Adult , Coronavirus Infections/complications , Coronavirus Infections/diagnosis , Female , Humans , Lung/diagnostic imaging , Pandemics , Pneumonia, Viral/complications , Pneumonia, Viral/diagnosis , Pneumonia, Viral/etiology , Pregnancy , Pregnancy Complications, Infectious/diagnosis , Pregnancy Outcome , Retrospective Studies , Tomography, X-Ray Computed , Young Adult
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