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Institution

Chinese PLA General Hospital

HealthcareBeijing, China
About: Chinese PLA General Hospital is a(n) healthcare organization based out in Beijing, China. It is known for research contribution in the topic(s): Population & Cancer. The organization has 18037 authors who have published 12349 publication(s) receiving 184803 citation(s). The organization is also known as: 301 Military Hospital.
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Journal ArticleDOI
TL;DR: O surto do novo coronavírus (COVID-19) em Wuhan, China, iniciado em dezembro de 2019, evoluiu para se tornar uma pandemia global A.
Abstract: bPortuguese Abstract:O surto do novo coronavírus (COVID-19) em Wuhan, China, iniciado em dezembro de 2019, evoluiu para se tornar uma pandemia global A

5,215 citations


Journal ArticleDOI
TL;DR: The current understanding of the pathogenesis, epidemiology, management and outcomes of patients with COVID-19 who develop venous or arterial thrombosis, and of those with preexistingThrombotic disease who develop CO VID-19 are reviewed.
Abstract: Coronavirus disease-2019 (COVID-19), a viral respiratory illness caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), may predispose patients to thrombotic disease, both in the venous and arterial circulations, because of excessive inflammation, platelet activation, endothelial dysfunction, and stasis. In addition, many patients receiving antithrombotic therapy for thrombotic disease may develop COVID-19, which can have implications for choice, dosing, and laboratory monitoring of antithrombotic therapy. Moreover, during a time with much focus on COVID-19, it is critical to consider how to optimize the available technology to care for patients without COVID-19 who have thrombotic disease. Herein, the authors review the current understanding of the pathogenesis, epidemiology, management, and outcomes of patients with COVID-19 who develop venous or arterial thrombosis, of those with pre-existing thrombotic disease who develop COVID-19, or those who need prevention or care for their thrombotic disease during the COVID-19 pandemic.

1,581 citations



Journal ArticleDOI
TL;DR: It is found that digestive symptoms are common in patients with COVID-19 and that the index of suspicion may need to be raised earlier in at-risk patients presenting with digestive symptoms, but further large sample studies are needed to confirm these findings.
Abstract: Objective Since the outbreak of Coronavirus Disease 2019 (COVID-19) in December 2019, various digestive symptoms have been frequently reported in patients infected with the virus. In this study, we aimed to further investigate the prevalence and outcomes of COVID-19 patients with digestive symptoms. Methods In this descriptive, cross-sectional, multicenter study, we enrolled confirmed patients with COVID-19 who presented to 3 hospitals from January 18, 2020, to February 28, 2020. All patients were confirmed by real-time polymerase chain reaction and were analyzed for clinical characteristics, laboratory data, and treatment. Data were followed up until March 18, 2020. Results In the present study, 204 patients with COVID-19 and full laboratory, imaging, and historical data were analyzed. The average age was 52.9 years (SD ± 16), including 107 men and 97 women. Although most patients presented to the hospital with fever or respiratory symptoms, we found that 103 patients (50.5%) reported a digestive symptom, including lack of appetite (81 [78.6%] cases), diarrhea (35 [34%] cases), vomiting (4 [3.9%] cases), and abdominal pain (2 [1.9%] cases). If lack of appetite is excluded from the analysis (because it is less specific for the gastrointestinal tract), there were 38 total cases (18.6%) where patients presented with a gastrointestinal-specific symptom, including diarrhea, vomiting, or abdominal pain. Patients with digestive symptoms had a significantly longer time from onset to admission than patients without digestive symptoms (9.0 days vs 7.3 days). In 6 cases, there were digestive symptoms, but no respiratory symptoms. As the severity of the disease increased, digestive symptoms became more pronounced. Patients with digestive symptoms had higher mean liver enzyme levels, lower monocyte count, longer prothrombin time, and received more antimicrobial treatment than those without digestive symptoms. Discussion We found that digestive symptoms are common in patients with COVID-19. Moreover, these patients have a longer time from onset to admission, evidence of longer coagulation, and higher liver enzyme levels. Clinicians should recognize that digestive symptoms, such as diarrhea, are commonly among the presenting features of COVID-19 and that the index of suspicion may need to be raised earlier in at-risk patients presenting with digestive symptoms. However, further large sample studies are needed to confirm these findings.

1,018 citations


Journal ArticleDOI
TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.
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.

942 citations


Authors

Showing all 18037 results

NameH-indexPapersCitations
Jie Zhang1784857221720
Gregory Y.H. Lip1693159171742
Chao Zhang127311984711
Hong Wang110163351811
Shuji Ogino10654943073
Li Chen105173255996
Jing Wang97112353714
Wei Wang95354459660
Zhiguo Yuan9363328645
Tai Hing Lam93116851646
Christopher P. Crum8741232399
Guozhen Shen8442223992
Jing-Feng Li8150723434
Zongjin Li8063022103
Wan Yee Lau7646321257
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202223
20212,013
20201,853
20191,159
2018943
2017967