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J Med Virol ; 2021 Sep 28.
Article in English | MEDLINE | ID: covidwho-1439702
Ann Palliat Med ; 10(7): 7329-7339, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1311480


BACKGROUND: This study aimed to build a radiomics model with deep learning (DL) and human auditing and examine its diagnostic value in differentiating between coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP). METHODS: Forty-three COVID-19 patients, whose diagnoses had been confirmed with reverse-transcriptase polymerase-chain-reaction (RT-PCR) tests, and 60 CAP patients, whose diagnoses had been confirmed with sputum cultures, were enrolled in this retrospective study. The candidate regions of interest (ROIs) on the computed tomography (CT) images of the 103 patients were determined using a DL-based segmentation model powered by transfer learning. These ROIs were manually audited and corrected by 3 radiologists (with an average of 12 years of experience; range 6-17 years) to check the segmentation acceptance for the radiomics analysis. ROI-derived radiomics features were subsequently extracted to build the classification model and processed using 4 different algorithms (L1 regularization, Lasso, Ridge, and Z test) and 4 classifiers, including the logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM), and extreme Gradient Boosting (XGboost). A receiver operating characteristic curve (ROC) analysis was conducted to evaluate the performance of the model. RESULTS: Quantitative CT measurements derived from human-audited segmentation results showed that COVID-19 patients had significantly decreased numbers of infected lobes compared to patients in the CAP group {median [interquartile range (IQR)]: 4 [3, 4] and 4 [4, 5]; P=0.031}. The infected percentage (%) of the whole lung was significantly more elevated in the CAP group [6.40 (2.77, 11.11)] than the COVID-19 group [1.83 (0.65, 4.42); P<0.001], and the same trend applied to each lobe, except for the superior lobe of the right lung [1.81 (0.09, 5.28) for COVID-19 vs. 1.32 (0.14, 7.02) for CAP; P=0.649]. Additionally, the highest proportion of infected lesions were observed in the CT value range of (-470, -370) Hounsfield units (HU) in the COVID-19 group. Conversely, the CAP group had a value range of (30, 60) HU. Radiomic model using corrected ROIs exhibited the highest area under ROC (AUC) of 0.990 [95% confidence interval (CI): 0.962-1.000] using Lasso for feature selection and MLP for classification. CONCLUSIONS: The proposed radiomics model based on human-audited segmentation made accurate differential diagnoses of COVID-19 and CAP. The quantification of CT measurements derived from DL could potentially be used as effective biomarkers in current clinical practice.

COVID-19 , Deep Learning , Computers , Humans , Retrospective Studies , SARS-CoV-2
Chin J Acad Radiol ; : 1-9, 2021 Jun 28.
Article in English | MEDLINE | ID: covidwho-1286228


Background: Coronary artery calcification (CAC) is an independent risk factor of major adverse cardiovascular events; however, the impact of CAC on in-hospital death and adverse clinical outcomes in patients with coronavirus disease 2019 (COVID-19) remains unclear. Objective: To explore the association between CAC and in-hospital mortality and adverse events in patients with COVID-19. Methods: This multicenter retrospective cohort study enrolled 2067 laboratory-confirmed COVID-19 patients with definitive clinical outcomes (death or discharge) admitted from 22 tertiary hospitals in China between January 3, 2020 and April 2, 2020. Demographic, clinical, laboratory results, chest CT findings, and CAC on admission were collected. The primary outcome was in-hospital death and the secondary outcome was composed of in-hospital death, admission to intensive care unit (ICU), and requiring mechanical ventilation. Multivariable Cox regression analysis and Kaplan-Meier plots were used to explore the association between CAC and in-hospital death and adverse clinical outcomes. Results: The mean age was 50 years (SD,16) and 1097 (53.1%) were male. A total of 177 patients showed high CAC level, and compared with patients with low CAC, these patients were older (mean age: 49 vs. 69 years, P < 0.001) and more likely to be male (52.0% vs. 65.0%, P = 0.001). Comorbidities, including cardiovascular disease (CVD) ([33.3%, 59/177] vs. [4.7%, 89/1890], P < 0.001), presented more often among patients with high CAC, compared with patients with low CAC. As for laboratory results, patients with high CAC had higher rates of increased D-dimer, LDH, as well as CK-MB (all P < 0.05). The mean CT severity score in high CAC group was also higher than low CAC group (12.6 vs. 11.1, P = 0.005). In multivariable Cox regression model, patients with high CAC were at a higher risk of in-hospital death (hazard ratio [HR], 1.731; 95% CI 1.010-2.971, P = 0.046) and adverse clinical outcomes (HR, 1.611; 95% CL 1.087-2.387, P = 0.018). Conclusion: High CAC is a risk factor associated with in-hospital death and adverse clinical outcomes in patients with confirmed COVID-19, which highlights the importance of calcium load testing for hospitalized COVID-19 patients and calls for attention to patients with high CAC. Supplementary Information: The online version contains supplementary material available at 10.1007/s42058-021-00072-4.

Front Immunol ; 12: 679482, 2021.
Article in English | MEDLINE | ID: covidwho-1285291


Infection with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes COVID-19, a disease that involves significant lung tissue damage. How SARS-CoV-2 infection leads to lung injury remains elusive. The open reading frame 8 (ORF8) protein of SARS-CoV-2 (ORF8SARS-CoV-2) is a unique accessory protein, yet little is known about its cellular function. We examined the cellular distribution of ORF8SARS-CoV-2 and its role in the regulation of human lung epithelial cell proliferation and antiviral immunity. Using live imaging and immunofluorescent staining analyses, we found that ectopically expressed ORF8SARS-CoV-2 forms aggregates in the cytosol and nuclear compartments of lung epithelial cells. Using in silico bioinformatic analysis, we found that ORF8SARS-CoV-2 possesses an intrinsic aggregation characteristic at its N-terminal residues 1-18. Cell culture did not reveal any effects of ORF8SARS-CoV-2 expression on lung epithelial cell proliferation and cell cycle progression, suggesting that ORF8SARS-CoV-2 aggregates do not affect these cellular processes. Interestingly, ectopic expression of ORF8SARS-CoV-2 in lung epithelial cells suppressed basal expression of several antiviral molecules, including DHX58, ZBP1, MX1, and MX2. In addition, expression of ORF8SARS-CoV-2 attenuated the induction of antiviral molecules by IFNγ but not by IFNß in lung epithelial cells. Taken together, ORF8SARS-CoV-2 is a unique viral accessory protein that forms aggregates when expressing in lung epithelial cells. It potently inhibits the expression of lung cellular anti-viral proteins at baseline and in response to IFNγ in lung epithelial cells, which may facilitate SARS-CoV-2 escape from the host antiviral innate immune response during early viral infection. In addition, it seems that formation of ORF8SARS-CoV-2 aggregate is independent from the viral infection. Thus, it would be interesting to examine whether any COVID-19 patients exhibit persistent ORF8 SARS-CoV-2 expression after recovering from SARS-CoV-2 infection. If so, the pathogenic effect of prolonged ORF8SARS-CoV-2 expression and its association with post-COVID symptoms warrant investigation in the future.

COVID-19/immunology , Lung/pathology , Respiratory Mucosa/physiology , SARS-CoV-2/physiology , Viral Proteins/metabolism , COVID-19/virology , Gene Expression Regulation , HEK293 Cells , Humans , Immunity , Interferon-gamma/metabolism , Intracellular Space , Protein Aggregation, Pathological , Respiratory Mucosa/virology
Quant Imaging Med Surg ; 10(5): 1153-1157, 2020 May.
Article in English | MEDLINE | ID: covidwho-520941