A novel coronavirus (COVID-19) recently emerged as an acute respiratory syndrome, and has caused a pneumonia outbreak world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Here, we have collected chest CT scans of 88 patients diagnosed with COVID-19 from hospitals of two provinces in China, 100 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the data, a deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model could accurately discriminate the COVID-19 patients from the bacteria pneumonia patients with an AUC of 0.95, recall (sensitivity) of 0.96, and precision of 0.79. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO), which are visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by our server ( http://biomed.nscc-gz.cn/model.php ). Source codes and datasets are available at our GitHub ( https://github.com/SY575/COVID19-CT ).

Background: A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. Materials and Methods: We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. Based on the collected dataset, a deep learning-based CT diagnosis system (DeepPneumonia) was developed to identify patients with COVID-19. Results: The experimental results showed that our model can accurately identify the COVID-19 patients from others with an excellent AUC of 0.99 and recall (sensitivity) of 0.93. In addition, our model was capable of discriminating the COVID-19 infected patients and bacteria pneumonia-infected patients with an AUC of 0.95, recall (sensitivity) of 0.96. Moreover, our model could localize the main lesion features, especially the ground-glass opacity (GGO) that is of great help to assist doctors in diagnosis. The diagnosis for a patient could be finished in 30 seconds, and the implementation on Tianhe-2 supercompueter enables a parallel executions of thousands of tasks simultaneously. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/server/Ncov2019 . Conclusions: The established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

Background: Activated hepatic stellate cells (aHSCs) regulate the function of immune cells during liver fibrosis. As major innate cells in the liver, macrophages have inducible plasticity. Nevertheless, the mechanisms through which aHSCs regulate macrophages' phenotype and function during liver fibrosis and cirrhosis remain unclear. In this study, we examined the immunoregulatory function of aHSCs during liver fibrosis and explored their role in regulating macrophage phenotype and function.Methods: A total of 96 patients with different stages of chronic hepatitis B-related liver fibrosis were recruited in the study. Metavir score system was used to evaluate the degree of fibrosis. The expression of hepatic CCL2 and M2 phenotype macrophage marker CD163 were detected by immunohistochemistry, and the relationship among hepatic CD163, CCL2, and fibrosis scores were also explored. In the in vitro model, the aHSCs isolated from human liver tissues and THP-1-derived M0-type macrophages (M0MΦ) were co-cultured to observe whether and how aHSCs regulate the phenotype and function of macrophages. To explore whether CCL2/CCR2 axis has a crucial role in macrophage phenotypic changes during liver fibrosis, we treated the M0MΦ with recombinant human CCL2 or its specific receptor antagonist INCB-3284. Furthermore, we used LX2 and TGF-β-activated LX2 to mimic the different activation statuses of aHSCs to further confirm our results.Results: In patients, the infiltration of M2 macrophages increased during the progression of liver fibrosis. Intriguingly, as a key molecule for aHSC chemotactic macrophage aggregation, CCL2 markedly up-regulated the expression of CD163 and CD206 on the macrophages, which was further confirmed by adding the CCR2 antagonist (INCB 3284) into the cell culture system. In addition, the TGF-β stimulated LX2 further confirmed that aHSCs up-regulate the expression of CD163 and CD206 on macrophages. LX2 stimulated with TGF-β could produce more CCL2 and up-regulate other M2 phenotype macrophage-specific markers, including IL-10, ARG-1, and CCR2 besides CD163 and CD206 at the gene level, indicating that the different activation status of aHSCs might affect the final phenotype and function of macrophages.Conclusions: The expression of the M2 macrophage marker increases during liver fibrosis progression and is associated with fibrosis severity. AHSCs can recruit macrophages through the CCL2/CCR2 pathway and induce M2 phenotypic transformation.

Background. Platelet to white blood cell ratio (PWR) was an independent prognostic predictor for outcomes in some diseases. However, the prognostic role of PWR is still unclear in patients with hepatitis B related acute-on-chronic liver failure (ACLF). In this study, we evaluated the clinical performances of PWR in predicting prognosis in HBV-related ACLF. Methods. A total of 530 subjects were recruited, including 97 healthy controls and 433 with HBV-related ACLF. Liver function, prothrombin time activity (PTA), international normalized ratio (INR), HBV DNA measurement, and routine hematological testing were performed at admission. Results. At baseline, PWR in patients with HBV-related ACLF (14.03 ± 7.17) was significantly decreased compared to those in healthy controls (39.16 ± 9.80). Reduced PWR values were clinically associated with the severity of liver disease and the increased mortality rate. Furthermore, PWR may be an inexpensive, easily accessible, and significant independent prognostic index for mortality on multivariate analysis (HR = 0.660, 95% CI: 0.438–0.996, p=0.048) as well as model for end-stage liver disease (MELD) score. Conclusions. The PWR values were markedly decreased in ACLF patients compared with healthy controls and associated with severe liver disease. Moreover, PWR was an independent prognostic indicator for the mortality rate in patients with ACLF. This investigation highlights that PWR comprised a useful biomarker for prediction of liver severity.

Bile acid (BA) synthesis in the liver is regulated by Fibroblast Growth Factor 19 (FGF19) secreted from the ileum as an enterohepatic feedback mechanism. Although FGF19 mRNA is absent in normal liver, FGF19 gene expression was reported to increase in response to both extrahepatic and intrahepatic cholestasis. The impact of upregulated FGF19 expression on BA synthesis is unclear and the overall role of circulating FGF19 and BA synthesis under cholestatic conditions needs to be further investigated. BA synthesis was directly quantified by measuring serum concentrations of 7alpha-hydroxycholest-4-en-3-one (C4), along with serum FGF19 and other parameters, in 44 patients with primary biliary cirrhosis (PBC) and 10 healthy subjects. Serum C4 were substantially lower, while those of FGF19 were higher, in cirrhotic PBC patients, as compared to those of either healthy or non-cirrhotic PBC patients. Analyses of the relationships between circulating FGF19, BA synthesis and cholestasis revealed that circulating FGF19 was strongly correlated with BA synthesis (r = -0.735, p<0.0001) and the severity of cholestasis (r = 0.590, p<0.001). Moreover, BA synthesis was found to be strongly correlated with the degree of cholestasis (r = 0.522, p = 0.0005). These findings demonstrate that the regulation of BA synthesis in response to cholestasis is primarily controlled by circulating FGF19 and that under cholestatic conditions, the FGF19-BA synthesis feedback mechanism remains intact. Administering FGF19, or suitable mimetic, as a pharmacological intervention to increase circulating levels of FGF19 and suppress BA synthesis by inhibiting CYP7A1 gene expression is likely to provide therapeutic benefits for many PBC patients.

Background: With the advent of CRISPR-Cas9 genome editing tool in gene therapy, identification of aberrantly expressed genes is of great value across various cancer types. Since a large number of patients may benefit from molecular targeted gene therapy. The purpose of this study was to identify aberrantly expressed genes across various cancer types, analyze prospective mechanisms and their correlation with survival outcomes. Results: NCAPG was highly expressed in The Cancer Genome Atlas (TCGA) database, which includes the transcriptomes of 6,647 cancer and 647 normal tissue samples from 16 cancer types. Furthermore, a predicted NCAPG overexpression rate was also observed at the protein level in 16 tumor types. Importantly, high NCAPG level was significantly associated with unfavorable survival in various cancer types such as hepatocellular carcinoma (HCC), breast, lung or ovarian cancer. The multivariate analyses demonstrated that NCAPG, TNM, and Barcelona Clinic Liver Cancer (BCLC) staging were independent risk factors for mortality of patients with HCC. Moreover, functional and pathway enrichment analysis suggested that NCAPG was closely correlated with the pathways of cell cycle, cellular senescence, and mismatch repair. By weighted gene co-expression network analysis (WGCNA), we identified NCAPG as a hub gene in the turquoise module mostly related to the survival time of HCC samples. Conclusion: To our knowledge, this study represents a comprehensive RNA-Seq analysis of several tumor types, revealing NCAPG as a promising molecular target. NCAPG overexpression may play important roles in carcinogenesis and progression of tumors via regulating tumor-related pathways, thereby broadening the understanding of the pathogenic mechanisms and highlighting the possibility of developing novel targeted therapeutics.