Long non-coding RNAs (lncRNAs) are being found to play an increasingly important role in the development of tumors. However, their biological functions and the underlying mechanisms remain unclear. Using information from GEO Datasets, we found that the lncRNA LINC00588 was downregulated in osteosarcoma (OS) in bone but was upregulated in the metastatic tumor present in the lung. We assessed the function of LINC00588 using both overexpression and knockout studies. We performed colony formation assay, CCK-8 assay, flow cytometry, wound healing assay, transwell assay, and RT-qPCR assay and used a xenograft model to investigate the influence of LINC00588 on cell proliferation, viability, cell apoptosis and cycle, migration, invasion, endothelial cell function, EMT (epithelial to mesenchymal transition), and tumor growth, respectively. Overexpression of LINC00588 appeared to inhibit cell proliferation, viability, migration, invasion, endothelial cell function, EMT, and tumor growth but not apoptosis, while we got the opposite result when we knocked down LINC00588. Next, we predicted that LINC00588 bound to miRNA-1972 and significantly downregulated its expression, which we then verified through a luciferase reporter assay. Subsequently, we knocked down miR1972 and performed CCK-8 and transwell assays to demonstrate that downregulation of miRNA-1972 could substantially inhibit the viability and invasion of osteosarcoma cells. The expression of TP53 was downregulated at the protein level but not at the mRNA level after the overexpression of miRNA-1972. Taken together, our findings indicate that LINC00588 plays a role in OS development by downregulating the expression of miRNA-1972, which can, in turn, inhibit the expression of TP53. Hence, we believe that the LINC00588/miRNA-1072/TP53 axis could potentially serve as a therapeutic target or diagnostic biomarker for osteosarcoma.

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

Abstract. Liver stiffness, which correlates well with liver fibrosis stage, can be measured noninvasively by transient elastography, also known as Fibroscan. The present study aimed to determine the independent factors influencing Fibroscan detection by multiple regression analysis. A total of 181 patients who required liver biopsy were enrolled. Liver stiffness measurement (LSM) was detected by Fibroscan on the day of liver biopsy, while clinical information and routine biochemical examination results were also collected. Correlation was analyzed by Spearman's correlation, and multiple regression analysis was performed to analyze the independent influencing factors. The results demonstrated that platelet (PLT) levels, serum albumin (ALB), prothrombin activity (PTA) and body mass index (BMI) were independent predictors of liver stiffness. The contribution of these four predictors to the regression equation was in the following descending order: PLT (negative correlation) > ALB (negative correlation) > PTA (negative correlation) > BMI (positive correlation). In conclusion, the parameters of PLT, ALB, PTA and BMI are independent predicting factors affecting Fibroscan detection. Therefore, the diagnosis and evaluation of liver fibrosis should comprehensively consider the results of Fibroscan, and clinical and laboratory examinations.