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To study the classification efficiency of using texture feature machine learning method in distinguishing solid lung adenocarcinoma (SADC) and tuberculous granulomatous nodules (TGN) that appear as solid nodules (SN) in non-enhanced CT images. 200 patients with SADC and TGN who underwent thoracic non-enhanced CT examination from January 2012 to October 2019 were included in the study, 490 texture eigenvalues of 6 categories were extracted from the lesions in the non-enhanced CT images of these patients for machine learning, the classification prediction model is established by using relatively the best classifier selected according to the fitting degree of learning curve in the process of machine learning, and the effectiveness of the model was tested and verified. The logistic regression model of clinical data (including demographic data and CT parameters and CT signs of solitary nodules) was used for comparison. The prediction model of clinical data was established by logistic regression, and the classifier was established by machine learning of radiologic texture features. The area under the curve was 0.82 and 0.65 for the prediction model based on clinical CT and only CT parameters and CT signs, and 0.870 based on Radiomics characteristics. The machine learning prediction model developed by us can improve the differentiation efficiency of SADC and TGN with SN, and provide appropriate support for treatment decisions. [ABSTRACT FROM AUTHOR]

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia. [ABSTRACT FROM AUTHOR]

Metformin or glucagon-like peptide-1 (GLP-1) analogue liraglutide has cardiovascular benefits. However, it is not clear whether their combined treatment have additive or synergistic effects on the vasculature. In this study, human umbilical vein endothelial cells (HUVECs), exposed to palmitic acid (PA) to induce endothelial dysfunction, were incubated with metformin, liraglutide or their combination. High fat diet (HFD)-fed ApoE−/− mice were randomized into control, metformin, liraglutide, and combination treatment groups. Results showed that in PA-treated HUVECs and HFD-fed ApoE−/− mice, combination of metformin and liraglutide at lower dose significantly improved endothelial dysfunction compared with the single treatment. Metformin upregulated GLP-1 receptor (GLP-1R) level and protein kinase A (PKA) phosphorylation. However, PKA inhibition but not GLP-1R blockade eliminated the protective effects of metformin on endothelial function. Furthermore, AMPK inhibitor compound C abolished the metformin-mediated upregulation of GLP-1R level and PKA phosphorylation. In conclusion, combination of metformin and liraglutide has synergistic protective effects on endothelial function. Moreover, metformin stimulates GLP-1R and PKA signalling via AMPK-dependent pathway, which may account for its synergistic protective effects with liraglutide. Our findings provide new insights on the interaction between metformin and GLP-1, and provide important information for designing new GLP-1-based therapy strategies in treating type 2 diabetes. [ABSTRACT FROM AUTHOR]