Objective To observe the value of residual neural network(ResNet)-101-feature pyramid network(FPN)model based on CT for differentiating benign and malignant lung nodules.Methods Totally 2 040 lung nodules in 2 000 patients were retrospectively enrolled,including 1 150 benign and 890 malignant nodules.The nodules were divided into training set(n=1 632)and test set(n=408)at the ratio of 8∶2,the former including 881 benign and 751 malignant ones,while the latter including 269 benign and 139 malignant ones,respectively.Taken ResNet-101 as the backbone network,combined with FPN,a classification model was established based on chest CT,and the efficiency of this model alone and combined with evaluation of physicians for differentiating benign and malignant lung nodules were evaluated.Results Among 269 benign lung nodules in test set,ResNet-101-FPN model alone correctly diagnosed 214 nodules(214/269,79.55%),while combined with evaluation of physicians correctly diagnosed 230 ones(230/269,85.50%).For 139 malignant nodules in test set,ResNet-101-FPN model alone correctly diagnosed 124 nodules(124/139,89.21%),while combined with evaluation of physicians correctly diagnosed 131 ones(131/139,94.24%).The sensitivity,accuracy and precision of ResNet-101-FPN model combined with evaluation of physicians for distinguishing benign and malignant lung nodules were all higher,while the specificity of the combination was lower than those of ResNet-101-FPN model alone,but the differences were not significant(all P＞0.05).Conclusion ResNet-101-FPN model could be used to distinguish benign and malignant lung nodules based on CT.Combining with evaluation of physicians could improve diagnostic efficiency of this model.

Peptides are increasingly important resources for biological and therapeutic development, however, their intrinsic susceptibility to proteolytic degradation represents a big hurdle. As a natural agonist for GLP-1R, glucagon-like peptide 1 (GLP-1) is of significant clinical interest for the treatment of type-2 diabetes mellitus, but its in vivo instability and short half-life have largely prevented its therapeutic application. Here, we describe the rational design of a series of α/sulfono-γ-AA peptide hybrid analogues of GLP-1 as the GLP-1R agonists. Certain GLP-1 hybrid analogues exhibited enhanced stability (t _1/2 > 14 days) compared to t _1/2 (<1 day) of GLP-1 in the blood plasma and in vivo. These newly developed peptide hybrids may be viable alternative of semaglutide for type-2 diabetes treatment. Additionally, our findings suggest that sulfono-γ-AA residues could be adopted to substitute canonical amino acids residues to improve the pharmacological activity of peptide-based drugs.