Objective: To set up a multi-components by single maker (QAMS) method for determination of four diterpene lactones in Lianzhi Xiaoyan capsules.
Methods: Shimadzu Shim-pack C₁₈ column（250 mm×4.6 mm,5 μm）was adopted with 35 ℃. Acetonitrile-0.1% phosphoric acid aqueous solution as the mobile phase in gradient mode was used at flow rate of 1.0 mL·min^-1. The detection wavelength was set at 203 nm. Andrographolide was used as reference to calculate the relative correction factors of the other three components.
Results: The linear relationships of four components were good （r≥0.999 9） within their ranges, and their average recoveries were 100.0%-102.8% with RSDs 1.1%-2.2%. The results obtained by QAMS were close to those obtained by external standard method.
Conclusion: This rapid, precise and reproducible method can be used for the quality control of Lianzhi Xiaoyan capsules.

Objective To explore the value of radiomics in differential diagnosis of small cell lung cancer(SCLC)and non-small cell lung cancer(NSCLC).Methods Literature on the differential diagnosis of SCLC and NSCLC using radiomics was searched in Chinese and English databases.After literature screening and data extraction,Meta-DiSc1.4 and State16.0 SE software were used for analysis.Results A total of 910 patients were included in 8 studies.Meta-analysis results showed that the radiomics differential diag-nosis of SCLC and NSCLC had high co-sensitivity(Sen)and specificity(Spe),0.87[95%confidence interval(CI)0.83-0.91]and 0.88(95%CI 0.85-0.90),respectively.Meta-regression analysis showed that heterogeneity was not caused by feature extraction software type,joint machine learning,image pattern,brain metastasis,and sample size.Publication bias results didn't show any sig-nificant publication bias.Conclusion The radiomics method can differentiate and diagnose SCLC from NSCLC more accurately.When Matlab software is used to extract MRI image features combined with machine learning,and the sample size is large enough,the radiomics can differentiate and diagnose SCLC from NSCLC more accurately.