Objective·To analyze the differences and classify hypertrophic cardiomyopathy(HCM)patients and healthy controls(HC)using short-axis cine cardiac magnetic resonance(CMR)images-derived radiomics features.Methods·One hundred HCM subjects were included,and fifty HC were randomly selected at 2∶1 ratio during January 2018 to December 2021 in the Department of Cardiology,Renji Hospital,Shanghai Jiao Tong University School of Medicine.The CMR examinations were performed by experienced radiologists on these subjects.CVI 42 post-processing software was used to obtain left ventricular morphology and function measurements,including left ventricular ejection fraction(LVEF),left ventricular end-diastolic volume(LVEDV)and left ventricular end-diastolic mass(LVEDM).The 3D radiomic features of the end-diastolic myocardial region were extracted from short-axis images CMR cine.The distribution of the radiomic features in the two groups was analysed and machine learning models were constructed to classify the two groups.Results·One hundred and seven 3D radiomic features were selected and extracted.After exclusion of highly correlated features,least absolute shrinkage and selection operator(LASSO)was used,and a 5-fold cross-validation was performed.There were still 11 characteristics with non-zero coefficients.The K-best method was used to decide the top 8 features for subsequent analysis.Among them,four features were significantly different between the two groups(all P<0.05).Support vector machine(SVM)and random forest(RF)models were constructed to discriminate the two groups.The results showed that the maximum area under the curve(AUC)for the single-feature model(first order grayscale:entropy)was 0.833(95%CI 0.685?0.968)and the maximum accuracy for the multi-feature model was 83.3%with an AUC of 0.882(95%CI 0.705?0.980).Conclusion·There are significant differences in both left ventricular function and left ventricular morphology between HCM and HC.The 3D myocardial radiomic features of the two groups are also significantly different.Although single feature is able to distinguish the two groups,the combination of multi-features show better classification performance.

Objective To characterize the distribution and assess the exposure to phthalic acid esters （PAEs） in the indoor dust of Shanghai City. Methods Samples were collected from 33 sampling sites， including homes， hotels， offices and public places， in Shanghai in 2018， 2019， and 2020. The samples were pretreated by 100 sieves， extracted and concentrated， and then analyzed by gas chromatography-mass spectrometry in selected ion mode （SIM）. Results Results on the characteristics of PAEs in indoor dust in different places showed that concentrations of PAEs were in a range of <0.01-2 464 mg·kg^-1.The average concentration of 16 PAEs was 613 mg·kg^-1. Bis（2-ethylhexyl） phthalate （DEHP）， di-iso-butyl phthalate （DiBP）， di-n-butyl phthalate （DBP） and di-n-octyl phthalate （DnOP） were the main components of PAEs in indoor dust， accounting for approximately 99.5% of 16 PAEs. The intake of DEHP， DBP， DEP and BBP was lower than the tolerable daily intake （TDI） and reference doses （RfD） set by EU CSTEE and U.S. EPA. Conclusion Average daily dose （ADD） via indoor dust is estimated， and the order of intake through different pathways is hand-oral intake>skin contact>respiratory inhalation. Exposure risk of PAEs in children is greater than that in adults.