摘要：设计合成了一种四足针尖状铁钯纳米颗粒(简称TFPs),分别通过诱导乳腺癌细胞自噬和巨噬细胞释放促炎因子,实现在光声成像引导下的乳腺肿瘤细胞铁死亡协同免疫治疗.首先,该新型TFPs具有优异的光学吸收和较高的光热转换效率,是良好的光声成像造影剂.其次,TFPs具有过氧化物酶/谷胱甘肽氧化酶模拟酶性能,能原位催化肿瘤区过表达过氧化氢,产生羟基自由基,并同时下调胞内谷胱甘肽水平,协同诱导细胞铁死亡.此外,其独特的针状形态可以进一步触发肿瘤细胞自噬,加快铁蛋白降解,释放铁离子,进而增效铁死亡;同时,特殊的尖状结构能触发巨噬细胞释放促炎细胞因子,增效放大免疫效应,在与PD-L1检查点抑制剂联合使用后,取得最强的抗肿瘤效果.这项工作不仅开发了一种高效的铁死亡诱导剂,也为在纳米尺度调控肿瘤细胞自噬/免疫功能提供了一种全新的策略.

摘要：Objective To observe the value of deep learning (DL) models for automatic classification of echocardiographic views. Methods Totally 100 patients after heart transplantation were retrospectively enrolled and divided into training set, validation set and test set at a ratio of 7 ∶ 2 ∶ 1. ResNet18, ResNet34, Swin Transformer and Swin Transformer V2 models were established based on 2D apical two chamber view, 2D apical three chamber view, 2D apical four chamber view, 2D subcostal view, parasternal long-axis view of left ventricle, short-axis view of great arteries, short-axis view of apex of left ventricle, short-axis view of papillary muscle of left ventricle, short-axis view of mitral valve of left ventricle, also 3D and CDFI views of echocardiography. The accuracy, precision, recall, F1 score and confusion matrix were used to evaluate the performance of each model for automatically classifying echocardiographic views. The interactive interface was designed based on Qt Designer software and deployed on the desktop. Results The performance of models for automatically classifying echocardiographic views in test set were all good, with relatively poor performance for 2D short-axis view of left ventricle and superior performance for 3D and CDFI views. Swin Transformer V2 was the optimal model for automatically classifying echocardiographic views, with high accuracy, precision, recall and F1 score was 92.56%, 89.01%, 89.97% and 89.31%, respectively, which also had the highest diagonal value in confusion matrix and showed the best classification effect on various views in t-SNE figure. Conclusion DL model had good performance for automatically classifying echocardiographic views, especially Swin Transformer V2 model had the best performance. Using interactive classification interface could improve the interpretability of prediction results to some extent.