目的 檢測類風濕關節炎（RA）患者血清和關節液白細胞介素17A（IL-17A）的變化，探討其與臨床炎癥指標、疾病活動性的關系。 方法 2011年6月－2012年6月采用酶聯免疫吸附試驗檢測30例活動性RA患者和20例健康對照血清IL-17A水平，其中18例有膝關節積液RA患者同時檢測配對血清和關節液IL-17A水平。 結果 RA組患者血清IL-17A水平顯著高于健康對照組[（40.651 ± 16.402）、（23.799 ± 10.693） pg/mL，P＜0.05]。RA患者關節液IL-17A水平明顯高于其血清中水平[（63.555 ± 23.405）、（43.727 ± 17.212） pg/mL，P＜0.05]。RA患者血清IL-17A水平只與疾病活動性評分（DAS28）呈正相關（r=0.498，P=0.020），而RA患者關節液IL-17A水平與DAS28和血清C反應蛋白有相關性（r=0.515，P=0.029；r=0.498，P=0.035）。 結論 RA患者血清和關節液IL-17A水平與疾病活動性顯著相關，提示IL-17A可作為衡量疾病活動和關節損傷的標志之一。
Lung cancer has the highest mortality rate among all malignant tumors. The key to reducing lung cancer mortality is the accurate diagnosis of pulmonary nodules in early-stage lung cancer. Computer-aided diagnostic techniques are considered to have potential beyond human experts for accurate diagnosis of early pulmonary nodules. The detection and classification of pulmonary nodules based on deep learning technology can continuously improve the accuracy of diagnosis through self-learning, and is an important means to achieve computer-aided diagnosis. First, we systematically introduced the application of two dimension convolutional neural network (2D-CNN), three dimension convolutional neural network (3D-CNN) and faster regions convolutional neural network (Faster R-CNN) techniques in the detection of pulmonary nodules. Then we introduced the application of 2D-CNN, 3D-CNN, multi-stream multi-scale convolutional neural network (MMCNN), deep convolutional generative adversarial networks (DCGAN) and transfer learning technology in classification of pulmonary nodules. Finally, we conducted a comprehensive comparative analysis of different deep learning methods in the detection and classification of pulmonary nodules.