目的 检测类风湿关节炎（RA）患者血清和关节液白细胞介素17A（IL-17A）的变化，探讨其与临床炎症指标、疾病活动性的关系。 方法 2011年6月－2012年6月采用酶联免疫吸附试验检测30例活动性RA患者和20例健康对照血清IL-17A水平，其中18例有膝关节积液RA患者同时检测配对血清和关节液IL-17A水平。 结果 RA组患者血清IL-17A水平显著高于健康对照组[（40.651 ± 16.402）、（23.799 ± 10.693） pg/mL，P＜0.05]。RA患者关节液IL-17A水平明显高于其血清中水平[（63.555 ± 23.405）、（43.727 ± 17.212） pg/mL，P＜0.05]。RA患者血清IL-17A水平只与疾病活动性评分（DAS28）呈正相关（r=0.498，P=0.020），而RA患者关节液IL-17A水平与DAS28和血清C反应蛋白有相关性（r=0.515，P=0.029；r=0.498，P=0.035）。 结论 RA患者血清和关节液IL-17A水平与疾病活动性显著相关，提示IL-17A可作为衡量疾病活动和关节损伤的标志之一。
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.