目的:分析食管癌患者放疗期间预防性使用吡非尼酮对放射性肺炎的影响。方法:回顾性收集2017年11月至2020年1月于福建医科大学附属协和医院接受调强放疗(intensity-modulated radiation therapy,IMRT)的胸段食管癌患者资料,按是否使用吡...目的:分析食管癌患者放疗期间预防性使用吡非尼酮对放射性肺炎的影响。方法:回顾性收集2017年11月至2020年1月于福建医科大学附属协和医院接受调强放疗(intensity-modulated radiation therapy,IMRT)的胸段食管癌患者资料,按是否使用吡非尼酮将患者分为吡非尼酮组和对照组,通过逆概率处理加权法(inverse probability of treatment weighting,IPTW)将各协变量在两组人群进行加权处理,分析两组人群2级及3级以上放射性肺炎的发生率。结果:共纳入170例符合要求的病例,其中吡非尼酮组40例,对照组130例。中位随访时间22.6个月,通过对年龄、吡非尼酮用药史、放疗剂量、双肺V_(5)及V_(20)等可能影响放射性肺炎发生的临床因素及肺体积剂量参数等进行IPTW法加权分析,加权后两组基线特征标准化均值差值下降99.72%,两组2级以上、3级以上放射性肺炎发生率分别为3.92%vs.14.73%(P=0.0007)及3.92%vs.10.99%(P=0.0141),差异均有统计学意义。多因素Logistic回归分析发现吡非尼酮用药史(2级P=0.0017,3级P=0.0191)、年龄(2级P=0.0336,3级P=0.0028)、放疗剂量(2级P=0.0119,3级P=0.0031)均与2级和3级以上放射性肺炎相关。吡非尼酮组未发现明显的不良反应。结论:接受IMRT治疗食管癌患者,放疗期间同步使用吡非尼酮可有效降低2级及3级以上放射性肺炎的发生,安全性好,值得开展进一步临床研究证实。展开更多
A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters i...A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.展开更多
文摘目的:分析食管癌患者放疗期间预防性使用吡非尼酮对放射性肺炎的影响。方法:回顾性收集2017年11月至2020年1月于福建医科大学附属协和医院接受调强放疗(intensity-modulated radiation therapy,IMRT)的胸段食管癌患者资料,按是否使用吡非尼酮将患者分为吡非尼酮组和对照组,通过逆概率处理加权法(inverse probability of treatment weighting,IPTW)将各协变量在两组人群进行加权处理,分析两组人群2级及3级以上放射性肺炎的发生率。结果:共纳入170例符合要求的病例,其中吡非尼酮组40例,对照组130例。中位随访时间22.6个月,通过对年龄、吡非尼酮用药史、放疗剂量、双肺V_(5)及V_(20)等可能影响放射性肺炎发生的临床因素及肺体积剂量参数等进行IPTW法加权分析,加权后两组基线特征标准化均值差值下降99.72%,两组2级以上、3级以上放射性肺炎发生率分别为3.92%vs.14.73%(P=0.0007)及3.92%vs.10.99%(P=0.0141),差异均有统计学意义。多因素Logistic回归分析发现吡非尼酮用药史(2级P=0.0017,3级P=0.0191)、年龄(2级P=0.0336,3级P=0.0028)、放疗剂量(2级P=0.0119,3级P=0.0031)均与2级和3级以上放射性肺炎相关。吡非尼酮组未发现明显的不良反应。结论:接受IMRT治疗食管癌患者,放疗期间同步使用吡非尼酮可有效降低2级及3级以上放射性肺炎的发生,安全性好,值得开展进一步临床研究证实。
基金supported by the National Natural Science Foundation of China(61401196)the Jiangsu Provincial Natural Science Foundation of China(BK20140954)+1 种基金the Science and Technology on Information Transmission and Dissemination in Communication Networks Laboratory(KX152600015/ITD-U15006)the Beijing Shengfeifan Electronic System Technology Development Co.,Ltd(KY10800150036)
文摘A novel modulation recognition algorithm is proposed by introducing a Chen-Harker-Kanzow-Smale (CHKS) smooth function into the C-support vector machine deformation algorithm. A set of seven characteristic parameters is selected from a range of parameters of communication signals including instantaneous amplitude, phase, and frequency. And the Newton-Armijo algorithm is utilized to train the proposed algorithm, namely, smooth CHKS smooth support vector machine (SCHKS-SSVM). Compared with the existing algorithms, the proposed algorithm not only solves the non-differentiable problem of the second order objective function, but also reduces the recognition error. It significantly improves the training speed and also saves a large amount of storage space through large-scale sorting problems. The simulation results show that the recognition rate of the algorithm can batch training. Therefore, the proposed algorithm is suitable for solving the problem of high dimension and its recognition can exceed 95% when the signal-to-noise ratio is no less than 10 dB.
