目的:通过研究儿童呼吸和哮喘控制测试(test for respiratory and asthma control in kids,TRACK)和儿童哮喘控制测试(childhood asthma control test,C-ACT)与全球哮喘防治创议(global initiative for asthma,GINA)标准哮喘控制水平评...目的:通过研究儿童呼吸和哮喘控制测试(test for respiratory and asthma control in kids,TRACK)和儿童哮喘控制测试(childhood asthma control test,C-ACT)与全球哮喘防治创议(global initiative for asthma,GINA)标准哮喘控制水平评估的一致性、与哮喘儿童肺功能指标的相关性,探讨2种评分方法在儿童哮喘管理中的价值。方法:选择2020年8月至2022年3月重庆市妇幼保健院儿科门诊就诊的哮喘患儿135例为研究对象,用TRACK评分表和C-ACT评分表对相应年龄的患儿及其家长进行问卷调查,分析2种评分方法与GINA标准哮喘控制水平评估分级的一致性、与肺功能指标的相关性,并比较不同评分结果组肺功能指标差异。结果:TRACK评分、C-ACT评分与GINA哮喘控制水平分级一致性检验Kappa值分别为0.517和0.531,均显示一致性一般;TRACK评分与TPF%TE和VPF%VE存在显著的正相关,但TRACK评分、C-ACT评分与FEV1%均没有显著性相关;不同TRACK评分组哮喘患儿的FEV1%差异无统计学意义(F=2.054,P=0.134),但多重比较发现≥80分组与<60分组FEV1%的差异有统计学意义(P=0.048);不同TRACK评分组哮喘患儿的TPTEF/TE(%)(F=3.171,P=0.044)和VPEF/VE(%)(F=3.919,P=0.022)差异有统计学意义,进一步多重比较发现其差异主要来自于≥80分组与60~80分组,其中VPEF/VE(%)(P=0.017)的组间差异较TPTEF/TE(%)(P=0.030)更为明显;比较不同C-ACT评分组哮喘患儿的FEV1%差异无统计学意义(F=1.756,P=0.182)。结论:TRACK评分能更好反映儿童肺功能的差异,可作为5岁以下儿童哮喘管理的一个有效评估工具。展开更多
The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,th...The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.展开更多
文摘目的:通过研究儿童呼吸和哮喘控制测试(test for respiratory and asthma control in kids,TRACK)和儿童哮喘控制测试(childhood asthma control test,C-ACT)与全球哮喘防治创议(global initiative for asthma,GINA)标准哮喘控制水平评估的一致性、与哮喘儿童肺功能指标的相关性,探讨2种评分方法在儿童哮喘管理中的价值。方法:选择2020年8月至2022年3月重庆市妇幼保健院儿科门诊就诊的哮喘患儿135例为研究对象,用TRACK评分表和C-ACT评分表对相应年龄的患儿及其家长进行问卷调查,分析2种评分方法与GINA标准哮喘控制水平评估分级的一致性、与肺功能指标的相关性,并比较不同评分结果组肺功能指标差异。结果:TRACK评分、C-ACT评分与GINA哮喘控制水平分级一致性检验Kappa值分别为0.517和0.531,均显示一致性一般;TRACK评分与TPF%TE和VPF%VE存在显著的正相关,但TRACK评分、C-ACT评分与FEV1%均没有显著性相关;不同TRACK评分组哮喘患儿的FEV1%差异无统计学意义(F=2.054,P=0.134),但多重比较发现≥80分组与<60分组FEV1%的差异有统计学意义(P=0.048);不同TRACK评分组哮喘患儿的TPTEF/TE(%)(F=3.171,P=0.044)和VPEF/VE(%)(F=3.919,P=0.022)差异有统计学意义,进一步多重比较发现其差异主要来自于≥80分组与60~80分组,其中VPEF/VE(%)(P=0.017)的组间差异较TPTEF/TE(%)(P=0.030)更为明显;比较不同C-ACT评分组哮喘患儿的FEV1%差异无统计学意义(F=1.756,P=0.182)。结论:TRACK评分能更好反映儿童肺功能的差异,可作为5岁以下儿童哮喘管理的一个有效评估工具。
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grants 61941104,61921004the Key Research and Development Program of Shandong Province under Grant 2020CXGC010108+1 种基金the Southeast University-China Mobile Research Institute Joint Innovation Centersupported in part by the Scientific Research Foundation of Graduate School of Southeast University under Grant YBPY2118.
文摘The great potentials of massive Multiple-Input Multiple-Output(MIMO)in Frequency Division Duplex(FDD)mode can be fully exploited when the downlink Channel State Information(CSI)is available at base stations.However,the accurate CsI is difficult to obtain due to the large amount of feedback overhead caused by massive antennas.In this paper,we propose a deep learning based joint channel estimation and feedback framework,which comprehensively realizes the estimation,compression,and reconstruction of downlink channels in FDD massive MIMO systems.Two networks are constructed to perform estimation and feedback explicitly and implicitly.The explicit network adopts a multi-Signal-to-Noise-Ratios(SNRs)technique to obtain a single trained channel estimation subnet that works well with different SNRs and employs a deep residual network to reconstruct the channels,while the implicit network directly compresses pilots and sends them back to reduce network parameters.Quantization module is also designed to generate data-bearing bitstreams.Simulation results show that the two proposed networks exhibit excellent performance of reconstruction and are robust to different environments and quantization errors.