目的观察穴位贴敷配合他达拉非治疗勃起功能障碍合并抑郁的临床疗效。方法将102例勃起功能障碍合并抑郁患者随机分为治疗组(52例)和对照组(50例)。治疗组采用穴位贴敷配合口服他达拉非治疗,对照组采用单纯口服他达拉非治疗。观察两组治...目的观察穴位贴敷配合他达拉非治疗勃起功能障碍合并抑郁的临床疗效。方法将102例勃起功能障碍合并抑郁患者随机分为治疗组(52例)和对照组(50例)。治疗组采用穴位贴敷配合口服他达拉非治疗,对照组采用单纯口服他达拉非治疗。观察两组治疗前后国际勃起功能指数(the international index of erectile function-5,IIEF-5)评分、RigiScan AVSS各项监测数据(勃起总时间、根部≥60%勃起时间、头部≥60%勃起时间)及汉密尔顿抑郁量表(Hamiltondepression scale,HAMD)评分、健康状况调查问卷(36-itemshort-form,SF-36)评分的变化情况。比较两组临床疗效。结果治疗组总有效率为86.5%,明显高于对照组的74.0%(P<0.05)。两组治疗后IIFE-5评分、RigiScan AVSS各项监测数据均明显高于同组治疗前(P<0.05)。治疗组治疗后HAMD评分较同组治疗前显著下降,SF-36评分显著上升,差异均具有统计学意义(P<0.05);对照组治疗后仅SF-36评分较同组治疗前上升(P<0.05)。治疗组治疗后IIFE-5评分、RigiScan AVSS各项监测数据及SF-36评分均明显高于对照组,差异均有统计学意义(P<0.05)。结论穴位贴敷配合他达拉非是一种治疗勃起功能障碍合并抑郁的有效方法,能改善HAMD评分,提高患者生活质量。展开更多
Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate ...Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.展开更多
文摘目的观察穴位贴敷配合他达拉非治疗勃起功能障碍合并抑郁的临床疗效。方法将102例勃起功能障碍合并抑郁患者随机分为治疗组(52例)和对照组(50例)。治疗组采用穴位贴敷配合口服他达拉非治疗,对照组采用单纯口服他达拉非治疗。观察两组治疗前后国际勃起功能指数(the international index of erectile function-5,IIEF-5)评分、RigiScan AVSS各项监测数据(勃起总时间、根部≥60%勃起时间、头部≥60%勃起时间)及汉密尔顿抑郁量表(Hamiltondepression scale,HAMD)评分、健康状况调查问卷(36-itemshort-form,SF-36)评分的变化情况。比较两组临床疗效。结果治疗组总有效率为86.5%,明显高于对照组的74.0%(P<0.05)。两组治疗后IIFE-5评分、RigiScan AVSS各项监测数据均明显高于同组治疗前(P<0.05)。治疗组治疗后HAMD评分较同组治疗前显著下降,SF-36评分显著上升,差异均具有统计学意义(P<0.05);对照组治疗后仅SF-36评分较同组治疗前上升(P<0.05)。治疗组治疗后IIFE-5评分、RigiScan AVSS各项监测数据及SF-36评分均明显高于对照组,差异均有统计学意义(P<0.05)。结论穴位贴敷配合他达拉非是一种治疗勃起功能障碍合并抑郁的有效方法,能改善HAMD评分,提高患者生活质量。
文摘Nowadays,the internal structure of spacecraft has been increasingly complex.As its“lifeline”,cables require extensive manpower and resources for manual testing,and it is challenging to quickly and accurately locate quality problems and find solutions.To address this problem,a knowledge graph based method is employed to extract multi-source heterogeneous cable knowledge entities.The method utilizes the bidirectional encoder representations from transformers(BERT)network to embed word vectors into the input text,then extracts the contextual features of the input sequence through the bidirectional long short-term memory(BiLSTM)network,and finally inputs them into the conditional random field(CRF)network to predict entity categories.Simultaneously,by using the entities extracted by this model as the data layer,a knowledge graph based method has been constructed.Compared to other traditional extraction methods,the entity extraction method used in this study demonstrates significant improvements in metrics such as precision,recall and an F1 score.Ultimately,employing cable test data from a particular aerospace precision machining company,the study has constructed the knowledge graph based method in the field to achieve visualized queries and the traceability and localization of quality problems.
