目的探讨经皮穴位电刺激(TEAS)对选择性脊神经后根切断术(SPR)患儿苏醒期躁动(EA)的影响。方法选择择期在全麻下行SPR的脑瘫患儿42例,男20例,女22例,年龄6~12岁,BMI 13~24 kg/m^(2),ASAⅠ或Ⅱ级。将患儿随机分为两组:经皮穴位电刺激组(T...目的探讨经皮穴位电刺激(TEAS)对选择性脊神经后根切断术(SPR)患儿苏醒期躁动(EA)的影响。方法选择择期在全麻下行SPR的脑瘫患儿42例,男20例,女22例,年龄6~12岁,BMI 13~24 kg/m^(2),ASAⅠ或Ⅱ级。将患儿随机分为两组:经皮穴位电刺激组(T组)和对照组(C组),每组21例。T组于麻醉诱导前30 min给予TEAS双侧合谷穴及内关穴,持续至手术结束。C组在相同的穴位放置电极片,但不予电刺激。所有患儿均采用全凭静脉麻醉。记录入室时、拔管即刻、拔管后5、15、30 min的HR、MAP。记录术中瑞芬太尼和丙泊酚的用量、手术时间、拔管时间。记录拔管后15 min的Wong-Baker面部疼痛表情(FPS-R)评分和儿童麻醉苏醒期躁动评估量表(PAED)评分,并计算苏醒期躁动(EA)发生率。记录术后恶心呕吐(PONV)发生情况。结果与入室时比较,C组拔管即刻、拔管后5、15 min HR明显增快,拔管即刻、拔管后5、15、30 min MAP明显升高(P<0.05);T组拔管即刻、拔管后5、15 min HR明显增快,MAP明显升高(P<0.05)。与C组比较,T组拔管即刻、拔管后5、15、30 min HR明显减慢,MAP明显降低(P<0.05);术中瑞芬太尼用量明显减少,拔管时间明显缩短,术后FPS-R评分、PAED评分和EA发生率明显降低(P<0.05)。两组手术时间、术中丙泊酚用量和PONV发生率差异无统计学意义。结论TEAS可有效预防行SPR的脑瘫患儿EA发生,有利于维持血流动力学平稳,减少阿片类药物用量,减轻患儿术后疼痛,加快麻醉复苏时间。展开更多
Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus d...Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models.The emerging concept of broad learning shows the potential to address the label noise problem.Compared with existing deep learning algorithms,broad learning has a simple architecture and high training efficiency.An active label denoising algorithm based on broad learning(ALDBL)is proposed.First,ALDBL captures the embedded representation from the time-frequency features by a recurrent memory cell.Second,it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space.A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation matrix.Finally,ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo labels.The performance of ALDBL is validated with three benchmark datasets,including 30 label denoising tasks.Computational results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.展开更多
文摘目的探讨经皮穴位电刺激(TEAS)对选择性脊神经后根切断术(SPR)患儿苏醒期躁动(EA)的影响。方法选择择期在全麻下行SPR的脑瘫患儿42例,男20例,女22例,年龄6~12岁,BMI 13~24 kg/m^(2),ASAⅠ或Ⅱ级。将患儿随机分为两组:经皮穴位电刺激组(T组)和对照组(C组),每组21例。T组于麻醉诱导前30 min给予TEAS双侧合谷穴及内关穴,持续至手术结束。C组在相同的穴位放置电极片,但不予电刺激。所有患儿均采用全凭静脉麻醉。记录入室时、拔管即刻、拔管后5、15、30 min的HR、MAP。记录术中瑞芬太尼和丙泊酚的用量、手术时间、拔管时间。记录拔管后15 min的Wong-Baker面部疼痛表情(FPS-R)评分和儿童麻醉苏醒期躁动评估量表(PAED)评分,并计算苏醒期躁动(EA)发生率。记录术后恶心呕吐(PONV)发生情况。结果与入室时比较,C组拔管即刻、拔管后5、15 min HR明显增快,拔管即刻、拔管后5、15、30 min MAP明显升高(P<0.05);T组拔管即刻、拔管后5、15 min HR明显增快,MAP明显升高(P<0.05)。与C组比较,T组拔管即刻、拔管后5、15、30 min HR明显减慢,MAP明显降低(P<0.05);术中瑞芬太尼用量明显减少,拔管时间明显缩短,术后FPS-R评分、PAED评分和EA发生率明显降低(P<0.05)。两组手术时间、术中丙泊酚用量和PONV发生率差异无统计学意义。结论TEAS可有效预防行SPR的脑瘫患儿EA发生,有利于维持血流动力学平稳,减少阿片类药物用量,减轻患儿术后疼痛,加快麻醉复苏时间。
基金supported by the China Scholarship Council during a research visit of Guokai Liu to the University of Iowa(Grant No.201906160078)the Fundamental Research Funds for the Central Universities(Grant No.HUST:2021GCRC058)。
文摘Deep learning has led to tremendous success in machine maintenance and fault diagnosis.However,this success is predicated on the correctly annotated datasets.Labels in large industrial datasets can be noisy and thus degrade the performance of fault diagnosis models.The emerging concept of broad learning shows the potential to address the label noise problem.Compared with existing deep learning algorithms,broad learning has a simple architecture and high training efficiency.An active label denoising algorithm based on broad learning(ALDBL)is proposed.First,ALDBL captures the embedded representation from the time-frequency features by a recurrent memory cell.Second,it augments wide features with a sparse autoencoder and projects the sparse features into an orthogonal space.A proposed corrector then iteratively changes the weights of source examples during the training and corrects the labels by using a label adaptation matrix.Finally,ALDBL finetunes the model parameters with actively sampled target data with reliable pseudo labels.The performance of ALDBL is validated with three benchmark datasets,including 30 label denoising tasks.Computational results demonstrate the effectiveness and advantages of the proposed algorithm over the other label denoising algorithms.
