目的观察疏肝健脾合养心安神方治疗功能性消化不良(functional dyspepsia,FD)伴有焦虑抑郁状态的疗效。方法将90例FD伴焦虑抑郁状态的患者随机分为疏肝组、疏肝养心组和疏肝黛力新组,分别治以中药疏肝健脾方、中药疏肝健脾方+养心安神方...目的观察疏肝健脾合养心安神方治疗功能性消化不良(functional dyspepsia,FD)伴有焦虑抑郁状态的疗效。方法将90例FD伴焦虑抑郁状态的患者随机分为疏肝组、疏肝养心组和疏肝黛力新组,分别治以中药疏肝健脾方、中药疏肝健脾方+养心安神方,及中药疏肝健脾方+黛力新,治疗前后观察3组患者脾胃症状评分,采用36条目生活质量简表(36-item short form health survey questionnaire,SF-36)评价患者治疗前后的生活质量,采用汉密尔顿焦虑量表(Hamilton anxiety scale,HAMA)、汉密尔顿抑郁量表(Hamilton depression scale,HAMD)评价患者治疗前后的焦虑抑郁水平。结果疏肝养心组、疏肝黛力新组在改善FD患者脾胃症状,降低HAMA、HAMD评分,改善SF-36各维度评分方面显著优于疏肝组(P<0.05,或P<0.01);疏肝养心组与疏肝黛力新组疗效比较,差异无统计学意义(P>0.05)。结论疏肝健脾方合养心安神方可明显改善FD伴焦虑抑郁状态患者的脾胃症状,并可改善焦虑抑郁症状,提高生活质量。展开更多
The gray-scale ultrasound(US) imaging method is usually used to assess synovitis in rheumatoid arthritis(RA) in clinical practice. This four-grade scoring system depends highly on the sonographer's experience and ...The gray-scale ultrasound(US) imaging method is usually used to assess synovitis in rheumatoid arthritis(RA) in clinical practice. This four-grade scoring system depends highly on the sonographer's experience and has relatively lower validity compared with quantitative indexes. However, the training of a qualified sonographer is expensive and timeconsuming while few studies focused on automatic RA grading methods. The purpose of this study is to propose an automatic RA grading method using deep convolutional neural networks(DCNN) to assist clinical assessment. Gray-scale ultrasound images of finger joints are taken as inputs while the output is the corresponding RA grading results. Firstly,we performed the auto-localization of synovium in the RA image and obtained a high precision in localization. In order to make up for the lack of a large annotated training dataset, we performed data augmentation to increase the number of training samples. Motivated by the approach of transfer learning, we pre-trained the GoogLeNet on ImageNet as a feature extractor and then fine-tuned it on our own dataset. The detection results showed an average precision exceeding 90%. In the experiment of grading RA severity, the four-grade classification accuracy exceeded 90% while the binary classification accuracies exceeded 95%. The results demonstrate that our proposed method achieves performances comparable to RA experts in multi-class classification. The promising results of our proposed DCNN-based RA grading method can have the ability to provide an objective and accurate reference to assist RA diagnosis and the training of sonographers.展开更多
文摘目的观察疏肝健脾合养心安神方治疗功能性消化不良(functional dyspepsia,FD)伴有焦虑抑郁状态的疗效。方法将90例FD伴焦虑抑郁状态的患者随机分为疏肝组、疏肝养心组和疏肝黛力新组,分别治以中药疏肝健脾方、中药疏肝健脾方+养心安神方,及中药疏肝健脾方+黛力新,治疗前后观察3组患者脾胃症状评分,采用36条目生活质量简表(36-item short form health survey questionnaire,SF-36)评价患者治疗前后的生活质量,采用汉密尔顿焦虑量表(Hamilton anxiety scale,HAMA)、汉密尔顿抑郁量表(Hamilton depression scale,HAMD)评价患者治疗前后的焦虑抑郁水平。结果疏肝养心组、疏肝黛力新组在改善FD患者脾胃症状,降低HAMA、HAMD评分,改善SF-36各维度评分方面显著优于疏肝组(P<0.05,或P<0.01);疏肝养心组与疏肝黛力新组疗效比较,差异无统计学意义(P>0.05)。结论疏肝健脾方合养心安神方可明显改善FD伴焦虑抑郁状态患者的脾胃症状,并可改善焦虑抑郁症状,提高生活质量。
基金supported by the National Key Research and Development Program of China(Grant No.2017YFC0111402)the Natural Science Funds of Jiangsu Province of China(Grant No.BK20181256)
文摘The gray-scale ultrasound(US) imaging method is usually used to assess synovitis in rheumatoid arthritis(RA) in clinical practice. This four-grade scoring system depends highly on the sonographer's experience and has relatively lower validity compared with quantitative indexes. However, the training of a qualified sonographer is expensive and timeconsuming while few studies focused on automatic RA grading methods. The purpose of this study is to propose an automatic RA grading method using deep convolutional neural networks(DCNN) to assist clinical assessment. Gray-scale ultrasound images of finger joints are taken as inputs while the output is the corresponding RA grading results. Firstly,we performed the auto-localization of synovium in the RA image and obtained a high precision in localization. In order to make up for the lack of a large annotated training dataset, we performed data augmentation to increase the number of training samples. Motivated by the approach of transfer learning, we pre-trained the GoogLeNet on ImageNet as a feature extractor and then fine-tuned it on our own dataset. The detection results showed an average precision exceeding 90%. In the experiment of grading RA severity, the four-grade classification accuracy exceeded 90% while the binary classification accuracies exceeded 95%. The results demonstrate that our proposed method achieves performances comparable to RA experts in multi-class classification. The promising results of our proposed DCNN-based RA grading method can have the ability to provide an objective and accurate reference to assist RA diagnosis and the training of sonographers.
