Background:It aimed to observe the effect of electro-acupuncture on the improvement of psychiatric symptoms, as well as anxiety and depression in methamphetamine (MA) addicts during abstinence using randomized controlled trials.Methods:All patients in the present study received compulsory drug detoxification in Shanghai Drug Rehabilitation Center. All patients were enrolled consecutively from June 2014 to February 2015; data collection was completed in March 2015. According to the randomized, single-blind and control principle, 68 men MA addicts were randomly divided into 2 groups: electro-acupuncture (EA) and sham electro-acupuncture (sham-EA) groups. Patients were given 20 minutes EA or sham-EA treatment every Monday, Wednesday, and Friday, with a total of 4 weeks. Positive and Negative Syndrome Scale (PANSS), Hamilton Anxiety Scale (HAMA), and Hamilton Depression Scale (HAMD) were used to evaluate the patients’ psychotic symptoms, anxiety and depression before treatment and after receiving treatment with 1 to 4 weeks, respectively.Results:EA could effectively improve the symptoms of psychosis, anxiety, and depression during abstinence in patients with MA addiction. In terms of PANSS score, the scores for positive symptoms and general psychopathological symptoms in patients after receiving 1 to 4 weeks of treatment were significantly decreased compared with the control group, while the score for negative symptoms was significantly decreased after receiving 2 and 4 weeks of treatment. For the HAMA score, the psychotic anxiety scores in patients receiving 1 to 4 weeks of treatment were significant lower than the control group. In terms of HAMD score, there was a significant reduction in anxiety/somatization and sleep disturbance scores after the 4 weeks of EA treatment.Conclusion:Electroacupuncture helps to improve psychiatric symptoms and anxiety and depression in MA addicts during abstinence, and promote rehabilitation of patients.
ABSTRACT:DEM super resolution is proposed in our previous publication to improve the resolution for a DEM on basis of some learning examples. Meanwhile, the nonlocal algorithm is introduced to deal with it and lots of experiments show that the strategy is feasible. In our publication, the learning examples are defined as the partial original DEM and their related high measurements due to this way can avoid the incompatibility between the data to be processed and the learning examples. To further extent the applications of this new strategy, the learning examples should be diverse and easy to obtain. Yet, it may cause the problem of incompatibility and unrobustness. To overcome it, we intend to investigate a convolutional neural network based method. The input of the convolutional neural network is a low resolution DEM and the output is expected to be its high resolution one. A three layers model will be adopted. The first layer is used to detect some features from the input, the second integrates the detected features to some compressed ones and the final step transforms the compressed features as a new DEM. According to this designed structure, some learning DEMs will be taken to train it. Specifically, the designed network will be optimized by minimizing the error of the output and its expected high resolution DEM. In practical applications, a testing DEM will be input to the convolutional neural network and a super resolution will be obtained. Many experiments show that the CNN based method can obtain better reconstructions than many classic interpolation methods.
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