Objective The purpose of this research is to use modern statistical methods,to analyze the rules of cointment by Prof.Yang Zhimin treating yang deficiency and fatigue state patients,to increase the pertinence and to improve the clinical efficacy.Methods This paper analyzed 62 cases of Prof.Yang Zhimin in the treatment of the patients with yang deficiency and fatigue state from October 2010 to January 2013.The analyses were frequency analysis and association rules.Results Among the patients with yang-deficiency and fatigue,193 Chinese medicine were used.The monkshood was the highest frequency.Dampness-transforming medicine accounts for about 22.2％ in the top 36 frequency of medicine.Based on analyzing association rules,we found that the compatibility of medicine which were used more frequently came from Sini decoction,Danggui Sini decoction and Jinkui Shenqi pills,such as "monkshood and dried ginger","monkshood and roasted liquorice","dried ginger and roasted liquorice","monkshood and angelica","monkshood and comel","monkshood and prepared rehmannia root" and "monkshood and yam".Futhermore,we found two new core prescription.One was Qian Yang Dan combined with Sini decoction consist of "monkshood,roasted liquorice,dried ginger,fructus amomi".The other one was Tu Si Jian consist of "yam,semen cuscutae,angelica,roasted liquorice".Conclusions "Invigorating spleen and kidney","warming yang and dissipating dampness" and "hiding yang and Seeking Yang in Yin" are the habits and rules of the ointment medication by Prof.Yang Zhimin treatingyang deficiency and fatigue state patients.

Objective To investigate the effect of scan table on size-specific dose estimate ( size-specific dose estimate, SSDE) in children's CT scan. Methods CT imaging data and CTDIvol of 44 children ( 15 heads, 13 chests, 16 abdomen-pelvis) who underwent Siemens SOMATOM Definition AS+ 64 row 128-slice CT scan were retrospectively collected. CTDIvol of each patient was recored, WED ( water equivalent diameter) was calculated by two different methods ( with or without table) , donated as WED-T and WED-NT, then the corresponding SSDEWED ( SSDEWED-T and SSDEWED-NT ) was calculated. And the SSDEWED-NT was used as reference to evaluate the difference between WED and SSDEWED obtained by two different methods. Results Including part of table will lead to the overestimate for WED, with mean differences of 0. 10％, 2. 82％ and 2. 54％ for head, chest and abdomen-pelvis, respectively, while SSDEWED will be underestimated by 0. 06％ ( head ) , 2. 70％ ( chest ) and 1. 59％ ( abdomen-pelvis ) . Conclusions Including par of the patient table has a certain effect on SSDEWED for children, more attention should be paid for the application of SSDEWED.

Acquired immune deficiency syndrome is an infectious disease with high mortality caused by human immunodefi-ciency virus(HIV).Even after antiretroviral therapy,the virus reservoir formed by the provirus integrated in the host genome can evade host immune surveillance and clearance.The existing methods of HIV reservoir detection mainly include cell culture induced virus growth assay in vitro and direct detection of the provirus genome based on PCR technology.Understanding and measuring accurately of HIV reservoir can provide reliable technical basis for HIV treatment research.This article summarizes and analyzes various detection methods of HIV reservoirs in recent years and their advantages and disadvantages,in order to select the suitable method for different detection objects of HIV reservior,and give appropriate suggestions,to provide technical support and theoretical guidance for further research on acquired immune deficiency syndrome.

Objective To investigate the correlation between serum miR-320b and carotid atherosclerosis in patients with acute ischemic stroke.Methods From January 2017 to December 2017,patients with acute ischemic stroke visited the Department of Neurology,the Affiliated Hospital of Yangzhou University were enrolled.According to the findings of carotid artery ultrasonography,they were divided into plaque group and plaque-free group.The baseline clinical data such as demographic data,vascular risk factors,and blood biochemical indicators were collected.Reverse transcription quantitative polymerase chain reaction was used to detect the expression level of serum miR-320b.Multivariatelogistic regression analysis was used to determine the independent risk factors for carotid atherosclerosis.Results A total of 135 patients with acute ischemic stroke were enrolled in this study,including 58 females and 77 males,aged 58.4 ± 10.6 years.There were 85 patients in the plaque group and 50 in the plaque-free group.The total cholesterol (t =5.523,P =0.023) and low-density lipoprotein cholesterol (t =4.415,P =0.044) in the plaque group were significantly higher than those in the plaque-free group,while high-density lipoprotein cholesterol (t =5.849,P=0.017) and serum miR-320b (t =4.331,P=0.039) were significantly lower than those in the plaque-free group.Multivariate logistic regression analysis showed that referring to the highest quartile group,the low serum miR-320b level might be an independent risk factor for carotid atherosclerosis (the first quartile group:odds ratio 2.701,95％ confidence interval 1.154-6.321,P =0.022;the second quartile group:odds ratio 2.521,95％ confidence interval 1.249-5.091,P =0.010;and the third quartile group:odds ratio 1.849,95％ confidence interval 1.041-3.283,P=0.036).Conclusion The low serum miR-320b level might be an independent risk factor for carotid atherosclerosis in patients with acute ischemic stroke.

In the process of lower limb rehabilitation training, fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury. However, most of the existing methods only consider surface electromyography (sEMG) features but ignore electrocardiogram (ECG) features when performing in fatigue estimation, which leads to the low and unstable recognition efficiency. Aiming at this problem, a method that uses the fusion features of ECG and sEMG signal to estimate the fatigue during lower limb rehabilitation was proposed, and an improved particle swarm optimization-support vector machine classifier (improved PSO-SVM) was proposed and used to identify the fusion feature vector. Finally, the accurate recognition of the three states of relax, transition and fatigue was achieved, and the recognition rates were 98.5%, 93.5%, and 95.5%, respectively. Comparative experiments showed that the average recognition rate of this method was 4.50% higher than that of sEMG features alone, and 13.66% higher than that of the combined features of ECG and sEMG without feature fusion. It is proved that the feature fusion of ECG and sEMG signals in the process of lower limb rehabilitation training can be used for recognizing fatigue more accurately.
Algorithms ; Electrocardiography ; Electromyography ; Fatigue/diagnosis* ; Humans ; Lower Extremity ; Support Vector Machine

Objective To explore the feasibility of radiomics features combined with different machine learning methods based on CT scans to predict lymphovascular and perineural invasion in patient with gastric cancer.Methods A total of 142 patients with gas-tric cancer lymphovascular confirmed by operative pathological examination were retrospectively selected.Among all patients,there were 96 positive cases and 46 negative cases.Among 137 patients with perineural invasion,there were 76 positive cases and 61 nega-tive cases.The 3D-Slicer package was used for delineation,and the Pyradiomics package was used to extract radiomics features.All data were randomly divided into training set and test set in an 8∶2 ratio.Intraclass correlation coefficient(ICC),Pearson correla-tion analysis,least absolute shrinkage and selection operator(LASSO)algorithm were used for feature selection.Support vector machine(SVM),K-nearest neighbor(KNN),decision tree(DT),random forest(RF),extreme tree(ET),extreme gradient boosting(XGBoost),and LightGBM were used to compare the models of lymphovascular and perineural invasion,respectively.Receiver operating characteris-tic(ROC)curve and area under the curve(AUC)were used to evaluate the predictive performance of these models.Results The lymphovascular group AUC of SVM,KNN,DT,RF,ET,XGBoost,and LightGBM in the training set were 0.926,0.753,1.000,0.999,1.000,1.000,and 0.917,and the AUC in the test set were 0.894,0.692,0.456,0.678,0.753,0.650,and 0.650,respectively.The perineural invasion group AUC of SVM,KNN,DT,RF,ET,XGBoost,and LightGBM in the training set were 0.864,0.794,1.000,1.000,1.000,1.000,and 0.866,and the AUC in the test set were 0.861,0.706,0.700,0.672,0.731,0.667,and 0.678,respectively.Conclusion Based on venous phase CT radiomics features combined with machine learning methods,it is feasible to predict lymphovascu-lar and perineural invasion of gastric cancer preoperatively.Among the variousmachine learning methods,SVM shows the best predictive performance for lymphovascular and perineural invasion in patient with gastric cancer.