Effects of zhongfeng cutong moxibustion on motor function and corticospinal tract in the patients with motor dysfunction during the recovery period of cerebral infarction / 中国针灸

OBJECTIVES@#To observe the effects of zhongfeng cutong moxibustion (moxibustion therapy for unblocking and treating stroke) on the motor function and the structure of corticospinal tract (CST) in the patients with motor dysfunction during the recovery period of cerebral infarction, and to explore the central mechanism of this moxibustion therapy for improving the motor function.@*METHODS@#Fifty patients with motor dysfunction during the recovery period of cerebral infarction were randomly divided into an observation group (25 cases, 1 case dropped out) and a control group (25 cases, 1 case dropped out). The patients in both groups underwent the conventional basic treatment. In the control group, acupuncture was applied to Baihui (GV 20) and Shuigou (GV 26), as well as Chize (LU 5), Neiguan (PC 6), Weizhong (BL 40) and Sanyinjiao (SP 6) etc. on the affected side. Besides the intervention of the control group, in the observation group, zhongfeng cutong moxibustion therapy was combined at Baihui (GV 20), Shenque (CV 8) and bilateral Zusanli (ST 36). Both acupuncture and moxibustion therapies were delivered once daily, 5 times a week, for 2 weeks. The scores of Fugl-Meyer assessment scale (FMA) and National Institutes of Health stroke scale (NIHSS) were compared between the two groups before and after treatment. The diffusion tensor imaging technique was used to observe the fractional anisotropy (FA) of CST at the bilateral whole segment, the cerebral cortex, the posterior limb of the internal capsule and the cerebral peduncle before and after treatment in the two groups.@*RESULTS@#The scores of the upper and the lower limbs of FMA, as well as the total FMA score swere increased after treatment when compared with those before treatment in the two groups (P<0.05), the upper limb FMA score and the total FMA score in the observation group were higher than those in the control group (P<0.05), and NIHSS scores of the two groups were dropped compared with those before treatment (P<0.01). FA of CST at the bilateral sides of the posterior limb of the internal capsule and the whole segment on the focal side was improved in comparison with that before treatment in the observation group (P<0.05), and FA of CST at the healthy side of the whole segment was higher than that before treatment in the control group (P<0.05).@*CONCLUSIONS@#Zhongfeng cutong moxibustion improves motor function and reduces neurological deficits in the patients with motor dysfunction during the recovery period of cerebral infarction, which may be related to enhancing the remodeling of white matter fiber bundles in the corticospinal tract on the focal side of the whole segment and the bilateral posterior limb of the internal capsule.

ZHANG Jian-bin's clinical experience in treatment of depressive disorder based on the theory of governor vessel / 中国针灸

Depressive disorder is manifested as emotional and physical abnormality. Theoretically, the governor vessel is distributed along the spine, related to the brain and communicated with five zang and six fu organs. It is the key meridian for understanding the various symptoms of depressive disorder. Depressive disorder is caused by dysfunction, stagnation or emptiness of the governor vessel, resulting in malnutrition of the brain. In clinical diagnosis and treatment, based on the theory of the governor vessel, the etiology and pathogenesis are analyzed in the patients with depressive disorder. In order to achieve harmonizing mutually the mental and physical conditions, acupuncture is delivered to adjust the spirit and physical state, moving cupping is to regulate the governor vessel, tuina manipulation is to promote meridians and collaterals and physical exercise is to coordinate the body and the spirit.

Performance comparison between Logistic regression, decision trees, and multilayer perceptron in predicting peripheral neuropathy in type 2 diabetes mellitus / 中华医学杂志(英文版)

<p><b>BACKGROUND</b>Various methods can be applied to build predictive models for the clinical data with binary outcome variable. This research aims to explore the process of constructing common predictive models, Logistic regression (LR), decision tree (DT) and multilayer perceptron (MLP), as well as focus on specific details when applying the methods mentioned above: what preconditions should be satisfied, how to set parameters of the model, how to screen variables and build accuracy models quickly and efficiently, and how to assess the generalization ability (that is, prediction performance) reliably by Monte Carlo method in the case of small sample size.</p><p><b>METHODS</b>All the 274 patients (include 137 type 2 diabetes mellitus with diabetic peripheral neuropathy and 137 type 2 diabetes mellitus without diabetic peripheral neuropathy) from the Metabolic Disease Hospital in Tianjin participated in the study. There were 30 variables such as sex, age, glycosylated hemoglobin, etc. On account of small sample size, the classification and regression tree (CART) with the chi-squared automatic interaction detector tree (CHAID) were combined by means of the 100 times 5-7 fold stratified cross-validation to build DT. The MLP was constructed by Schwarz Bayes Criterion to choose the number of hidden layers and hidden layer units, alone with levenberg-marquardt (L-M) optimization algorithm, weight decay and preliminary training method. Subsequently, LR was applied by the best subset method with the Akaike Information Criterion (AIC) to make the best used of information and avoid overfitting. Eventually, a 10 to 100 times 3-10 fold stratified cross-validation method was used to compare the generalization ability of DT, MLP and LR in view of the areas under the receiver operating characteristic (ROC) curves (AUC).</p><p><b>RESULTS</b>The AUC of DT, MLP and LR were 0.8863, 0.8536 and 0.8802, respectively. As the larger the AUC of a specific prediction model is, the higher diagnostic ability presents, MLP performed optimally, and then followed by LR and DT in terms of 10-100 times 2-10 fold stratified cross-validation in our study. Neural network model is a preferred option for the data. However, the best subset of multiple LR would be a better choice in view of efficiency and accuracy.</p><p><b>CONCLUSION</b>When dealing with data from small size sample, multiple independent variables and a dichotomous outcome variable, more strategies and statistical techniques (such as AIC criteria, L-M optimization algorithm, the best subset, etc.) should be considered to build a forecast model and some available methods (such as cross-validation, AUC, etc.) could be used for evaluation.</p>