Study on quantitative diagnosis model of TCM syndromes of post-stroke depression based on combination of disease and syndrome.

BACKGROUND: Post-stroke depression (PSD) is one of the most common stroke complications with high morbidity. Researchers have done much clinical research on Traditional Chinese Medicine (TCM) treatment, but very little research on diagnosis. Based on the thought of combination of disease and syndrome, this study will establish a unified and objective quantitative diagnosis model of TCM syndromes of PSD, so as to improve the clinical diagnosis and treatment of PSD. OBJECTIVE: First: To establish a unified and objective quantitative diagnosis model of TCM syndromes in PSD under different disease courses, and identify the corresponding main, secondary, and concurrent symptoms, which are based on the weighting factor of each TCM symptom. Second: To find out the relationship between different stages of PSD and TCM syndromes. Clarify the main syndrome types of PSD under different stages of disease. Reveal the evolution and progression mechanism of TCM syndromes of PSD. METHODS AND ANALYSIS: This is a retrospective study of PSD TCM diagnosis. Three hundred patients who were hospitalized in the First Teaching Hospital of Tianjin University of TCM with complete cases from January 2014 to January 2019 are planned to be recruited. The study will mainly collect the diagnostic information from the cases, find the related indicators of TCM and Western medicine in PSD, and clarify the relationship between different disease stages and TCM syndromes. Finally, the PSD TCM syndrome quantitative diagnosis model will be established based on the operation principle of Back Propagation (BP) artificial neural network. CONCLUSION: To collect sufficient medical records and establish models to speed up the process of TCM diagnosis.

[Clinical observation of Alzheimer's disease treated with acupuncture].

OBJECTIVE: To compare the differences in the clinical efficacy on Alzheimer's disease between acupuncture and medicine. METHODS: One hundred and forty-one patients were randomized into an acupuncture group (72 cases) and a medicine group (69 cases). In the acupuncture group, the needling technique for benefiting qi, promoting blood circulation, regulating mind and improving intelligence was used at Shenting (GV 24), Baihui (GV 20), Fengchi (GB 20), Wangu (GB 12), Danzhong (CV 17), Zhangwan (CV 12), Qihai (CV 6), Xuehai (SP 10) and Zusanli (ST 36). The supplementary acupoints were selected according to the symptoms and physical signs. Acupuncture was given once a day and 6 treatments were required for a week. In the medicine group, the choline sterase inhibitor, donepezil (aricept) was prescribed for oral administration, 1 tablet (5 mg) each time, once every night. Four weeks later, the dose was increased to 2 tablets (10 mg) each time. In the two groups, the treatment of 4 weeks made one session and 4 sessions were required. The changes of scores before and after treatment in the minimum mental state examination (MMSE), the activity of daily living scale (ADL), Alzheimer's disease assessment scale-cognition (ADAS-cog) and the digit span (DS) were observed. RESULTS: After treatment, scores of MMSE and DS were increased as compared with those before treatment (both P < 0.05) and scores of ADL and ADAS-cog were reduced as compared with those before treatment. The score differences in MMSE, ADL, ADAS-cog and DS before and after treatment were significant in the two groups (all P < 0.01). CONCLUSION: The needling technique for benefiting qi, promoting blood circulation, regulating mind and improving intelligence significantly improves the overall function, cognition and activity of daily life in the patients of Alzheimer's disease and the efficacy is better than donepezil.

[Application of DPLS-based LDA in corn qualitative near infrared spectroscopy analysis].

NIR technology is a rapid, nondestructive and user-friendly method ideally suited for Qualitative analysis. In this paper the authors present the use of discriminant partial least Squares (DPLS)-based linear discriminant analysis (LDA) in corn qualitative near infrared spectroscopy analysis. Firstly, a training set including 30 corn varieties (each variety has 20 samples) was used to build the DPLS regression model, and 28 principal components (DPLS-PCs) were obtained from original spectrum. Secondly, the DPLS-PCs scores of the training set were extracted as DPLS features. Thirdly, LDA was applied to the DPLS features, determining 26 principal components (LDA-PCs). A test sample was first projected onto the DPLS-PCs and then onto the LDA-PCs, and finally 26 DPLS+LDA features were obtained. The recognition results were obtained by minimum distance classifier. DPLS+LDA method achieved 96.18% recognition rate, while traditional DPLS regression method and DPLS feature extraction method only achieved 85.38% and 95.76% recognition rate respectively. The experiment results indicated that DPLS +LDA method is with better generalization ability compared with traditional DPLS regression method and NIRS analysis by DPLS+LDA method is an efficient way to discriminate corn species.