Minor protopanaxadiol type sapogenins from the alkali hydrolysate of stems-leaves of Panax notoginseng.

Two new protopanaxadiol type sapogenins, (3ß,12ß)-3,12,20-trihydroxydammar-24-en-26-al (1) and (3ß,12ß)-3,12,20-trihydroxydammar-24-en-26-oic acid (2), were isolated from the alkali hydrolysate of stems-leaves of Panax notoginseng, along with seven known analogues (3-9). Their structures were elucidated by spectroscopic analyses and single-crystal X-ray diffraction. Compound 2 and the known sapogenins 5-8 displayed weak to moderate inhibition of NO production in LPS-induced RAW264.7 macrophages with IC50 values from 44.5 to 143.6 µM, respectively.

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave.

OBJECTIVE: In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). METHOD: The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. RESULT: Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. CONCLUSION: Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.

Tongue Image Database Construction Based on the Expert Opinions: Assessment for Individual Agreement and Methods for Expert Selection.

This study aims at introducing a method for individual agreement evaluation to identify the discordant raters from the experts' group. We exclude those experts and decide the best experts selection method, so as to improve the reliability of the constructed tongue image database based on experts' opinions. Fifty experienced experts from the TCM diagnostic field all over China were invited to give ratings for 300 randomly selected tongue images. Gwet's AC1 (first-order agreement coefficient) was used to calculate the interrater and intrarater agreement. The optimization of the interrater agreement and the disagreement score were put forward to evaluate the external consistency for individual expert. The proposed method could successfully optimize the interrater agreement. By comparing three experts' selection methods, the interrater agreement was, respectively, increased from 0.53 [0.32-0.75] for original one to 0.64 [0.39-0.80] using method A (inclusion of experts whose intrarater agreement>0.6), 0.69 [0.63-0.81] using method B (inclusion of experts whose disagreement score="0"), and 0.76 [0.67-0.83] using method C (inclusion of experts whose intrarater agreement>0.6& disagreement score="0"). In this study, we provide an estimate of external consistency for individual expert, and the comprehensive consideration of both the internal consistency and the external consistency for each expert would be superior to either one in the tongue image construction based on expert opinions.