Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation.

Traditional Chinese medicine (TCM) has been recognized worldwide as a valuable asset of human medicine. The procedure of TCM is to treatment based on syndrome differentiation. However, the effect of TCM syndrome differentiation relies heavily on the experience of doctors. The gratifying progress of machine learning research in recent years has brought new ideas for TCM syndrome differentiation. In this paper, we propose a deep network model for TCM syndrome differentiation, which improves network performance by injecting TCM syndrome differentiation knowledge in the form of first-order logic into the deep network. Experimental results show that the accuracy of our proposed model reaches 89%, which is significantly better than the deep learning model MLP and other traditional machine learning models. In addition, we present the collected and formatted TCM syndrome differentiation (TSD) dataset, which contains more than 40,000 TCM clinical records. Moreover, 45 symptoms (""), 322 patterns(""), and more than 500 symptoms are labeled in TSD respectively. To the best of our knowledge, this is the first TCM syndrome differentiation dataset labeling diseases, syndromes and pattern. Such detailed labeling is helpful to explore the relationship between various elements of syndrome differentiation.

In vitro simulated digestion of and microbial characteristics in colonic fermentation of polysaccharides from four varieties of Tibetan tea.

Polysaccharides as a functional prebiotic have numerous activities such as regulating intestinal microorganisms and polysaccharide is one of the functional active components in tea has been known. In this study, we aimed to investigate the physicochemical characteristics of polysaccharides from four kinds of Tibetan teas at simulated digestion stages and the effect on the microbiota of fecal fermentation stages in vitro. The results revealed that Tibetan tea polysaccharides were partially digested during digestion. Additionally, during in vitro fecal microbial fermentation, Tibetan tea polysaccharides can promote the growth of some beneficial bacteria such as Bifidobacterium, Prevotella and Phascolarctobacterium to change the composition of intestinal microorganisms and promote the production of short-chain fatty acids (SCFAs). Finally, a strong correlation was found between the production of SCFAs and microorganisms including Bacteroides, Bifidobacterium and Lachnoclostridium. These results suggest that Tibetan tea polysaccharides could be developed as a prebiotic to regulate human gut microbiota.