Relationships Between Diurnal Changes of Tongue Coating Microbiota and Intestinal Microbiota.

The oral cavity and the intestine are the main distribution locations of human digestive bacteria. Exploring the relationships between the tongue coating and gut microbiota, the influence of the diurnal variations of the tongue coating microbiota on the intestinal microbiota can provide a reference for the development of the disease diagnosis and monitoring, as well as the medication time. In this work, a total of 39 healthy college students were recruited. We collected their tongue coating microbiota which was collected before and after sleep and fecal microbiota. The diurnal variations of tongue coating microbiota are mainly manifested on the changes in diversity and relative abundance. There are commensal bacteria in the tongue coating and intestines, especially Prevotella which has the higher proportion in both sites. The relative abundance of Prevotella in the tongue coating before sleep has a positive correlation with intestinal Prevotella; the r is 0.322 (p < 0.05). Bacteroides in the intestine had the most bacteria associated with the tongue coating and had the highest correlation coefficient with Veillonella in the oral cavity, which was 0.468 (p < 0.01). These results suggest that the abundance of the same flora in the two sites may have a common change trend. The SourceTracker results show that the proportion of intestinal bacteria sourced from tongue coating is less than 1%. It indicates that oral flora is difficult to colonize in the intestine in healthy people. This will provide a reference for the study on the oral and intestinal microbiota in diseases.

Tongue color clustering and visual application based on 2D information.

PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.