Microbiological evidences for gastric cardiac microflora dysbiosis inducing the progression of inflammation.

BACKGROUND AND AIM: Nowadays, anti-inflammation treatment is a promising approach for preventing tumorigenesis, and human microflora is closely related to inflammation. This study aimed to investigate the gastric cardiac microbiome and identify inflammation-related microorganisms for gastric cardiac inflammation. METHODS: We performed 16S rRNA sequencing on a total of 11 healthy individuals and 89 individuals with different degree of gastric cardiac inflammation. Immunohistochemistry was used for verifying candidate bacteria. Phylogenetic reconstruction of unobserved states (picrust) was used for predicting the pathways involved by cardiac microflora. RESULTS: The resident phyla in normal were Proteobacteria, Firmicutes, Bacteroides, and Actinobacteria, and the dominant genus in normal were Halomonas, shewanella, and Comamonas. In the progression of gastric cardiac inflammation, the diversity of cardiac microflora did not change (P > 0.05). However, the composition structure of cardiac microflora varied between healthy and inflamed tissues (P < 0.05). Meanwhile, there were 64 species parallel increased with inflammation degree, especially Helicobacter pylori, Lactobacillus spp. Additionally, inflammation-related species were detected (P < 0.05), including H. pylori, Acinetobacter ursingii, and Streptococcus agalactiae. Higher H. pylori colonization was positively related to the progression of cardiac inflammation (γ coefficient = 0.678, P < 0.001), and it also influenced the cardiac microbial community structure. Cardiac microflora also participated in DNA repair pathways and is affected by the relative abundance of H. pylori (P < 0.0001). CONCLUSIONS: Cardiac microflora dysbiosis, especially the increasing of the relevant abundance of H. pylori, promotes the progression of cardiac inflammation.

Occupancy of rooms in urban residential buildings by users in cold areas of China.

Occupancy is used to represent the movements and locations of users among various zones of buildings, and it is the basis of all other daily energy consumption behaviors. This study investigated eight families in cold areas of China based on occupancy measurements obtained in four main rooms, i.e., living room, bedroom, kitchen, and bathroom. In particular, we analyzed the duration of user occupancy and hourly mean occupancy, and characterized their regular and random features. According to the results, we developed an event-based occupancy model using an inhomogeneous Markov chain, where the rooms were modeled and daily events were divided into three categories according to their randomness. We established a new method for conversion between event characteristic parameters and a transition probability matrix, as well as an overlap avoidance method for active events. The model was then validated using real data. The results showed that the model performed well in terms of two evaluation criteria. The model should improve the accuracy of simulations of occupancy.