Objective:To study the characteristics of vaginal microbiota in pregnant women with premature rupture of membranes (PROM) and to establish prediction models for PROM.Methods:This study involved 35 women with preterm premature rupture of membranes (PPROM), 180 with term premature rupture of membranes (TPROM) and 255 term birth cases without premature rupture of membranes (TBWPROM, control group). The V3-V4 hypervariable region sequences in the vaginal samples collected at 16-28 weeks of gestation were detected by 16S rRNA gene next-generation sequencing. The differences in Alpha and Beta diversity, and the attributes and metabolic function prediction of each recognized species among the three groups were analyzed. Subsequently, a random forest model was used to establish the prediction models for PROM using vaginal microbiota species and environmental risk factors.Results:Compared with the control group, the Alpha diversity of the PPROM group was higher (Observed features, P=0.022; Faith_pd index, P=0.024) and Beta diversity was also significantly different (Unweighted UniFrac, P=0.010; Jaccard index, P=0.008). In PPROM cases, Megasphaera genomosp. typeⅠ was significantly increased ( P=0.017) and Lactobacillus mulieris was significantly decreased ( P=0.003). In the patients with TPROM, Megasphaera was significantly increased ( P=0.009) and Lactobacillus mulieris was significantly decreased ( P=0.002). In terms of functional pathways, sulfur oxidation ( P=0.021), methanogenesis from acetate ( P=0.036), L-histidine biosynthesis ( P=0.009), adenosylcobalamin biosynthesis ( P=0.041) and fucose degradation ( P=0.001) were significantly increased in patients with PPROM; L-histidine biosynthesis ( P<0.001) and fucose degradation ( P=0.030) were significantly increased in patients with TPROM. The prediction models were established using the random forest model with vaginal microbiota species and environmental risk factors and the prediction model for PPROM performed well [AUC: 0.739 (95%CI: 0.609-0.869), sensitivity: 0.928, specificity: 0.659, positive predictive value: 0.750, negative predictive value: 0.906], which had a certain reference value. Conclusions:Vaginal microbiota might be related to the development and progression of PROM. Studying the differences in vaginal microbiota might provide a new idea for the prevention and treatment of PROM. Functional prediction provided a direction for further research on the mechanism of PROM. The established prediction model could prevent the occurrence of PPROM and promote maternal and infant health.

Objective:To study the characteristics and influencing factors of vaginal microbiota in normal pregnant women.Methods:This study was based on a cohort of pregnant women established in Anqing Municipal Hospital Affiliated to Anhui Medical University from February 2018 to February 2020. Vaginal samples of normal pregnant women who met the inclusion and exclusion criteria were ordered by the gestational weeks at sampling. Five samples were randomly selected from each gestational week group and if the samples were less than five, all samples were included. Sequencing of the V3-V4 region of the 16S rRNA gene was performed. Dominant species were analyzed by MicrobiomeAnalyst. Alpha diversity was measured with Chao1, Observed Features, Shannon diversity, Simpson diversity, Faith_pd and Pielou′s Evenness. The dominant status of Lactobacillus was also described and compared. Multiple linear regression and logistic regression were used to analyze the factors influencing vaginal microbiota. Analysis of variance and Kruskal Wallis test were used for statistical analysis of continuous variables, and Chi-square test and Fisher′s exact test were used for categorical data. The differences were considered statistically significant when the P value was less than 0.05. Results:This study enrolled 91 pregnant women (91 vaginal samples) with an average age of (27.37±3.60) years. There were 18, 56 and 17 vaginal samples collected at the median gestational age of 11.93 weeks (the first trimester), 19.43 weeks (the second trimester) and 38.29 weeks (the third trimester), respectively. The relative abundance of Firmicutes and Lactobacillus was 91.30% and 87.67%, respectively. Lactobacillus iners and Lactobacillus crispatus had a relative abundance of 43.95% and 36.33%, respectively. Moreover, Lactobacillus iners-dominated vaginal microbiota was detected in all trimesters. The number of samples with high relative abundance of Lactobacillus iners gradually decreased with gestational age. Lactobacillus crispatus-dominated vaginal microbiota was found in the second and third trimesters and the number of samples with high relative abundance gradually increased during pregnancy. The Alpha diversity of vaginal microbiota had a decreasing trend during the gestation. There were significant differences in Pielou′s Evenness diversity index of vaginal microbiota between different smoking groups ( P<0.05) and in Shannon diversity index between different drinking groups ( P<0.05). There were significant differences in Chao1, Observed Features and Faith_pd diversity index of vaginal microbiota between pregnant women with different education ( P<0.05) and in Shannon and Simpson diversity index between different income groups ( P<0.05). Conclusions:Vaginal microbiota was dominated by Lactobacillus in normal pregnant women. The dominance of Lactobacillus iners gradually decreased, while that of Lactobacillus crispatus increased during gestation. In normal pregnant women, the Alpha diversity of vaginal microbiota was correlated with smoking, drinking, education and family annual income. Smoking cessation and drinking before pregnancy were related to lower Alpha diversity of vaginal microbiota in pregnant women, while lower education and higher family income were associated with higher Alpha diversity.

Objective:To explore the role of commensal microbiota in the regulation of long non-coding RNA(LncRNA)expression in mouse alveolar macrophages(AMs).Methods:AMs were separated from antibiotics-treated mice and normal mice and then were purified.LncRNA microarray technology was used to screen differentially expressed LncRNAs and conduct bioinformatics analysis.Fluorescence in situ hybridization(FISH)was used to detect the subcellular localization of LncRNA-30162.RNA interference technology was used to knock out the expression of LncRNA-30162 in RAW264.7 cells, and reverse transcription polymerase chain reation(RT-PCR) was used to detect the regulation of gene expression by LncRNA-30162 in RAW264.7 cells.Results:The purity of the separated AMs was greater than 95%.Compared with normal mice, there were 634 differentially expressed LncRNAs with changes greater than 2 folds in the AMs from antibiotics-treated mice, 363 of which were upregulated and 271 were downregulated.The target genes of differentially expressed LncRNAs were closely associated with immune system regulation, cell differentiation and chemotaxis.The expression levels of CCL24 and Arg1 in RAW264.7 macrophages were decreased after interference with LncRNA-30162 expression[(218.70±31.45) &mu;g/L vs.(420.23±56.25) &mu;g/L, (1.24±0.21)×10 3 U/L vs.(2.63±0.31)×10 3 U/L, t=5.416 and 6.409, P=0.006 and 0.003]. Conclusions:Commensal microbiota can regulate the expression of LncRNAs in AMs.Differentially expressed LncRNAs are associated with a variety of gene ontology(GO)biological processes and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways.LncRNA-30162 can regulate the expression levels of CCL24 and ARG1 in RAW264.7 cells.

Due to the flaws inherited in synchronicity, statistical association and survivor bias on variables under measurement, a common 'consensus’ has been reached on "cross-sectiional studies (CSS) can lead to failure on causal inference". In this paper, under both causal thinking and diagram, the real and measured cross-sections are clearly defined that these two concepts only exist theoretically. In real CSS research, the temporal orders of measured variables are all non-synchronic, equivalent to the assumption that measurement variables are independent to each other, or there is no differentiated classification bias. Similar to cumulative case-control or historical cohort studies, both exposure and outcome that exist or occur before their measurements in cross-sectional studies, are actions of historical reconstruction or doing 'Archaeology’. One of the common preconditions for causal inference in such studies is that: there must be a causal relation between the measured variables and their historical counterparts. The measured variables are all agents of their corresponding real counterparts, and the temporal orders are not that important in causal inference. It is necessary to better understand the analytic role of the CSS.

We evaluated the tolerance and pathogenesis of foodborne pathogens with a simulated gastro-intestinal tract model that simulates the chemical, physical and biological effects of human digestion process under laboratory conditions. This could be used to study the tolerance, pathogenesis, gut microbiota interaction and vaccine development of foodborne pathogens, so as to contribute to control and treatment of foodborne pathogens. This review introduces the applications of simulated gastro-intestinal tract model tp evaluate foodborne pathogens, which includes in-vitro static gastro-intestinal model, in-vitro dynamic gastro-intestinal model, conventional animal model and humanized animal model. And the concepts and characteristics of all models are described in detail. Also, the shortcomings of existing models are analyzed, and improvements of artificial gastro-intestinal tract model are prospected. In conclusion, this review could provide comprehensive data for promoting the progress of studying tolerance and pathogenesis of foodborne pathogens.