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The impact of external factors on the human gut microbiota and how gut microbes contribute to human health is an intriguing question. Here, the gut microbiome of 3,224 individuals (496 with serum metabolome) with 109 variables is studied. Multiple analyses reveal that geographic factors explain the greatest variance of the gut microbiome and the similarity of individuals' gut microbiome is negatively correlated with their geographic distance. Main food components are the most important factors that mediate the impact of host habitats on the gut microbiome. Diet and gut microbes collaboratively contribute to the variation of serum metabolites, and correlate to the increase or decrease of certain clinical indexes. Specifically, systolic blood pressure is lowered by vegetable oil through increasing the abundance of Blautia and reducing the serum level of 1‐palmitoyl‐2‐palmitoleoyl‐GPC (16:0/16:1), but it is reduced by fruit intake through increasing the serum level of Blautia improved threonate. Besides, aging‐related clinical indexes are also closely correlated with the variation of gut microbes and serum metabolites. In this study, the linkages of geographic locations, diet, the gut microbiome, serum metabolites, and physiological indexes in a Chinese population are characterized. It is proved again that gut microbes and their metabolites are important media for external factors to affect human health. [ABSTRACT FROM AUTHOR]

Periodontitis development is strongly associated with the succession of the oral microbiome. However, the knowledge about the succession of the oral microbiome in the development of periodontitis remains insufficient. In the present study, an analysis was conducted on the succession of tongue back, the saliva (Sal) microbiome, and gingival crevicular fluid (GCF) from healthy individuals and patients with mild (CPL), moderate (CPM), severe chronic (CPH), and generalized aggressive periodontitis (GAgP). The composition and structure of the oral microbiome gradually changed with the increasing severity of periodontitis, among which GCF showed the highest correlation with periodontitis. With an ecological preference, pathogens in the mouth varied with the development of periodontitis. In healthy and CPL patients, Sal‐derived microorganisms accounted for a large proportion of GCF but exhibited a decrease in patients with CPM, CPH, and GAgP. Permutation and time course sequencing analysis revealed that a variety of microorganisms changed with the severity of periodontitis. A majority of these microorganisms are closely related to clinical periodontal indices. Ecological analysis suggested that the composition of oral microbial communities at different stages of periodontitis is controlled by random processes. The comparison of microbial interaction networks demonstrated that a series of key microorganisms drive oral health to severe periodontitis. In this study, the relationship between the succession process of the oral microbiota and the development of periodontitis was revealed. [ABSTRACT FROM AUTHOR]

In the field of landmark detection based on deep learning, most of the research utilise convolutional neural networks to represent landmarks, and rarely adopt Transformer to represent and encode landmarks. Meanwhile, many works focus on modifying the network structure to improve network performance, and there is little research on the distribution of landmarks. In this article,the authors propose an unsupervised model to extract landmarks of objects in images. First, Transformer structure is combined with the convolutional neural network structure to represent and encode the landmarks; next, positive and negative sample pairs between landmarks are constructed, so that the semantically consistent landmarks on the image are pulled closer in the feature space and the semantically inconsistent landmarks are pushed farther in the feature space; then the authors concentrate their attention on the most active points to distinguish the landmarks of an object from the background; finally, based on the new contrastive loss, the network reconstructs the image by the landmarks of the object that are continuously learnt during training. Experiments show that the proposed model achieves better performance than other unsupervised methods on the CelebA, Annotated Facial Landmarks in the Wild, 300W datasets. [ABSTRACT FROM AUTHOR]

Objective: Progesterone supplementation is recommended to prevent spontaneous preterm birth (sPTB) in clinical practice. However, the exact mechanism is still unclear. This study aims to better understand the mechanisms that progesterone can prevent PTB. Methods: Late pregnant mice were given various doses of progesterone receptor antagonist mifepristone, and pregnancy outcomes were observed. Then, non‐pregnant and pregnant mice were given a subcutaneous injection of 40 mg/kg progesterone and 5 mg/kg mifepristone, respectively. CD4+CD25+FoxP3+ Treg cells in peripheral blood and decidua basalis were detected by FACS. Expressions of FoxP3 and TGF‐β1 in the decidua basalis were detected. Results: Mifepristone induced preterm birth, and an obvious dose‐response was found. Proportions of CD4+CD25+FoxP3+ Treg cells in the peripheral blood of non‐pregnant mice increased significantly after progesterone injection. CD4+CD25+FoxP3+ Treg cells in the peripheral blood of pregnant mice increased significantly compared with those of non‐pregnant mice. In pregnant mice, mifepristone significantly decreased the proportions of CD4+CD25+FoxP3+ Treg cells in peripheral blood, and reduced proportions of Treg cells at the maternal‐fetal interface and expressions of FoxP3 and TGF‐β1 in the maternal‐fetal interface. Total 40 mg/kg of progesterone did not increase CD4+CD25+FoxP3+ Treg in the peripheral blood of pregnant mice, but increased proportions of Treg cells at the maternal‐fetal interface and up‐regulated FoxP3 and TGF‐β1 expressions in the maternal‐fetal interface. Conclusion: Progesterone promotes pregnancy immune homeostasis by up‐regulating Treg cells and TGF‐β1 expression in the maternal‐fetal interface. It may be one of the mechanisms of progesterone in preventing sPTB. [ABSTRACT FROM AUTHOR]

Objective: Progesterone supplementation is recommended to prevent spontaneous preterm birth (sPTB) in clinical practice. However, the exact mechanism is still unclear. This study aims to better understand the mechanisms that progesterone can prevent PTB., Methods: Late pregnant mice were given various doses of progesterone receptor antagonist mifepristone, and pregnancy outcomes were observed. Then, non-pregnant and pregnant mice were given a subcutaneous injection of 40 mg/kg progesterone and 5 mg/kg mifepristone, respectively. CD4 + CD25 + FoxP3 + Treg cells in peripheral blood and decidua basalis were detected by FACS. Expressions of FoxP3 and TGF-β1 in the decidua basalis were detected., Results: Mifepristone induced preterm birth, and an obvious dose-response was found. Proportions of CD4 + CD25 + FoxP3 + Treg cells in the peripheral blood of non-pregnant mice increased significantly after progesterone injection. CD4 + CD25 + FoxP3 + Treg cells in the peripheral blood of pregnant mice increased significantly compared with those of non-pregnant mice. In pregnant mice, mifepristone significantly decreased the proportions of CD4 + CD25 + FoxP3 + Treg cells in peripheral blood, and reduced proportions of Treg cells at the maternal-fetal interface and expressions of FoxP3 and TGF-β1 in the maternal-fetal interface. Total 40 mg/kg of progesterone did not increase CD4 + CD25 + FoxP3 + Treg in the peripheral blood of pregnant mice, but increased proportions of Treg cells at the maternal-fetal interface and up-regulated FoxP3 and TGF-β1 expressions in the maternal-fetal interface., Conclusion: Progesterone promotes pregnancy immune homeostasis by up-regulating Treg cells and TGF-β1 expression in the maternal-fetal interface. It may be one of the mechanisms of progesterone in preventing sPTB., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)