Weitao Ye

The woman's gut microbiota during pregnancy may support nutrient acquisition, is associated with diseases, and has been linked to infant health. However, there is limited information on gut microbial characteristics and dependence in pregnant women. In this study, we provide a comprehensive overview of the gut microbial characteristics of 1479 pregnant women using 16S rRNA gene sequencing of fecal samples. We identify a core microbiota of pregnant women, which displays a similar overall structure to that of age-matched nonpregnant women. Our data show that the gestational age-associated variation in the gut microbiota, from the ninth week of gestation to antepartum, is relatively limited. Building upon rich metadata, we reveal a set of exogenous and intrinsic host factors that are highly correlated with the variation in gut microbial community composition and function. These microbiota covariates are concentrated in basic host properties (e.g., age and residency status) and blood clinical parameters, suggesting that individual heterogeneity is the major force shaping the gut microbiome during pregnancy. Moreover, we identify microbial and functional markers that are associated with age, pre-pregnancy body mass index, residency status, and pre-pregnancy and gestational diseases. The gut microbiota during pregnancy is also different between women with high or low gestational weight gain. Our study demonstrates the structure, gestational age-associated variation, and associations with host factors of the gut microbiota during pregnancy and strengthens the understanding of microbe-host interactions. The results from this study offer new materials and prospects for gut microbiome research in clinical and diagnostic fields.

Chronic disease, mental health symptoms and poor social relations are reported common causes for poor self-rated health in older people. To assess the co-occurrence rate of chronic diseases, poor mental health and poor social relationships in older people, and determine their association with self-rated health. 6,551 older people in Zhongshan, China, participated a large health surveillance program were randomly selected and questioned about their SRH, chronic conditions, mental health symptoms and social relationships. The association between self-rated health and chronic conditions, poor mental health, social relationships, and their co-occurrence were analyzed. 56.4% of participants reported poor self-rated health. 39.1% experienced at least one chronic disease. 29.0% experienced one or more mental health symptoms; 19.5% experienced at least one poor social relationship. 7.8% had co-occurrence of chronic diseases, mental health problems, and poor social relationships. Logistic regressions showed that poor self-rated health was associated with chronic diseases, poor mental health, poor social relationships and their co-occurrence. The findings indicate the importance of managing chronic disease, poor mental health and poor social relationships for older people.

Background: Lymph node (LN) metastasis is the most important prognostic factor in esophageal squamous cell carcinoma (ESCC). Traditional clinical factor and existing methods based on CT images are insufficiently effective in diagnosing LN metastasis. A more efficient method to predict LN status based on CT image is needed. Methods: In this muticentre retrospective study, 411 patients with pathologically confirmed ESCC were registered from two hospitals. Quantitative image features including handcrafted-, computer vision-(CV-) and deep-features were extracted from preoperative arterial phase CT images for each patient. A handcrafted-, CV- and deep-radiomics signature were built respectively. Then, multiple radiomics models were constructed by merging independent clinical risk factor into radiomics signatures. The performance of models were evaluated with respect to the discrimination, calibration, and clinical usefulness. Finally, an independent external validation cohort was used to validate the model's predictive performance. Results: 5, 7 and 9 features were selected for building handcrafted-, CV- and deep-radiomics signatures from extracted features, respectively. Those signatures were statistically significant different between LN-positive and LN-negative patients in all cohorts (p < 0.001). The developed multiple level CT radiomics model that integrates multiple radiomics signatures with clinical risk factor, was superior to traditional clinical factors and the results reported by existing methods, and achieved satisfactory discrimination performance with C-statistic of 0.875 in development cohort, 0.874 in internal validation cohort and 0.840 in independent external validation cohort. Nomogram and decision curve analysis further confirmed our method may serve as an effective tool for clinicians to evaluate the risk of LN metastasis in patients with ESCC and further choose treatment strategy. Conclusions: The proposed multiple level CT radiomics model which integrate multiple level radiomics features into clinical risk factor can be used for preoperative predicting LN metastasis of patients with ESCC.