Objective. To investigate the gut microbiota differences of obese children compared with the control healthy cohort to result in further understanding of the mechanism of obesity development. Methods. We evaluated the 16S rRNA gene, the enterotypes, and quantity of the gut microbiota among obese children and the control cohort and learned the differences of the gut microbiota during the process of weight reduction in obese children. Results. In the present study, we learned that the gut microbiota composition was significantly different between obese children and the healthy cohort. Next we found that functional changes, including the phosphotransferase system, ATP-binding cassette transporters, flagellar assembly, and bacterial chemotaxis were overrepresented, while glycan biosynthesis and metabolism were underrepresented in case samples. Moreover, we learned that the amount of Bifidobacterium and Lactobacillus increased among the obese children during the process of weight reduction. Conclusion. Our results might enrich the research between gut microbiota and obesity and further provide a clinical basis for therapy for obesity. We recommend that Bifidobacterium and Lactobacillus might be used as indicators of healthy conditions among obese children, as well as a kind of prebiotic and probiotic supplement in the diet to be an auxiliary treatment for obesity.

Background: Imbalances in gut microbiota composition are linked to hypertension, host metabolic abnormalities, systemic inflammation, and other conditions. In the present study, we examined the changes of gut microbiota in women with early-onset preeclampsia (PE) and in normotensive, uncomplicated pregnant women during late pregnancy and at 1 and 6 weeks postpartum. Methods: Gut microbiota profiles of women with PE and healthy pregnant women in the third trimester and at 1 and 6 weeks postpartum were assessed by 16S rRNA gene amplicon sequencing. Plasma levels of interleukin-6 (IL-6), intestinal fatty acid-binding protein (I-FABP), zonulin, and lipopolysaccharide (LPS) were measured in the third trimesters. Results: At the genus level, 8 bacterial genera were significantly enriched in the antepartum samples of PE patients compared to healthy controls, of which Blautia, Ruminococcus2, Bilophila, and Fusobacterium represented the major variances in PE microbiomes. Conversely, 5 genera, including Faecalibacterium, Gemmiger, Akkermansia, Dialister, and Methanobrevibacter, were significantly depleted in antepartum PE samples. Maternal blood pressure and liver enzyme levels were positively correlated to the PE-enriched genera such as Anaerococcus, Ruminococcus2, Oribacterium, and Bilophila, while the fetal features (e.g., Apgar score and newborn birth weight) were positively correlated with PE-depleted genera and negatively correlated with PE-enriched genera. Moreover, maternal blood IL-6 level was positively associated with gut Bilophila and Oribacterium, whereas LPS level was negatively associated with Akkermansia. In terms of postpartum women, both the gut microbial composition and the PE-associated microbial alterations were highly consistent with those of the antepartum women. Conclusion: PE diagnosed in the third trimester of pregnancy is associated with a disrupted gut microbiota composition compared with uncomplicated pregnant women, which are associated with maternal clinical features (blood pressure level and liver dysfunction) and newborn birth weight. Moreover, these antepartum alterations in gut microbiota persisted 6 weeks postpartum.

Persistent high-risk human papillomavirus (HPV) infection is the pivotal cause of cervical carcinogenesis. HPV types distribution varies greatly by region, and its long-term changes of prevalence remain to be fully characterized in China. Here, the largest population of 198,111 consecutive women who underwent routine cervical screening were investigated from 2015 to 2021 in Guangzhou, south China. The results showed that the overall HPV prevalence was 21.66% (42,911/198,111), and the annual prevalence increased significantly from 2015 to 2021 (p < 0.001). HPV52, 16, 58, CP8304, 51, 53, 39, and 68 were the most prevalent HPV types. The relative HPV-positive rate correlated positively with the progression of cervical intraepithelial neoplasia (p < 0.001); HPV16 was the predominant carcinogenic type, followed by HPV52 and HPV18. HPV infections were significantly age-specific, and 26.51% (11,375/42,911) of cases were caused by multiple HPV types. In addition, HPV infections typically cleared over a median time of 16 (interquartile range 9–31) months, and the clearance of HPV16 was significantly faster than that of other types (p < 0.001). These findings may serve as a guide for local governments to evaluate HPV vaccination and cervical cancer prevention strategies in south China.

Significance: Noninvasive prenatal testing (NIPT) using sequencing of fetal cell-free DNA from maternal plasma has allowed accurate prenatal diagnosis of aneuploidy without requirement of an invasive procedure; this approach has gained increasing clinical acceptance. In this manuscript, we evaluate whether NIPT using semiconductor sequencing platform (SSP) could reliably detect subchromosomal deletions/duplications in women carrying a high-risk fetus. We show that detection of fetal subchromosomal abnormalities is a viable extension of NIPT using SSP. Given the recent strong interest in NIPT using cell-free DNA, these results should be of broad interest and immediate clinical importance, even beyond the NIPT field, such as for detection of cell-free tumor DNA.

Background: The identification of cell-free fetal DNA (cffDNA) facilitated non-invasive prenatal screening (NIPS) through analysis of cffDNA in maternal plasma. However, challenges regarding its clinical implementation become apparent. Factors affecting fetal fraction should be clarified to guide its clinical application. Results: A total of 13,661 pregnant subjects with singleton pregnancies who undertook NIPS were included in the study. Relationship of gestational age, maternal BMI, and maternal age with the cffDNA fetal fraction in maternal plasmas for NIPS was investigated. Compared with 13 weeks (12.74%) and 14–18 weeks group (12.73%), the fetal fraction in gestational ages of 19–23 weeks, 24–28 weeks, and more than 29 weeks groups significantly increased to 13.11%, 16.14%, and 21.17%, respectively (P < 0.01). Compared with fetal fraction of 14.54% in the maternal BMI group of < 18.5 kg/m², the percentage of fetal fraction in the group of 18.5–24.9 kg/m² (13.37%), 25–29.9 kg/m² (12.20%), 30–34.9 kg/m² (11.32%), and 35–39.9 kg/m² (11.57%) decreased significantly (P < 0.01). Compared with the fetal fraction of 14.38% in the group of 18–24 years old, the fetal fraction in the maternal age group of 25–29 years old group (13.98%) (P < 0.05), 30–34 years old group (13.18%) (P < 0.01), 35–39 years old group (12.34%) (P < 0.01), and ≥ 40 years old (11.90%) group (P < 0.01) decreased significantly. Conclusions: The percentage of fetal fraction significantly increased with increase of gestational age. Decreased fetal fraction with increasing maternal BMI was found. Maternal age was also negatively related to the fetal fraction.

Method: This paper focuses on chromosome classification because it is critical for chromosome karyotyping. In recent years, deep learning-based methods are the most promising methods for solving the tasks of chromosome classification. Although the deep learning-based Inception architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge, it has not been used in chromosome classification tasks so far. Therefore, we develop an automatic chromosome classification approach named CIR-Net based on Inception-ResNet which is an optimized version of Inception. However, the classification performance of origin Inception-ResNet on the insufficient chromosome dataset still has a lot of capacity for improvement. Further, we propose a simple but effective augmentation method called CDA for improving the performance of CIR-Net. Results: The experimental results show that our proposed method achieves 95.98% classification accuracy on the clinical G-band chromosome dataset whose training dataset is insufficient. Moreover, the proposed augmentation method CDA improves more than 8.5% (from 87.46% to 95.9%) classification accuracy compared to other methods. In this paper, the experimental results demonstrate that our proposed method is recent the most effective solution for solving clinical chromosome classification problems in chromosome auto-karyotyping on the condition of the insufficient training dataset. Code and Dataset are available at https://github.com/CloudDataLab/CIR-Net.