The way in which a balanced vaginal microbiome helps prevent gynecological diseases in women and maintain health remains to be fully elucidated. In the present study, the potential effect of aberrations in the vaginal flora on unexplained recurrent miscarriage (RM) was investigated. The vaginal bacterial communities of 10 patients with unexplained RM and 10 healthy volunteers were sampled and subjected to sequencing analysis of the V3-V4 regions of the bacterial 16S ribosomal RNA gene using the Illumina MiSeq platform. Beta diversity analysis/principal component analysis indicated that bacterial community structures were different between the RM and control groups. A lower microbiota diversity in samples from RM patients was revealed by alpha diversity estimation. Taxonomic analysis demonstrated that abundance of three types of phyla ( Firmicutes, Actinobacteria and Bacteroidetes ) was significantly different between the RM and the normal control group. Furthermore, at the genus level, Lactobacillus was the most dominant genus in the two groups. Statistically significant differences were observed in 5 genera between the two groups. In the RM group, 3 bacterial taxa ( Atopobium, Prevotella and Streptococcus ) were significantly more abundant, while only 2 taxa were overrepresented in the control group ( Lactobacillus and Gardnerella ). In conclusion, the present results provide experimental evidence supporting dysbiosis of the vaginal flora in women with RM.
Children born to ovarian-hyperstimulated women displayed cardiovascular dysfunctions. The underlying mechanisms may involve the effects of supraphysiological estradiol and progesterone levels.
Aims/IntroductionTo explore the association between lactation and type 2 diabetes incidence in women with prior gestational diabetes.Materials and MethodsWe searched PubMed, Embase and the Cochrane Library for cohort studies published through 12 June 2017 that evaluated the effect of lactation on the development of type 2 diabetes in women with prior gestational diabetes. A random effects model was used to estimate relative risks (RRs) with 95% confidence intervals (CIs).ResultsA total of 13 cohort studies were included in the meta‐analysis. The pooled result suggested that compared with no lactation, lactation was significantly associated with a lower risk of type 2 diabetes (RR 0.66, 95% CI 0.48–0.90, I 2 = 72.8%, P < 0.001). This relationship was prominent in a study carried out in the USA (RR 0.66, 95% CI 0.43–0.99), regardless of study design (prospective design RR 0.56, 95% CI 0.41–0.76; retrospective design RR 0.63, 95% CI 0.40–0.99), smaller sample size (RR 0.52, 95% CI 0.30–0.92, P = 0.024) and follow‐up duration >1 years (RR 0.75, 95% CI 0.56–1.00), and the study used adjusted data (RR 0.69, 95% CI 0.50–0.94). Finally, by pooling data from three studies, we failed to show that compared with no lactation, long‐term lactation (>1 to 3 months postpartum) was associated with the type 2 diabetes risk (RR 0.69, 95% CI 0.41–1.17).ConclusionsThe present meta‐analysis showed that lactation was associated with a lower risk of type 2 diabetes in women with prior gestational diabetes. Furthermore, no significant relationship between long‐term lactation and type 2 diabetes risk was detected. The impact of long‐term lactation and the risk of type 2 diabetes should be verified in further large‐scale studies.
The key to leak detection and location in water supply pipelines is signal denoising and feature extraction. First, in this paper, an improved spline-local mean decomposition (ISLMD) is proposed to eliminate noise interference. Based on the ISLMD decomposition of a signal, the cross-correlation function between the reference signal and the product functions component can be obtained. And then the PF component containing the leak information can be extracted reasonably. Compared with improved local mean decomposition, the ISLMD has higher accuracy in leak location. Second, an image recognition method using a convolutional neural network for leak detection is proposed, which can better address the problem that the features of different leak apertures or locations are highly similar to each other. The images from the conversion of the reconstructed signals are used as the input of the AlexNet model, which is capable of adaptive extraction of leak signal features. The trained AlexNet model can effectively detect different leak apertures. Finally, the signal time-delay between the upstream and downstream pressure transmitters caused by the leak and propagation of negative pressure wave is determined according to generalized crosscorrelation analysis, and thereby, the leak location is obtained. The experimental results show that the proposed method is effective for leak detection and location. INDEX TERMS Local mean decomposition, convolutional neural network, generalized cross-correlation, leak detection and location, fault detection.
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