Persistent dysregulation of genes in the development of endometriosis.

Background: Endometriosis is a chronic condition that affects women of child-bearing age. Since the etiology and pathogenesis of endometriosis have not been fully elucidated, it is important to investigate the mechanisms that lead to the deterioration of endometriosis. Methods: In this study, the transcriptome data of patients with normal, mild, and severe endometriosis were examined using the GSE51981 dataset obtained from the Gene Expression Omnibus database. Short Time Series Expression Miner (STEM) was used to screen the genes with continuous expression disorder in the development process, and the core genes were identified by constructing a protein-protein interaction network. The molecular mechanisms of endometriosis were examined using enrichment analysis. Finally, the transcription factors that regulate the core genes were predicted and the comprehensive mechanisms involved in the development of endometriosis were examined. Results: A total of 3,472 differentially expressed genes were identified from the normal, mild, and severe endometriosis samples. These were allocated into 12 modules and HRAS, HSP90AA1, TGFB1, TP53, and UBC were selected as the core genes. Enrichment analysis showed that the genes in modules 6, 7, and 9 were significantly related to oxygen levels, metallic processes, and hormone levels, respectively. Transcription factor prediction analysis showed that TP53 regulates HRAS to participate in immune related signaling pathways. Drug prediction analysis identified 792 drugs that interact with the targeted core genes. Conclusions: This study explored the molecular mechanisms involved in the development of endometriosis and identified potential biomarkers of endometriosis. This data may provide novel targets and research directions for the diagnosis and treatment of endometriosis.

The feature of cervical microbiota associated with the progression of cervical cancer among reproductive females.

OBJECTIVES: The aim of this study was to characterize cervical microbiome feature of reproductive-age women in the progression of squamous intraepithelial lesions (SIL) to cervical cancer. METHODS: We characterized the 16S rDNA cervical mucus microbiome in 94 participants (age from 18 to 52), including 13 cervical cancer (CA), 31 high-grade SIL (HSIL), 10 low-grade SIL (LSIL), 12 HPV-infected (NH) patients and 28 healthy controls (NN). Alpha (within sample) diversity was examined by Shannon and Simpson index, while Beta (between sample) diversity by principle coordinate analysis (PCoA) of weighted Unifrac distances. Relative abundance of microbial taxa was compared using Linear Discriminant Analysis Effect Size (LEfSe). Co-occurrence analysis was performed to identify correlation among marker genera, and Phylogenetic investigation of communities by reconstruction of unobserved states (PICRUSt) to explore functional features and pathways of cervical microbiota. RESULTS: Alpha diversity(p < 0.05) was higher in severer cervical pathology with lower relative abundance of Lactobacillus as well as higher of anaerobes. Beta diversity (p < 0.01) was significantly different. Marker genera were identified including Porphyromonas, Prevotella and Campylobacter of CA and Sneathia of HSIL. The correlation of differential functional pathways with Prevotella was opposite to that with Lactobacillus. CONCLUSION: Our study suggests differences in cervical microbiota diversity and relative abundance of reproductive-age females in different stages of cervical carcinogenesis. Marker genera might participate in the lesion progression and will be helpful for diagnosis, prevention and treatment. These findings may lead the way to further study of the cervical microbiome in development of cervical cancer.

Effective Cycle Slip Detection and Repair for PPP/INS Integrated Systems.

Cycle slip (CS) is a primary error source in Precise Point Positioning/Inertial Navigation System (PPP/INS) integrated systems. In this study, an INS-aided CS detection and repair method is presented. It utilizes high-precision INS information instead of a pseudorange to remove the satellite⁻receiver geometric range in the wide-lane (WL) and ionospheric-free (IF) phase combinations and creates an INS-aided WL (WL-INS) model and an INS-aided IF (IF-INS) model. Since INS information is superior to pseudorange, the INS-aided models have high detection accuracy. However, the effectiveness of INS-aided models cannot persist for a long time because of INS accumulation error. To overcome the disturbance of INS error, improved INS-aided models are proposed. This idea takes advantage of the long wavelength of WL combination and tries to fix WL CS. Once it succeeds, the INS error can be evaluated and removed. The proposed method was tested using land vehicle data, in which simulated cycle slips and signal interruption were introduced. The results show that this method can accurately detect and repair different cycle slips between the continuous Global Positioning System (GPS) epoch. When it comes to the cycle slip after a GPS interruption, the method can also accelerate PPP re-convergence, as it is not affected by the inertial accumulation error.