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Abstract As an emerging surgical technology, tissue removal systems have been widely used in the treatment of endometrial polyps due to its characteristics of less endometrial damage, shorter learning curve and clearer vision of the operative field. There are few cases in the literature reporting serious complications after endometrial polypectomy using tissue removal systems. As known, septic shock is a rare complication following hysteroscopic polypectomy. Now, we present the case of a 23-year-old woman who developed septic shock after polypectomy with tissue removal system. The patient had a history of recurrent vaginitis for more than half a year. Due to endometrial polyps, she was admitted to our hospital and scheduled to undergo hysteroscopic endometrial polypectomy. Three hours after the endometrial polypectomy using the tissue removal system, the patient had shock symptoms such as increased body temperature, decreased blood pressure and increased heart rate. Then, the patient was successfully treated and discharged after anti-infection and anti-shock treatments. The purpose of this case report is to remind clinicians to consider the possibility of serious infection and comprehensively evaluate the risk of infection before choosing hysteroscopic devices for endometrial polyps, especially for patients who choose the mechanical hysteroscopic tissue removal systems. Furthermore, the mechanical hysteroscopic tissue removal systems should be used with caution in patients with previous recurrent vaginitis.

A growing number of studies have demonstrated that N6 methyladenine (m6A) acts as an important role in the pathogenesis of reproductive diseases. Therefore, it is essential to profile the genome-wide m6A modifications such as in spontaneous abortion. In this study, due to the trace of human villi during early pregnancy, we performed high-throughput sequencing in villous tissues from spontaneous abortion (SA group) and controls with induced abortion (normal group) in the first trimester. Based on meRIP-seq data, 18,568 m6A peaks were identified. These m6A peaks were mainly located in the coding region near the stop codon and were mainly characterized by AUGGAC and UGGACG motif. Compared with normal group, the SA group had 2,159 significantly upregulated m6A peaks and 281 downregulated m6A peaks. Biological function analyses revealed that differential m6A-modified genes were mainly involved in the Hippo and Wnt signaling pathways. Based on the conjoint analysis of meRIP-seq and RNA-seq data, we identified thirty-five genes with differentially methylated m6A peaks and synchronously differential expression. And these genes were mainly involved in the Wnt signaling pathway, phosphatase activity regulation, protein phosphatase inhibitor activity, and transcription inhibitor activity. This study is the first to profile the transcriptome-wide m6A methylome in spontaneous abortion during early pregnancy, which provide novel insights into the pathogenesis and treatment of spontaneous abortion in the first trimester.

Diabetic cardiomyopathy (DCM) is one of the most essential cardiovascular complications in diabetic patients associated with glucose and lipid metabolism disorder, fibrosis, oxidative stress, and inflammation in cardiomyocytes. Despite increasing research on the molecular pathogenesis of DCM, it is still unclear whether metabolic pathways and alterations are probably involved in the development of DCM. This study aims to characterize the metabolites of DCM and to identify the relationship between metabolites and their biological processes or biological states through untargeted metabolic profiling. UPLC-MS/MS was applied to profile plasma metabolites from 78 patients with diabetes (39 diabetes with DCM and 39 diabetes without DCM as controls). A total of 2,806 biochemical were detected. Compared to those of DM patients, 78 differential metabolites in the positive-ion mode were identified in DCM patients, including 33 up-regulated and 45 down-regulated metabolites; however, there were only six differential metabolites identified in the negative mode including four up-regulated and two down-regulated metabolites. Alterations of several serum metabolites, including lipids and lipid-like molecules, organic acids and derivatives, organic oxygen compounds, benzenoids, phenylpropanoids and polyketides, and organoheterocyclic compounds, were associated with the development of DCM. KEGG enrichment analysis showed that there were three signaling pathways (metabolic pathways, porphyrin, chlorophyll metabolism, and lysine degradation) that were changed in both negative- and positive-ion modes. Our results demonstrated that differential metabolites and lipids have specific effects on DCM. These results expanded our understanding of the metabolic characteristics of DCM and may provide a clue in the future investigation of reducing the incidence of DCM. Furthermore, the metabolites identified here may provide clues for clinical management and the development of effective drugs.

Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.

The herbaceous peony (Paeonia lactiflora Pall.) is a well-known ornamental flowering and pharmaceutical plant found in China. Its high medicinal value has long been recognized by traditional Chinese medicine (as Radix paeoniae Alba and Radix paeoniae Rubra), and it has become economically valued for its oilseed in recent years; like other Paeonia species, it has been identified as a novel resource for the α-linolenic acid used in seed oil production. However, its genome has not yet been sequenced, and little transcriptome data on Paeonia lactiflora are available. To obtain a comprehensive transcriptome for Paeonia lactiflora, RNAs from 10 tissues of the Paeonia lactiflora Pall. cv Shaoyou17C were used for de novo assembly, and 416,062 unigenes were obtained. Using a homology search, it was found that 236,222 (approximately 57%) unigenes had at least one BLAST hit in one or more public data resources. The construction of co-expression networks is a feasible means for improving unigene annotation. Using in-house transcriptome data, we obtained a co-expression network covering 95.13% of the unigenes. Then we integrated co-expression network analyses and lipid-related pathway genes to study lipid metabolism in Paeonia lactiflora cultivars. Finally, we constructed the online database HpeNet (http://bioinformatics.cau.edu.cn/HpeNet) to integrate transcriptome data, gene information, the co-expression network, and so forth. The database can also be searched for gene details, gene functions, orthologous matches, and other data. Our online database may help the research community identify functional genes and perform research on Paeonia lactiflora more conveniently. We hope that de novo transcriptome assembly, combined with co-expression networks, can provide a feasible means to predict the gene function of species that do not have a reference genome.

Rattan is regarded as one of the major non-timber forest products, second only to wood and bamboo, worldwide. Although the published genomes of Calamus simplicifolius and Daemonorops jenkinsiana have facilitated genome-wide gene functional analyses, coexpression networks (CENs) provide more comprehensive and complete annotations of gene function at the transcriptome level. Thus, we analyzed the CENs of the two rattans, C. simplicifolius and D. jenkinsiana, by integrating the genome sequences and analyzing in-house transcriptome data from different development stages of their cirri using a well-developed strategy. A total of 3,504 and 3,027 functional modules were identified in C. simplicifolius and D. jenkinsiana, respectively, based on a combination of CENs, gene family classification, and function enrichment tools. These modules covered the major developmental processes, including photosynthesis, lignin biosynthesis, flavonoid biosynthesis, and phenylpropanoid biosynthesis. Reference annotations were refined using CENs and functional modules. Moreover, we obtained novel insights into the regulation of cirrus growth and development in rattans. Furthermore, Rattan-NET (http://rattan.bamboogdb.org/), an online database with analysis tools for gene set enrichment analysis, module enrichment, network comparison analysis, and cis-element analysis, was constructed for the easy analysis of gene function and regulation modules involved in the growth and development of cirri in rattans.

As an economically important crop, apple is one of the most cultivated fruit trees in temperate regions worldwide. Recently, a large number of high-quality transcriptomic and epigenomic datasets for apple were made available to the public, which could be helpful in inferring gene regulatory relationships and thus predicting gene function at the genome level. Through integration of the available apple genomic, transcriptomic, and epigenomic datasets, we constructed co-expression networks, identified functional modules, and predicted chromatin states. A total of 112 RNA-seq datasets were integrated to construct a global network and a conditional network (tissue-preferential network). Furthermore, a total of 1,076 functional modules with closely related gene sets were identified to assess the modularity of biological networks and further subjected to functional enrichment analysis. The results showed that the function of many modules was related to development, secondary metabolism, hormone response, and transcriptional regulation. Transcriptional regulation is closely related to epigenetic marks on chromatin. A total of 20 epigenomic datasets, which included ChIP-seq, DNase-seq, and DNA methylation analysis datasets, were integrated and used to classify chromatin states. Based on the ChromHMM algorithm, the genome was divided into 620,122 fragments, which were classified into 24 states according to the combination of epigenetic marks and enriched-feature regions. Finally, through the collaborative analysis of different omics datasets, the online database AppleMDO (http://bioinformatics.cau.edu.cn/AppleMDO/) was established for cross-referencing and the exploration of possible novel functions of apple genes. In addition, gene annotation information and functional support toolkits were also provided. Our database might be convenient for researchers to develop insights into the function of genes related to important agronomic traits and might serve as a reference for other fruit trees.

Catharanthus roseus is a medicinal plant, which can produce monoterpene indole alkaloid (MIA) metabolites with biological activity and is rich in vinblastine and vincristine. With release of the scaffolded genome sequence of C. roseus, it is necessary to annotate gene functions on the whole-genome level. Recently, 53 RNA-seq datasets are available in public with different tissues (flower, root, leaf, seedling, and shoot) and different treatments (MeJA, PnWB infection and yeast elicitor). We used in-house data process pipeline with the combination of PCC and MR algorithms to construct a co-expression network exploring multi-dimensional gene expression (global, tissue preferential, and treat response) through multi-layered approaches. In the meanwhile, we added miRNA-target pairs, predicted PPI pairs into the network and provided several tools such as gene set enrichment analysis, functional module enrichment analysis, and motif analysis for functional prediction of the co-expression genes. Finally, we have constructed an online croFGD database (http://bioinformatics.cau.edu.cn/croFGD/). We hope croFGD can help the communities to study the C. roseus functional genomics and make novel discoveries about key genes involved in some important biological processes.

The formation of a covalent bond between two stacked cytosines, one of which is excited by an ultrafast laser pulse, was studied by semiclassical dynamics simulations. The results show that a bonded excimer is created, which sharply lowers the energy gap between the LUMO and HOMO and consequently facilitates the deactivation of the electronically excited molecule. This is different from the case of two stacked adenines, where the formation of a covalent bond alters the nonadiabatic deactivation mechanism in two opposite ways. It lowers the energy gap and consequently leads to the coupling between the HOMO and LUMO levels, thus enhancing the deactivation of the electronically excited molecule. On the other hand, it leads to restriction of the deformation vibration of the pyrimidine in the excited molecule, because of a steric effect, and this delays the deactivation process of the excited adenine molecule with return to the electronic ground state.

As the location-based service (LBS) plays an increasingly important role in real life, the topic of positioning attracts more and more attention. Under different environments and principles, researchers have proposed a series of positioning schemes and implemented many positioning systems. With widely deployed networks and massive devices, wireless fidelity (Wi-Fi) technology is promising in the field of indoor positioning. In this paper, we survey the authoritative or latest positioning schemes for Wi-Fi-assisted indoor positioning. To this end, we describe the problem and corresponding applications, as well as an overview of the alternative methods. Then, we classify and analyze Wi-Fi-assisted indoor positioning schemes in detail, as well as review related work. Furthermore, we point out open challenges and forecast promising directions for future work.

Asherman's syndrome (AS), a leading cause of uterine infertility worldwide, is characterized by scarring of the uterine surfaces lacking endometrial epithelial cells, which prevents endometrial regeneration. Current research on cell therapy for AS focuses on mesenchymal and adult stem cells from the endometrium. However, insufficient number, lack of purity, and rapid senescence of endometrial epithelial progenitor cells (EEPCs) during experimental processes restrict their use in cell therapies. In this study, we induced human embryonic stem cells-9 (H9-ESC) into EEPCs by optimizing the induction factors from the definitive endoderm. EEPCs, which act as endometrial epithelial cells, accompanied by human endometrial stromal cells provide a niche environment for the development of endometrial membrane organoids (EMOs) in an in vitro 3D culture model. To investigate the function of EMOs, we transplanted tissue-engineered constructs with EMOs into an in vivo rat AS model. The implantation of EMOs into the damaged endometrium facilitates endometrial regeneration and angiogenesis. Implanting EMOs developed from human embryonic stem cells into the endometrium might prove useful for “endometrial re-engineering” in the treatment of Asherman's syndrome.

To investigate the association between MLH3 Pro844Leu, Thr942Ile polymorphisms and potential linkage with the risk of cervical carcinoma and potential effect on protein function, we carried out a case-control study with 400 cervical squamous cell carcinoma, 400 CIN3 and 1200 normal controls in a Chinese population. The results showed that there was an increased risk of cervical carcinoma and CIN3 associated with the genotype 844CT [OR 2.17 (1.61-2.94); P