Colorectal cancer (CRC) is a common clinical malignancy globally ranked as the fourth leading cause of cancer mortality. Some microbes are known to contribute to adenomacarcinoma transition and possess diagnostic potential. Advances in high-throughput sequencing technology and functional studies have provided significant insights into the landscape of the gut microbiome and the fundamental roles of its components in carcinogenesis. Integration of scattered knowledge is highly beneficial for future progress. In this study, literature review and information extraction were performed, with the aim of integrating the available data resources and facilitating comparative research. A knowledgebase of the human CRC microbiome was compiled to facilitate understanding of diagnosis, and the global signatures of CRC microbes, sample types, algorithms, differential microorganisms and various panels of markers plus their diagnostic performance were evaluated based on statistical and phylogenetic analyses. Additionally, prospects about current changelings and solution strategies were outlined for identifying future research directions. This type of data integration strategy presents an effective platform for inquiry and comparison of relevant information, providing a tool for further study about CRC-related microbes and exploration of factors promoting clinical transformation (available at: http://gsbios.com/index/experimental/dts_ mben?id=1).

Background Long intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Here, we aimed to study the ceRNA network of lincRNA in glioblastoma (GBM). Methods We obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. The ceRNA network was further screened by the DEmiRNA and mRNA of Cox model. Results A prognostic prediction model was constructed for patients with GBM. We assembled a ceRNA network consisting of 18 lincRNAs, 6 miRNAs, and 8 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. Conclusion We identified four lincRNAs that have research value for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.

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The automated peritoneal dialysis is accepted increasingly. The automated peritoneal dialysis makes use of a device called automated peritoneal dialysis cycler to realize the automated treatment. The liquid quantity of dialysis treatment is an important index of peritoneal dialysis. Thus the flow calculation accuracy of the automated peritoneal dialysis cycler is one of the most important performances. However, due to the complex usage situations, the cyclers based on different principles are extremely affected by touching, flow resistance fluctuating, etc., which causes inaccuracy and instability. This paper investigated an accurate calculation model and a control algorithm to address these issues. Based on the State Equation of Ideal Gas, a calculation model is established. By analyzing the influence factors of time-varying flow resistance, a control algorithm is designed. A prototype with our method is developed and tested by experiments. The results show that the flow calculation errors are reduced significantly and the accuracy and stability are improved obviously. It proves that our method can realize an accurate flow calculating and effectively reduce errors and keep accuracy stable.

BackgroundLong intergenic non-coding RNAs (lincRNAs) are capable of regulating several tumours, while competitive endogenous RNA (ceRNA) networks are of great significance in revealing the biological mechanism of tumours. Currently, there is a dearth of studies on the ceRNA network of lincRNAs in glioblastoma (GBM), which we aimed to assess in the present study. MethodsWe obtained GBM and normal brain tissue samples from TCGA, GTEx, and GEO databases, and performed weighted gene co-expression network analysis and differential expression analysis on all lincRNA and mRNA data. Subsequently, we predicted the interaction between lincRNAs, miRNAs, and target mRNAs. Univariate and multivariate Cox regression analyses were performed on the mRNAs using CGGA data, and a Cox proportional hazards regression model was constructed. ResultsAccording to the Cox model, we assembled a ceRNA network consisting of 23 lincRNAs, 14 miRNAs, and 13 mRNAs. Gene Set Enrichment Analysis was carried out on four lincRNAs with obvious differential expressions and relatively few studies in GBM. ConclusionWe identified four lincRNAs that have the most research values for GBM and obtained the ceRNA network. Our research is expected to facilitate in-depth understanding and study of the molecular mechanism of GBM, and provide new insights into targeted therapy and prognosis of the tumour.