Global gene expression analysis reveals reduced abundance of putative microRNA targets in human prostate tumours

<p>Abstract</p> <p>Background</p> <p>Recently, microRNAs (miRNAs) have taken centre stage in the field of human molecular oncology. Several studies have shown that miRNA profiling analyses offer new possibilities in cancer classification, diagnosis and prognosis. However, the function of miRNAs that are dysregulated in tumours remains largely a mystery. Global analysis of miRNA-target gene expression has helped illuminate the role of miRNAs in developmental gene expression programs, but such an approach has not been reported in cancer transcriptomics.</p> <p>Results</p> <p>In this study, we globally analysed the expression patterns of miRNA target genes in prostate cancer by using several public microarray datasets. Intriguingly, we found that, in contrast to global mRNA transcript levels, putative miRNA targets showed a reduced abundance in prostate tumours relative to benign prostate tissue. Additionally, the down-regulation of these miRNA targets positively correlated with the number of types of miRNA target-sites in the 3' untranslated regions of these targets. Further investigation revealed that the globally low expression was mainly driven by the targets of 36 specific miRNAs that were reported to be up-regulated in prostate cancer by a miRNA expression profiling study. We also found that the transcript levels of miRNA targets were lower in androgen-independent prostate cancer than in androgen-dependent prostate cancer. Moreover, when the global analysis was extended to four other cancers, significant differences in transcript levels between miRNA targets and total mRNA backgrounds were found.</p> <p>Conclusion</p> <p>Global gene expression analysis, along with further investigation, suggests that miRNA targets have a significantly reduced transcript abundance in prostate cancer, when compared with the combined pool of all mRNAs. The abnormal expression pattern of miRNA targets in human cancer could be a common feature of the human cancer transcriptome. Our study may help to shed new light on the functional roles of miRNAs in cancer transcriptomics.</p

Herb-Drug Interaction: Effects of Relinqing® Granule on the Pharmacokinetics of Ciprofloxacin, Sulfamethoxazole, and Trimethoprim in Rats

Relinqing granule (RLQ) is the best-selling Chinese patent drug for treatment of urinary system diseases. In this study, the effects of RLQ on the pharmacokinetics of ciprofloxacin, sulfamethoxazole, and trimethoprim in SD rats were investigated. Rats were randomly divided into control group 1, control group 2, RLQ group 1, and RLQ group 2. RLQ group 1 and RLQ group 2 were treated orally with RLQ for 7 days, and rats were treated with the same volume of water in control group 1 and control group 2. Then, RLQ group 1 and control group 1 were given intragastrically ciprofloxacin on day 8, while RLQ group 2 and control group 2 were given intragastrically sulfamethoxazole and trimethoprim on day 8. Blood samples were collected and determined. There was no significant influence of pharmacokinetic parameters of trimethoprim on two groups. But some pharmacokinetic parameters of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats were evidently altered (P < 0.05), which indicated that absorption of ciprofloxacin and sulfamethoxazole in RLQ pretreated rats was significantly affected. It indicated the coadministration of RLQ would have an influence on the efficacy of ciprofloxacin and sulfamethoxazole, and the doses of ciprofloxacin tablet and compound sulfamethoxazole tablet need adjustment

A Graph Convolutional Network with Adaptive Graph Generation and Channel Selection for Event Detection

Graph convolutional networks have been successfully applied to the task of event detection. However, existing works rely heavily on a fixed syntactic parse tree structure from an external parser. In addition, the information content extracted for aggregation is determined simply by the (syntactic) edge direction or type but irrespective of what semantics the vertices have, which is somewhat rigid. With this work, we propose a novel graph convolutional method that combines an adaptive graph generation technique and a multi-channel selection strategy. The adaptive graph generation technique enables the gradients to pass through the graph sampling layer by using the ST-Gumbel-Softmax trick. The multi-channel selection strategy allows two adjacent vertices to automatically determine which information channels to get through for information extraction and aggregation. The proposed method achieves the state-of-the-art performance on ACE2005 dataset

Enhancing Forest Fire Risk Assessment: An Ontology-Based Approach with Improved Continuous Apriori Algorithm

Forest fires are sudden and difficult to extinguish, so early risk assessment is crucial. However, there are currently a lack of suitable knowledge-mining algorithms for forest fire risk assessment. This article proposes an improved continuous Apriori algorithm to mining forest fire rules by introducing prior knowledge to classify input data and enhance its ability to process continuous data. Meanwhile, it constructs an ontology to provide a standardized expression platform for forest fire risk assessment. The improved continuous Apriori algorithm cooperates with ontology and applies the mining rules to the forest fire risk assessment results. The proposed method is validated using the forest fire data from the Bejaia region in Algeria. The results show that the improved continuous Apriori algorithm is superior to the raw Apriori algorithm and can mine the rules ignored by the raw Apriori algorithm. Compared to the raw Apriori algorithm, the number of generated rules increased by 191.67%. The method presented here can be used to enhance forest fire risk assessments and contribute to the generation and sharing of forest-fire-related knowledge, thereby alleviating the problem of insufficient knowledge in forest fire risk assessment