Dynamic contrast-enhanced computed tomography (CECT) has been used previously to evaluate severe acute pancreatitis (SAP)-associated complications. However, optimal time points of CECT have not yet been established. The present study aimed to determine optimal timings for CECT to be undertaken for patients with SAP. The results of CECT from 309 patients with SAP, who were classified into either infected or non-infected SAP groups, were retrospectively analyzed. The severity and alterations in the periods within 72 h to >4 weeks of SAP onset were also assessed. In the analysis of the disease severity and changes, acute peripancreatic fluid collection was detected, where the number of areas increased within 1 week of SAP onset but decreased within 4 weeks and longer. However, no significant differences were observed between the infected and non-infected groups. The acute necrotic collection (ANC) areas were ≤30% of the area of the pancreas, with significantly more ANC areas and pancreatic necrosis in the infected SAP group compared with the non-infected SAP group at a time interval of >4 weeks. The exudation of pleural effusion (PE) was elevated within 1 week, but decreased within 2 weeks and longer. The difference in the alteration of the exudation of PE was not statistically different between the two groups. In conclusion, the results suggest that the period between 72 h and 1 week of SAP onset is optimal timing of CECT to assess SAP-associated complications, particularly for infected SAP patients.

Hepatocellular carcinoma (HCC) belongs to the most frequent cancer with a high death rate worldwide. Thousands of long non-coding RNAs (lncRNAs) have been confirmed to influence the development of human cancers, including HCC. Nevertheless, the biological role of PRR34 antisense RNA 1 (PRR34-AS1) in HCC remains obscure. Here, we observed via quantitative real-time reverse transcriptase polymerase chain reaction (quantitative real-time RT-PCR) that PRR34-AS1 was highly expressed in HCC cells. Functional assays revealed that PRR34-AS1 promoted HCC cell proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) process in vitro and facilitated tumor growth in vivo . In addition, western blot analysis and TOP Flash/FOP Flash reporter assays verified that PRR34-AS1 stimulated Wnt/β-catenin pathway in HCC cells. Furthermore, RNA immunoprecipitation (RIP), RNA pull-down, and luciferase reporter assays uncovered that PRR34-AS1 sequestered microRNA-296-5p (miR-296-5p) to positively modulate E2F transcription factor 2 (E2F2) and SRY-box transcription factor 12 (SOX12) in HCC cells. Importantly, chromatin immunoprecipitation (ChIP) and luciferase reporter assays uncovered that E2F2 transcriptionally activated PRR34-AS1 in turn. Further, rescue experiments reflected that PRR34-AS1 affected HCC progression through targeting miR-296-5p/E2F2/SOX12/Wnt/β-catenin axis. Our findings found that PRR34-AS1 elicited oncogenic functions in HCC, which indicated that PRR34-AS1 might be a novel therapeutic target for HCC.

CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs.