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Background: Epstein-Barr virus (EBV) is widely infected in humans and causes various diseases. Among them, microRNAs of EBV play a key role in the progression of EBV-associated febrile diseases. There're few specific indicators for rapid differential diagnosis of various febrile diseases associated with EBV, and the lack of more reliable screening methods with high diagnostic utility has led to spaces for improvement in the accurate diagnosis and efficient treatment of relevant patients, making EBV infection a complicated clinical problem. With recent advances in plasma microRNA testing, the apparent presence of EBV microRNAs in plasma can help screen for EBV infection. The gene networks targeted by these microRNAs can also indicate potential biomarkers of EBV-associated febrile diseases. This study aimed to identify some novel miRNAs as potential biomarkers for early diagnosis of respectively EBV-associated febrile diseases. Materials and methods: A total of 110 participants were recruited for this task. First, we performed high-throughput sequencing and preliminary PCR validation of differentially expressed miRNAs in 15 participants with EBV-associated fever (divided into 5 groups, common EBV carriers, infectious mononucleosis (IM) and chronic active EBV infection (CAEBV), EBV-associated Hemophagocytic Lymphohistiocytosis (EBV-HLH), and 3 healthy individuals). After a comprehensive analysis, 10 miRNAs with abnormal expression were screened, and then qRT-PCR was performed in the rest of 95 participants to detect the validation of miRNAs expression in plasma samples. Thereafter, we further investigated their potential for clinical application in EBV-related febrile diseases by using a combination of Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and Protein-protein interaction network analysis. Results: Through identification and detailed analysis of the obtained data, we found significant differences in the expression of Hsa-miR-320d, EBV-miR-BART22, and EBV-miR-BART2-3p in blood samples from patients with different EBV-related febrile diseases. We found that the expression levels of Hsa-miR-320d, EBV-miR-BART22, and EBV-miR-BART2-3p in plasma are indicative of determining different disease types of EBV-related febrile diseases, while EBV-miR-BART22 and EBV-miR-BART2-3p may be potential therapeutic targets. Conclusion: The expression levels of Hsa-miR-320d, EBV-miR-BART22, and EBV-miR-BART2-3p suggest that they may be used as transcriptional features for early differential diagnosis of EBV-related febrile diseases, and EBV-miR-BART22 and EBV-miR-BART2-3p may be potential therapeutic targets.

BackgroundLiver cancer is a leading cause of cancer death worldwide, and novel prognostic factor is needed for early detection and therapeutic responsiveness monitoring. The orphan nuclear receptor NR0B2 was reported to suppress liver cancer development in a mouse model, and its expression levels were reduced in liver cancer tissues and cell lines due to hypermethylation within its promoter region. However, it is not clear if NR0B2 expression is associated with cancer survival or disease progression and how NR0B2 gene expression is regulated at the molecular level.MethodsMultiple cancer databases were utilized to explore NR0B2 gene expression profiles crossing a variety of human cancers, including liver cancers, on several publicly assessable bioinformatics platforms. NR0B2 gene expression with or without kinase inhibitor treatment was analyzed using the qPCR technique, and NR0B2 protein expression was assessed in western blot assays. Two human hepatocellular carcinoma cell lines HepG2 and Huh7, were used in these experiments. NR0B2 gene activation was evaluated using NR0B2 promoter-driven luciferase reporter assays.ResultsNR0B2 gene is predominantly expressed in liver tissue crossing human major organs or tissues, but it is significantly downregulated in liver cancers. NR0B2 expression is mostly downregulated in most common cancers but also upregulated in a few intestinal cancers. NR0B2 gene expression significantly correlated with patient overall survival status in multiple human malignancies, including lung, kidney, breast, urinary bladder, thyroid, colon, and head-neck cancers, as well as liposarcoma and B-cell lymphoma. In liver cancer patients, higher NR0B2 expression is associated with favorite relapse-free and progression-free survival, especially in Asian male patients with viral infection history. In addition, NR0B2 expression negatively correlated with immune infiltration and PIK3CA and PIK3CG gene expression in liver cancer tissues. In HepG2 and Huh7 cells, NR0B2 expression at the transcription level was drastically reduced after MAPK inhibition but was significantly enhanced after PI3K inhibition.ConclusionNR0B2 gene expression is altered mainly in most human malignancies and significantly reduced in liver cancers. NR0B2 is a prognosis factor for patient survival in liver cancers. MAPK and PI3K oppositely modulate NR0B2 expression, and NR0B2 gene upregulation might serve as a therapeutic responsiveness factor in anti-PI3K therapy for liver cancer.

Nuclear power plants produce a massive amount of clean energy and necessitate safe operation through intelligence technologies. Recently, the rapid advancements in communication infrastructures including artificial intelligence, big data computing, and Internet of Things devices moving the nuclear industries towards digitalization and intelligence to improve safety. The integration of these technologies into the nuclear sector offers effective tactics in addressing several challenges in the control and safe operation of nuclear power plants. This can be achieved through the insights generated from massive amounts of data. This paper comprehensively reviews the literature on artificial intelligence technologies and big data, seeking to provide a holistic perspective on their relations and how they can be integrated with nuclear power plants. The utilization of computing platforms boosts the deployment of artificial intelligence and big data analytics effectively in nuclear power plants. Further, this review also points out the future opportunities as well as challenges for applying artificial intelligence and big data computing in the nuclear industry.