The transcriptional repressor Blimp-1 regulates terminal differentiation of B-lymphocytes and myeloid cells. We now show that Blimp-1 is also expressed in human and murine primary T lymphocytes. Blimp-1 expression is highest in freshly isolated primary T cells with an antigen experienced phenotype. Th2 and CD4 + CD25 + cells exhibited higher levels of Blimp-1 mRNA than Th1 cells. However, ectopic expression of Blimp-1 by retroviral transduction neither altered the frequency of IFN-c or IL-4 producing cells nor did it induce suppressor activity. In non-polarized cells, retroviral transduction of Blimp-1 led to a marked reduction in IL-2 secretion, to an inability to proliferate and to reduced viability. Our data suggest that Blimp-1 is physiologically expressed in T lymphocytes during late stages of differentiation, induces down regulation of IL-2 production and a shortened life span and might thus contribute to a limitation of T cell immune responses.

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Heterogeneous nuclear ribonucleoprotein K (hnRNP K) is an RNA/DNA special binding protein that participates in regulating the expression of related genes, transcription, RNA alternative splicing, translation, posttranslational modification, cell signal transduction, cell movement, interacts with ncRNAs, and induces angiogenesis. Moreover, several cellular functions forcefully indicated that hnRNP K participates in tumorigenesis. Numerous studies indicated hnRNP K is aberrantly elevated in multiple tumors. In addition, hnRNP K abnormal accumulation in cytoplasmic is also associated with poor prognosis. This suggests that hnRNP K may play a role in the development and progression of tumors. However, related studies demonstrated that hnRNP K acts as a tumor suppressor to suppress tumor formation. Therefore, this paper aims to explore the role of hnRNPK in tumors.

Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.

NOSH-Aspirin, which is generated from NO, H2S, and aspirin, affects a variety of essential pathophysiological processes, including anti-inflammatory, analgesic, antipyretic, antiplatelet, and anticancer properties. Although many people acknowledge the biological significance of NOSH-Aspirin and its therapeutic effects, the mechanism of action of NOSH-Aspirin and its regulation of tissue levels remains obscure. This is in part due to its chemical and physical features, which make processing and analysis difficult. This review focuses on the biological effects of NOSH-Aspirin and provides a comprehensive analysis to elucidate the mechanism underlying its disease-protective benefits.