No abstract

MicroRNAs (miRNAs) act as important post-transcriptional regulators of gene expression in diverse signalling pathways. However, the relationship between miR-200b and the nuclear factor-jB (NF-jB) signalling pathway remains poorly understood in breast cancer cells. In the current study, we show that IKBKB is a direct target of miR-200b, and that miR-200b downregulates IKBKB expression via directly binding to its 3 0 -UTR. miR200b inhibits IjBa phosphorylation, nuclear p50/p65 expression, NF-jBbinding activity, and the translocation of p65 to the nucleus. In addition, miR-200b also suppresses tumour necrosis factor (TNF)-a-induced NF-jB activation and the expression of NF-jB target genes. Importantly, IKBKB overexpression attenuates the inhibitory roles of miR-200b in NF-jB expression, NF-jB-binding activity, and the nuclear translocation of p65. We also show that NF-jB p65 knockdown reduces the binding of NF-jB to the miR-200b promoter and miR-200b promoter activity. Furthermore, p65 knockdown or inhibition of IjBa phosphorylation suppresses miR-200b expression. Finally, functional studies show that IKBKB overexpression can restore the cell growth and migration that are suppressed by miR-200b. In conclusion, our results demonstrate that miR-200b, a transcriptional target of NF-jB, suppresses breast cancer cell growth and migration, and NF-jB activation, through downregulation of IKBKB, indicating that miR-200b has potential as a therapeutic target in breast cancer patients.

We aimed to find the correlation between serum sPD-L1 (soluble programmed cell death L-1 ligand) and sepsis. Totally 91 consecutive patients with sepsis were performed in a 15-bed medical intensive care unit (ICU) of the second affiliated hospital, Xi'an Jiaotong University in Xi'an, China, between February 2015 and May 2016. Healthy controls (HC) consisted of 29 healthy volunteer. Baseline demographic data were recorded. Blood samples were collected through an indwelling central venous or by peripheral venipuncture. Serum sPD-L1 and sPD-1 levels were determined with enzyme-linked immunosorbent assay kits (Elabscience Biotechnology Co. Ltd, Wuhan, China). SPSS19.0 software (SPSS Inc., Chicago, Illinois, USA) was used for statistical analysis. Kaplan-Meier survival analysis and Cox regression analysis were also performed. Serum sPD-L1 levels and sPD-1 levels were significantly increased in septic patients compared with HC (P = 0.000). Serum sPD-L1 levels were significantly increased in non-survivors compared with survivors (P < 0.05), but there was no statistically difference on serum sPD-1 levels between non-survivors and survivors (P > 0.05). Serum sPD-L1 levels were correlated with absolute lymphocyte (ALC), platelets and SOFA scores. Serum sPD-L1/sPD-1 levels were negatively correlated with ALC and platelets, and SOFA scores. The prognostic accuracy of the sPD-L1 level to predict 28-day mortality was similar to that of the APACHE-II scores and SOFA scores. Cox regression analysis showed that sPD-L1 was an independent prognostic factor. Serum sPD-L1 is upregulated in sepsis and may reflect disease severity and clinical outcomes in patients. Serum sPD-L1 may be an independent prognostic factor for sepsis.

No abstract

Few-shot learning, aiming to learn novel concepts from few labeled examples, is an interesting and very challenging problem with many practical advantages. To accomplish this task, one should concentrate on revealing the accurate relations of the support-query pairs. We propose a transductive relation-propagation graph neural network (TRPN) to explicitly model and propagate such relations across support-query pairs. Our TRPN treats the relation of each support-query pair as a graph node, named relational node, and resorts to the known relations between support samples, including both intra-class commonality and inter-class uniqueness, to guide the relation propagation in the graph, generating the discriminative relation embeddings for support-query pairs. A pseudo relational node is further introduced to propagate the query characteristics, and a fast, yet effective transductive learning strategy is devised to fully exploit the relation information among different queries. To the best of our knowledge, this is the first work that explicitly takes the relations of support-query pairs into consideration in few-shot learning, which might offer a new way to solve the few-shot learning problem. Extensive experiments conducted on several benchmark datasets demonstrate that our method can significantly outperform a variety of state-of-the-art few-shot learning methods.