MicroRNAs (miRNA) play an important role in tumorigenesis, proliferation, and differentiation. Altered miRNA expression in cancer indicates that miRNAs can function as tumor suppressors or oncogenes. MiR-449c downregulation in non-small cell lung cancer (NSCLC) compared with normal lung tissues was investigated in this study. NSCLC cell proliferation and invasion assays indicate that transfection of miR-449c expression plasmid inhibits the proliferation and invasion ability of NCI-H23 and NCI-H838 cells. In addition, miR-449c overexpression could suppress tumor growth in vivo. Morever, c-Myc was identified as a direct target gene of miR-449c. These findings clearly suggest that miR-449c downregulation and c-Myc amplification may be involved in the development of NSCLC.

Recent studies have shown that Beclin 1, a key regulator of autophagic process, is frequently downregulated and may serve as an independent prognostic biomarker for nonsmall cell lung cancer. However, the molecular mechanisms underlying its downregulation remain poorly understood. The signal transducer and activator of transcription 3 (Stat3) is a transcription factor which plays a crucial role for multiple tumor growth and progression. Here, we demonstrate that Beclin 1 is a direct transcriptional target of Stat3 in lung cancer cells. Interleukin-6 (IL-6) treatment or transfection of a constitutively activated Stat3 in AGS and NCI-H1650 cells inhibited Beclin 1 expression. At the molecular level, we further revealed that Stat3 could directly bind to the promoter region of Beclin 1 and repressed its transcription through recruiting histone deacetylase 3 (HDAC3). Collectively, our results suggest that the activated Stat3 may represent an important mechanism for Beclin 1 downregulation in nonsmall cell lung cancer development.

Development of luminescent coordination polymers (LCPs) for effective detection of the environmental pollutants has been urgent and important for human health and environment protection. Three novel Zn-LCPs, {[Zn1(L)(4,4′-bbibp)]-[Zn2(L)(4,4′-bbibp)]}n [YMUN 6...

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.