Curcumin is a major yellow pigment and active component of turmeric widely used as dietary spice and herbal medicine. This compound has been reported to be a promising antitumor agent, although the underlying molecular mechanisms are not fully understood yet. In this study, we reported that curcumin inhibited growth of lung adenocarcinoma cells, but had no cytotoxic activity to IMR-90 normal lung fibroblast cells. Curcumin induced autophagy in the A549 human lung adenocarcinoma cell line, evidenced by LC3 immunofluorescence analysis and immunoblotting assays on LC3 and SQSTM1. Moreover, the autophagy inhibitor 3-MA partly blocked the inhibitory effect of curcumin on the growth of A549 cells. Curcumin markedly increased the phosphorylation of AMP-activated protein kinase (AMPK) and acetylCoA carboxylase in A549 cells. At last, pharmacological blockade of the AMPK signaling pathway by compound C and genetic disruption of the AMPK signaling pathway with siRNA-mediated AMPKα1 knockdown impaired the autophagy-inducing effect of curcumin. Collectively, our data suggests that curcumin induces autophagy via activating the AMPK signaling pathway and the autophagy is important for the inhibiting effect of curcumin in lung adenocarcinoma cells.

Graphical models for probabilistic reasoning are now in widespread use. Many approaches have been developed such as Bayesian network. A newly developed approach named as dynamic uncertain causality graph (DUCG) is initially presented in a previous paper, in which only the inference algorithm in terms of individual events and probabilities is addressed. In this paper, we first explain the statistic basis of DUCG. Then, we extend the algorithm to the form of matrices of events and probabilities. It is revealed that the representation of DUCG can be incomplete and the exact probabilistic inference may still be made. A real application of DUCG for fault diagnoses of a generator system of a nuclear power plant is demonstrated, which involves variables. Most inferences take with a laptop computer. The causal logic between inference result and observations is graphically displayed to users so that they know not only the result, but also why the result obtained.

We performed this meta-analysis to investigate and determine the role of metformin on serum adiponectin levels in Type 2 diabetes (T2DM) patients. Embase, Web of Science, Cochrane Library PubMed, and China National Knowledge Infrastructure (CNKI) were thoroughly searched. Eligible human studies assessing the association between serum adiponectin levels and metformin in patients were included, and data were extracted and then analyzed with STATA 12.0 statistical software. Eighteen cohort studies conducted among Asians and Caucasians from 2004 to 2013 were recruited. Post-treatment serum adiponectin level (mmol/l) was higher than pre-treatment levels in T2DM patients (SMD=0.19, 95% CI=0.09–0.30, p<0.001). Country-subgroup analysis showed that serum adiponectin levels in T2DM patients increased after the treatment of metformin in Italy (SMD=0.34, 95% CI=0.09–0.59, p=0.008). Further detection method and follow-up time subgroup analyses implied a positive association of metformin with serum adiponectin level in T2DM patients by using all ELISA, PETIA, and RIA in both<12 weeks and≥12 weeks subgroups (all p<0.05). The present meta-analysis provides compelling evidence that metformin may increase serum adiponectin levels when treating T2DM. Further studies should be promoted to explore the combined efficacy of metformin with other antidiabetic drugs, or developing new predictors with antidiabetic efficacy.