Previous studies have determined that activated hepatic stellate cells (aHSCs) promote the progression of hepatocellular carcinoma (HCC) by increasing angiogenesis in cancerous tissues. In addition, angiopoietin 1 (Ang‑1) has been reported to be involved in tumor growth and metastasis via the promotion of angiogenesis. It remains unclear whether aHSCs and Ang‑1 are involved in the angiogenesis in HCC. A total of 25 HCC and tumor‑adjacent tissues, and 21 normal liver tissues were used in the present study. Immunohistochemistry (IHC) was used to detect the expression of Ang‑1 and α smooth muscle actin (α‑SMA). The expression of CD34 was also analyzed using IHC to evaluate the microvessel density (MVD). The protein expression levels of Ang‑1 were evaluated using western blot analysis. The association between aHSC, Ang‑1 and angiogenesis was determined using Spearman's rank correlation coefficient. The present study determined that the expression of α‑SMA, Ang‑1 and MVD (CD34) was significantly higher in the HCC tissues when compared with tumor‑adjacent tissues and normal liver tissues. Spearman's rank analysis identified a positive correlation between the expression of α‑SMA, Ang‑1 and CD34. This suggests that α‑SMA‑positive aHSCs promoted angiogenesis by expressing Ang‑1, resulting in the proliferation and metastasis of HCC.

Autism spectrum disorder (ASD) has been defined as a pervasive neurodevelopmental disorder, involving communication, social interaction and repetitive behaviors. Currently, it is still challenging to understand the differences of brain activity between ASD and healthy children. In this study, we propose calculating the Rényi entropy of the eigenvalues derived from the signal correlation matrix to measure the global synchronization in multichannel electroencephalograph (EEG) from 16 children with ASD (aged 8-12 years) and 16 age-and sex-matched healthy controls at the resting state. The results indicate that there is a significantly diminished global synchronization from ASD to healthy control. The proposed method can help to reveal the intrinsic characteristics of multichannel EEG signals in children with ASD and aspects that distinguish them from healthy children.

In order to solve the problems of easy blockage and difficult maintenance of the current coal dust concentration sensor, a coal dust concentration sensor based on the electrostatic induction method was designed. Based on the analysis of the principle of electrostatic induction dust concentration detection, an electrostatic induction dust concentration sensor composed of a electrostatic detection electrode, a dust extraction fan, an induction signal processing circuit, an insulator, a shield, and other parts was designed. The influence of the length and width of the electrostatic detection electrode and the particle flow rate on the standard deviation of the induction signal was analyzed through experiments to optimize them. The induction signals on the electrostatic detection electrode at different dust concentrations were determined in tests, and the mathematical relationship between the standard deviation of the induction signal and the dust concentration was obtained. According to segment multiple-curve height fitting, the maximum deviation between the detected value and actual dust concentration does not exceed 10%.