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Recently, the incidence of NAFLD has exploded globally, but there are currently no officially approved medications for treating the condition. The regulation of NAFLD through plant-derived active substances has become a new area of interest. Quinoa (Chenopodium quinoa Willd.) has been discovered to contain a large quantity of bioactive compounds. In this study, we established a free fatty acid (FFA)-induced steatosis model and explored the effects of quinoa polyphenol extract (QPE) on the major hallmarks of NAFLD. The results indicated that QPE significantly reduced intracellular triglyceride (TG) and total cholesterol (TC) levels. Additionally, QPE remarkably elevated the levels of superoxide dismutase (SOD), catalase (CAT) and glutathione (GSH) and lowered levels of malondialdehyde (MDA). Further examination revealed that QPE attenuated intracellular inflammation, which was verified by the reduced levels of pro-inflammatory cytokines. Mechanistically, QPE inhibited fatty acid biosynthesis mainly by targeting de novo lipogenesis (DNL) via the AMPK/SREBP-1c signaling pathway. Moreover, network pharmacology was used to analyze key targets for NAFLD mitigation by ferulic acid (FA), a major component of QPE. Taken together, this study suggests that QPE could ameliorate NAFLD by modulating hepatic lipid metabolism and alleviating oxidative stress and inflammation. [ABSTRACT FROM AUTHOR]

As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics feature mining (PSFM) method. Specifically, the semantic information is first extracted from the code grammatical structure information and code text information. Then, the defect feature is mined through the semantic information. Finally, software defects are predicted by using the mined defect features. The experimental results show that, compared with the existing software defect prediction methods, the method in this paper (PSFM method) obtained a higher F-measure value. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. Studies have shown that bradykinin (BK) is highly expressed in liver cancer. We designed the novel BK receptor inhibitors J051-71 and J051-105, which reduced the viability of liver cancer cells and inhibited the formation of cancer cell colonies. J051-71 and J051-105 reduced cell proliferation and induced apoptosis in HepG2 and BEL-7402 cells, which may be due to the inhibition of the extracellular regulated protein kinase (ERK) signaling pathway. In addition, these BK receptor inhibitors reversed the cell proliferation induced by BK in HepG2 and BEL-7402 cells by downregulating B1 receptor expression. Inhibiting B1 receptor expression decreased the protein levels of p-ERK and reduced the malignant progression of HCC, providing a potential target for HCC therapy. [ABSTRACT FROM AUTHOR]