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Acute liver injury is a common consequence of taking overdose of acetaminophen (APAP). The aim of this study was to evaluate the antioxidant activity and hepatoprotective effect of a mangrove plant Sonneratia apetala fruit extract (SAFE) on APAP-induced liver injury in mice. Mice were orally pretreated with SAFE (100, 200, and 400 mg/kg) daily for one week. The control and APAP groups were intragastrically administered with distilled water, and NAC group was treated with N-Acetyl-L-cysteine (NAC) before APAP exposure. The results manifested that SAFE significantly improved survival rates, attenuated hepatic histological damage, and decreased the alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels in serum in APAP-exposed mice. SAFE treatment also increased glutathione (GSH) level and glutathione peroxidase (GSH-Px) activity, enhanced catalase (CAT), and total antioxidant capacity (T-AOC), as well as reducing malondialdehyde (MDA) level in liver. In addition, the formation of tumor necrosis factor-alpha (TNF-α), interleukin 6 (IL-6), and elevation of myeloperoxidase (MPO) in APAP-exposed mice were inhibited after SAFE treatment. And SAFE also displayed high DPPH radical scavenging activity and reducing power in vitro. The main bioactive components of SAFE such as total phenol, flavonoid, condensed tannin, and carbohydrate were determined. The current study proved that SAFE exerted potential protective effect against APAP-induced acute liver injury, which might be associated with the antioxidant and anti-inflammatory activities of SAFE. [ABSTRACT FROM AUTHOR]

The huge amount of gene expression data generated by microarray and next-generation sequencing technologies present challenges to exploit their biological meanings. When searching for the coexpression genes, the data mining process is largely affected by selection of algorithms. Thus, it is highly desirable to provide multiple options of algorithms in the user-friendly analytical toolkit to explore the gene expression signatures. For this purpose, we developed GESearch, an interactive graphical user interface (GUI) toolkit, which is written in MATLAB and supports a variety of gene expression data files. This analytical toolkit provides four models, including the mean, the regression, the delegate, and the ensemble models, to identify the coexpression genes, and enables the users to filter data and to select gene expression patterns by browsing the display window or by importing knowledge-based genes. Subsequently, the utility of this analytical toolkit is demonstrated by analyzing two sets of real-life microarray datasets from cell-cycle experiments. Overall, we have developed an interactive GUI toolkit that allows for choosing multiple algorithms for analyzing the gene expression signatures. [ABSTRACT FROM AUTHOR]