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Objective: Lumbar disc herniation (LDH) is a common spinal surgical disease. Low back and leg pain caused by LDH is the main factor leading to functional disability, which has caused a serious burden to patients and society. Osteoking can delay the progression of osteoporosis and osteoarthritis, and even has a significant effect on the prevention of deep vein thrombosis after fracture surgery. In recent years, it has been gradually used in the treatment of LDH and has received significant results. However, the underlying mechanism remains unclear. The aim of this study was to predict the mechanism of Osteoking in the treatment of LDH through network pharmacology and verify it by molecular docking method. Methods: The TCMSP database was used to collect the relevant active components and targets of Osteoking, while the GeneCards, OMIM and DisGeNET databases were utilized to collect the relevant disease targets of LDH. The Venny 2.1.0 software was employed to obtain the intersecting gene targets of Osteoking and LDH. PPI network construction and core target selection were performed using Cytoscape 3.9.0 software. The Metascape database was used for GO and KEGG enrichment analysis of the relevant targets. Finally, molecular docking was conducted using AutoDock software. Results: The study identified 116 potential targets and 26 core targets for the treatment of LDH with Osteoking. Pathways in cancer, Alzheimer's disease, microRNAs in cancer and the IL-17 signalling pathway were among the main involved signalling pathways. Molecular docking results demonstrated that the key targets AKT1, IL-6, ALB, TNF and IL-1β exhibited relatively stable binding activities with the main active components of Osteoking. Conclusions: Osteoking can alleviate the symptoms of lumbar disc herniation through the modulation of multiple targets and signalling pathways. [ABSTRACT FROM AUTHOR]

Objective. To evaluate the effects of He's Yangchao Recipe (HSYC) on ameliorating ovarian oxidative stress of aging mice under consecutive superovulation. Methods. An 8-month-old C57BL/6 female mouse was chosen to establish an aging model under ovarian hyperstimulation. Mice were randomly separated into four groups: R1 as the control group, R4 as the model group, NR4 with N-acetyl-L-cysteine (NAC) administration, and TR4 with HSYC administration. Oocyte collection, in vitro fertilization, and embryo culture were performed. The serum hormone levels were measured by enzyme-linked immunosorbent assays (ELISA); the reactive oxygen species (ROS) level of oocytes, the number of growing follicles, corpus luteum, ovulated oocytes, and developing embryos at each stage, along with the proportions of fragmented oocytes and abnormal mitochondria in granulosa cells (GCs) and the apoptosis rate of GCs were calculated; the mRNA and protein levels of JNK, P53, BAX were detected by real-time PCR and the Simple Western System. Results. HSYC enhanced estradiol, progesterone, and inhibin-B levels and increased growing follicle and corpus luteum and ovulated egg counts compared to the R4 group (P < 0.05), whereas it decreased the proportions of fragmented oocytes (P < 0.01); Meanwhile, embryos from mice subjected to four superovulation cycles with HSYC treated had a higher hatching potential. The ROS level of oocytes is downregulated by HSYC (P < 0.01) and the percentage of abnormal mitochondrial in ovaries of the TR4 group was also significantly declined compared to the R4 group (P < 0.05); the most TUNEL-positive cells proportion was detected in the R4 group; nevertheless, HSYC effectively attenuated this detrimental effect (P < 0.05). The mRNA and protein expressions of JNK and P53 in ovary tissues were reduced in the TR4 group while these genes were upregulated by repeated superovulation (P < 0.05). Conclusions. HSYC exerted promising effects on promoting the diminished ovarian reserve and decreased oocyte quality induced by both aging and consecutive ovarian superovulation, potentially via the ROS/JNK/p53 pathway. [ABSTRACT FROM AUTHOR]

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]