Premature ovarian failure (POF) is a condition affecting 1% of women in the general population, causing amenorrhea, hypergonadotropism and hypoestrogenism before the age of 40. Currently, POF cannot be reversed and, although treatments are available, there is an urgent need for improved treatment strategies. Growth hormone (GH) is a pleiotropic hormone that affects a broad spectrum of physiological functions, from carbohydrate and lipid metabolism to the immune response. GH has previously been used to treat POF in non-transgenic preclinical trials, but the biochemical mechanism underlying these effects are unclear. In the present study, a mouse model of POF was generated using cyclophosphamide. Treatment of POF mice with recombinant mouse growth hormone (rmGH) was revealed to markedly reduce POF histopathology in ovarian tissue, relieve ovarian granulosa cell injury, reduce the number of atretic follicles and significantly increase the number of mature oocytes. Furthermore, an enzyme-linked immunosorbent assay revealed that plasma estradiol levels increased and plasma follicle stimulating hormone levels decreased with time in a group of mice treated with a medium dose of rmGH (0.8 mg/kg) when compared with the POF model group (P<0.05). In addition, reverse transcription-quantitative polymerase chain reaction and immunohistochemical analysis demonstrated elevated levels of Notch-1 signaling pathway factors (Notch1, CBF1, and HES1) in wild-type mice and those treated with medium and high doses of rmGH, but not in those treated with low doses of rmGH. In conclusion, GH may promote ovarian tissue repair, estrogen release and oocyte maturation via activation of the Notch-1 signaling pathway in ovarian tissue.

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[1] A novel data mining method called MineTool is introduced which, by virtue of automating the modeling process and model evaluations, makes it more accessible to nonexperts. The technique aggregates the various stages of model building into a four-step process consisting of (1) data segmentation and sampling, (2) variable preselection and transform generation, (3) predictive model estimation and validation, and (4) final model testing. Optimal strategies are chosen for each modeling step. However, the modular design of the MineTool enables the substitution of alternative strategies in any of the four modeling steps. A notable feature of the technique is that the final model is always in closed analytical form rather than ''black box'' form of most other techniques. MineTool can be used for analysis of data (e.g., time series) as well as images. The utility of the technique is illustrated through several examples based on synthetic data. Application of the technique to analysis of spacecraft data will be presented in subsequent papers.