Exercise training has been reported to prevent bone loss in ovariectomized (OVX) rats and postmenopausal women. We hypothesized that treadmill training inhibited adipogenesis and enhanced osteogenesis through the regulation of adipocyte differentiation factor peroxisome proliferators-activated receptor gamma (PPARγ) and the osteogenic factor runt-related transcription factor 2 (Runx2) in a model of OVX-induced osteoporosis. To test this hypothesis, 3-month-old female Sprague-Dawley rats were divided randomly into the following groups: Sham, OVX, OVX exercised (EX), and OVX estrogen replacement (E(2)). At the end of the experiment, the bone mineral density (BMD) was detected using DEXA and the morphology change of bone tissues and uterus was observed by HE staining. The protein expression for PPARγ and Runx2 were measured by immunohistochemistry and western blot and the bone triacylglycerol (TG) was extracted by methanol/chloroform. OVX dramatically increased the number of fat vacuoles, protein levels for PPARγ and Runx2 as well as the TG level in tibiae and lumbar vertebrate. In contrast, the serum level of E(2), the lumbar vertebrate BMD as well as the proximal and distal femur BMD was significantly decreased in the OVX group. All changes induced by OVX were significantly reversed by exercise treatment except for the protein expression level of Runx2. Moreover, exercise treatment produced no estrogenic effects on uterus as evidenced by the uterus wet weight and histology. Treadmill training could prevent bone loss induced by OVX through the inhibition of adipocyte differentiation factor PPARγ rather than promoting osteogenic factor Runx2.

Postmenopausal osteoporosis is associated with high level of adipogenesis within the bone marrow at the expense of osteoblast population. The mechanical effect on β-catenin through phosphorylation of glycogen synthase kinase-3β (GSK-3β) is critical for inhibition of adipogenesis in mesenchymal stem cells in vitro. In present study, we hypothesized that treadmill training could regulate the β-catenin signaling through phosphorylation of GSK-3β in the lumbar vertebrae of ovariectomized (OVX) rats. 3-month-old female Sprague-Dawley rats were divided randomly into the following four groups: (a) Sham, (b) OVX, (c) OVX exercised (EX), and (d) OVX estrogen replacement (E(2)). At the end of the experiment, the serum levels of estradiol (E(2)) and luteinizing hormone (LH), the ultimate lumbar vertebra strength, as well as the protein expression for peroxisome proliferators-activated receptor γ (PPARγ), β-catenin, P-GSK-3β, and osterix (Osx) in lumbar vertebrae were analyzed. Moreover, the protein expression for β-catenin and P-GSK-3β were also examined in the uterus. The EX group had lower protein level of PPARγ, higher ultimate lumbar vertebral strength, and higher protein levels of β-catenin, and P-GSK-3β in lumbar vertebral bodies compared with sedentary OVX group. The effects of EX treatment on the protein levels of β-catenin and P-GSK-3β in bones were not reproducible in the uterus. Moreover, exercise treatment produced no estrogenic effect as evidenced by serum level of LH. In conclusion, this study suggested that treadmill training could activate the GSK-3β/β-catenin signaling and inhibit the production of PPARγ in lumbar vertebrae of OVX rats, which may contribute to the prevention of bone loss in OVX rats.

Purpose Many factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory. Design/methodology/approach The authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively. Findings Compared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods. Practical implications The two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8. Originality/value The improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.

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