The aim of this study was to investigate the syste-matic changes in ghrelin and leptin expression, as well as their correlation with insulin resistance (IR) during the development of type 2 diabetes mellitus (T2DM) in a rat model. T2DM was induced in rats fed a high-fat (HF)-diet followed by the intraperitoneal injection of low-dose streptozotocin (STZ, 35 mg/kg). Sixty male Sprague-Dawley rats were divided into 4 groups: the control, HF-4W (HF diet for 4 weeks), HF-8W (HF diet for 8 weeks) and the T2DM group. During the development of T2DM, the production of ghrelin in the stomach and leptin in adipose tissue, the blood levels of ghrelin and leptin, and the expression of leptin and ghrelin receptors (OB-Rb and GHS-R1a) in the hypothalamus were measured by enzyme-linked immunosorbent assay (ELISA), radioimmunology assay (RIA), immunohistochemistry (IHC) and real-time reverse transcription-polymerase chain reaction (real-time RT-PCR). IR was assessed using the hyperinsulinemic-euglycemic clamp technique. The production of ghrelin in the stomach, the plasma ghrelin levels and the expression of GHS-R1a in the hypothalamus were significantly reduced in the HF-4W and HF-8W rats compared with the control rats; however, no significant difference was found between the HF-8W and T2DM group rats. Comparing the control to the T2DM group, the production of leptin in the adipose tissue and the serum leptin levels increased, whereas the expression of OB-Rb in the hypothalamus decreased. At the same time, the glucose infusion rate (GIR), indicating the insulin sensitivity, decreased significantly; GIR positively correlated with plasma ghrelin and negatively correlated with serum leptin levels. In conclusion, increased leptin levels are associated with obesity and T2DM, while decreased ghrelin levels are associated with obesity/IR rather than T2DM.
Some recent successful semi-supervised learning methods construct more than one learner from both labeled and unlabeled data for inductive learning. This paper proposes a novel multiple-view multiple-learner (MVML) framework for semi-supervised learning, which differs from previous methods in possession of both multiple views and multiple learners. This method adopts a co-training styled learning paradigm in enlarging labeled data from a much larger set of unlabeled data. To the best of our knowledge it is the first attempt to combine the advantages of multiple-view learning and ensemble learning for semi-supervised learning. The use of multiple views is promising to promote performance compared with single-view learning because information is more effectively exploited. At the same time, as an ensemble of classifiers is learned from each view, predictions with higher accuracies can be obtained than solely adopting one classifier from the same view. Experiments on different applications involving both multiple-view and single-view data sets show encouraging results of the proposed MVML method.
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