Women with polycystic ovary syndrome (PCOS) are markedly insulin-resistant, but the molecular mechanisms of these changes and their relationship to the hyperandrogenic state remain to be clarified. Mutations have recently been identified in the insulin receptor gene of patients with extreme forms of insulin resistance associated with hyperandrogenism (eg, type A insulin resistance), and these mutations account for the insulin resistance in such patients. We performed this study to determine whether mutations in the coding portion of the insulin receptor gene were responsible for insulin resistance in PCOS. Insulin binding studies using cultured skin fibroblasts of three obese (body mass index > 27 kg/m2) women with PCOS (ie, mild hyperandrogenemia and chronic anovulation of unknown etiology) and documented insulin resistance showed no apprarent abnormalities in either the number or affinity of insulin binding sites. Direct sequencing of all 22 exons of the insulin receptor gene from two of the women with PCOS did not reveal any mutations. Furthermore, both alleles of the gene were expressed at equal levels. In a third insulin-resistant PCOS woman, there was no evidence for a mutation in the coding portion of the insulin receptor gene as determined by denaturing gradient gel electrophoresis (DGGE). We conclude that the insulin resistance in these PCOS women was caused by a defect extrinsic to the insulin receptor. 相似文献
We consider a class of Cohen–Grossberg neural networks with delays. We prove the existence and global asymptotic stability of an equilibrium point and estimate the region of existence. Furthermore, we show that the trajectories of the neural networks with positive initial data will stay in the positive region if the amplification function satisfies a divergent condition. We also establish the existence of a globally attracting compact set for more general networks. We estimate this compact set explicitly in terms of the network parameters from physiological and biological models. Our results can be applied to neural networks with a wide range of activation functions which are neither bounded nor globally Lipschitz continuous such as the Lotka–Volterra model. We also give some examples and simulations. 相似文献
Based on a typical residential area, this paper studies the characteristics of pollutant concentration changes in two rainfall runoffs and the first flush effect of rainfall. In rainfall runoff, the concentrations of seven pollutants (CODMn, TN, DTN, NH3-N, TP, DTP, and PO43−) increased during the initial rainfall period and decreased in the later period. Rainfall causes the erosion of pollutants on the underlying surface so that water pollution begins when rainfall runoff occurs, and the pollution level drops over time. The seven pollutants all experience this first flush effect, of which, rainfall has the strongest scouring effect on NH3-N produced by domestic sewage. The significant excess of pollutants in rainfall runoff should be considered by management departments. In addition, the existence of the first flush effect makes it possible in theory to partially intercept rainfall runoff to control water pollution, thereby reducing the cost of pollution control.