Abstract-Preeclampsia is a hypertensive disorder of pregnancy caused by abnormal placental function, partly because of chronic hypoxia at the utero-placental junction. The increase in levels of soluble vascular endothelial growth factor receptor 1, an antiangiogenic agent known to inhibit placental vascularization, is an important cellular factor implicated in the onset of preeclampsia. We investigated the ligand urotensin II (U-II), a potent endogenous vasoconstrictor and proangiogenic agent, for which levels have been reported to increase in patients with preeclampsia. We hypothesized that an increased sensitivity to U-II in preeclampsia might be achieved by upregulation of placental U-II receptors. We further investigated the role of U-II receptor stimulation on soluble vascular endothelial growth factor receptor 1 release in placental explants from diseased and normal patients. Immunohistochemistry, real-time PCR, and Western blotting analysis revealed that U-II receptor expression was significantly upregulated in preeclampsia placentas compared with controls (PϽ0.01). Cellular models of syncytiotrophoblast and vascular endothelial cells subjected to hypoxic conditions revealed an increase in U-II receptor levels in the syncytiotrophoblast model. This induction is regulated by the transcriptional activator hypoxia-inducible factor 1␣. U-II treatment is associated with increased secretion of soluble vascular endothelial growth factor receptor 1 only in preeclamptic placental explants under hypoxia but not in control conditions. Interestingly, normal placental explants did not respond to U-II stimulation. (Hypertension. 2010;56:172-178.)
Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.