The association of hs-CRP and fibrinogen with anthropometric and lipid parameters in non-obese adolescent girls with polycystic ovary syndrome.

Background The aim of the study was to evaluate high-sensitivity C-reactive protein (hs-CRP) and fibrinogen in non-obese normoinsulinemic adolescent girls with polycystic ovary syndrome (PCOS) and their relationship with anthropometric and lipid parameters. Methods The study comprised a total of 26 adolescent girls newly diagnosed with PCOS and 12 healthy controls with regular ovulatory menstrual cycles. The concentration of hs-CRP, fibrinogen, anthropometric measurements, and biochemical and hormonal testing were assessed. Results PCOS adolescent girls had significantly higher levels of hs-CRP and fibrinogen compared to healthy controls. In univariate regression analysis, statistically significant associations of hs-CRP and fibrinogen levels of PCOS patients have been shown with body mass index (BMI), waist circumference (WC), hip circumference (HC) and low-density lipoprotein (LDL)/high-density lipoprotein (HDL) ratio, while hs-CRP levels were also associated with cholesterol and LDL. In the multivariate regression model, we found that hs-CRP levels were predicted by BMI (ß=0.541, p<0.001) and LDL (ß=0.507, p=0.014), while fibrinogen levels were predicted by BMI (ß=0.449, p=0.004). Conclusions We have shown an association of proinflammatory indices hs-CRP and fibrinogen with anthropometric and lipid parameters of adolescent women with PCOS. The inflammatory markers might be useful in monitoring normal-weight adolescent women with PCOS in an effort to timely prevent unfavorable changes in body mass and lipid profile.

Constrained Nets for Graph Matching and Other Quadratic Assignment Problems.

Some time ago Durbin and Willshaw proposed an interesting parallel algorithm (the "elastic net") for approximately solving some geometric optimization problems, such as the Traveling Salesman Problem. Recently it has been shown that their algorithm is related to neural networks of Hopfield and Tank, and that they both can be understood as the semiclassical approximation to statistical mechanics of related physical models. The main point of the elastic net algorithm is seen to be in the way one deals with the constraints when evaluating the effective cost function (free energy in the thermodynamic analogy), and not in its geometric foundation emphasized originally by Durbin and Willshaw. As a consequence, the elastic net algorithm is a special case of the more general physically based computations and can be generalized to a large class of nongeometric problems. In this paper we further elaborate on this observation, and generalize the elastic net to the quadratic assignment problem. We work out in detail its special case, the graph matching problem, because it is an important problem with many applications in computational vision and neural modeling. Simulation results on random graphs, and on structured (hand-designed) graphs of moderate size (20-100 nodes) are discussed.