Ghrelin to obestatin ratio in maternal serum in pregnancies complicated by intrauterine growth restriction.

Ghrelin, an endogenous for the growth hormone secretagogue receptor, has been shown to participate in fetal growth. Obestatin, encoded by the same gene as ghrelin, is described as a physiological opponent of ghrelin. This study was designed to determine the changes of ghrelin/obestatin ratio in maternal serum in pregnancies with intrauterine growth restriction (IUGR). The authors found that the ghrelin levels in maternal serum were significent lower in IUGR group than in control group (236.34 ± 14.58 pg/ml vs. 321.49 ± 18.19 pg/ml, p = 0.003). However, the difference of obestatin levels in maternal serum in IUGR group than in control group was not significent (276.25, ±20.54 pg/ml vs. 256.34 ± 21.21 pg/ml, p = 0.308). The ratio of ghrelin to obestatin in maternal serum were significent lower in UGR group than in control group (1.05 ± 0.09 vs. 0.82 ± 0.08, p = 0.03). Meanwhile, the maternal serum growth hormone (GH) concentration in IUGR group was lower than that in control group (1.08 ± 0.08 pg/ml vs. 1.41 ± 0.09 pg/ml, p = 0.009), and the maternal serum pla- cental growth hormone (PGH) concentration in IUGR group was lower than that in control group (2.21 ± 1.24 pg/ml vs. 2.92 ± 0.27 pg/ml,p = 0.031). The ratio of ghrelin to obestatin in maternal serum were positively correlation with GH and PGH concentrations in IUGR group (r = 0.876, p = 0.52; r = 0.764, p = 0.64). The findings of this study suggest that the ratio of ghrelin to obestatin in maternal serum were low, and were positively correlated with GH and PGH concentration in IUGR group, which can been considered as evidencees of ghrelin/obestatin balance disorder role in pathogenesis of IUGR.

Prediction of programmed-temperature retention values of naphthas by artificial neural networks.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.