Orexin-stimulated MAP kinase cascades are activated through multiple G-protein signalling pathways in human H295R adrenocortical cells: diverse roles for orexins A and B

Orexins A and B (ORA and ORB) are neuropeptide hormones found throughout the central nervous system and periphery. They are required for a host of physiological processes including mitogen-activated protein kinase (MAPK) regulation, steroidogenesis, appetite control and energy regulation. While some signalling mechanisms have been proposed for individual recombinant orexin receptors in generic mammalian cell types, it is clear that the peripheral effects of orexin are spatially and temporally complex. This study dissects the different G-protein signalling and MAPK pathways activated in a pluripotent human adrenal H295R cell line capable of all the physiological steps involved in steroidogenesis. Both extracellular receptor kinase 1/2 (ERK1/2) and p38 were phosphorylated rapidly with a subsequent decline, in a time- and dose-dependent manner, in response to both ORA and ORB. Conversely, there was little or no direct activation of the ERK5 or JNK pathway. Analysis using signalling and MAPK inhibitors as well as receptor-specific antagonists determined the precise mediators of the orexin response in these cells. Both ERK1/2 and p38 activation were predominantly Gq- and to a lesser extent Gs-mediated; p38 activation even had a small Gi-component. Effects were broadly comparable for both orexin sub-types ORA and ORB and although most of the effects were transmitted through the orexin receptor-1 subtype, we did observe a role for orexin receptor-2-mediated activation of both ERK1/2 and p38. Cortisol secretion also differed in response to ORA and ORB. These data suggest multiple roles for orexin-mediated MAPK activation in an adrenal cell-line, this complexity may help to explain the diverse biological actions of orexins with wide-ranging consequences for our understanding of the mechanisms initiated by these steroidogenic molecules.

Data methods in optometry. Part 6: fitting a regression line to data

1. Fitting a linear regression to data provides much more information about the relationship between two variables than a simple correlation test. A goodness of fit test of the line should always be carried out. Hence, r squared estimates the strength of the relationship between Y and X, ANOVA whether a statistically significant line is present, and the ‘t’ test whether the slope of the line is significantly different from zero. 2. Always check whether the data collected fit the assumptions for regression analysis and, if not, whether a transformation of the Y and/or X variables is necessary. 3. If the regression line is to be used for prediction, it is important to determine whether the prediction involves an individual y value or a mean. Care should be taken if predictions are made close to the extremities of the data and are subject to considerable error if x falls beyond the range of the data. Multiple predictions require correction of the P values. 3. If several individual regression lines have been calculated from a number of similar sets of data, consider whether they should be combined to form a single regression line. 4. If the data exhibit a degree of curvature, then fitting a higher-order polynomial curve may provide a better fit than a straight line. In this case, a test of whether the data depart significantly from a linear regression should be carried out.

Laser-induced breakdown spectroscopy quantitative analysis method via adaptive analytical line selection and relevance vector machine regression model

Abstract A new LIBS quantitative analysis method based on analytical line adaptive selection and Relevance Vector Machine (RVM) regression model is proposed. First, a scheme of adaptively selecting analytical line is put forward in order to overcome the drawback of high dependency on a priori knowledge. The candidate analytical lines are automatically selected based on the built-in characteristics of spectral lines, such as spectral intensity, wavelength and width at half height. The analytical lines which will be used as input variables of regression model are determined adaptively according to the samples for both training and testing. Second, an LIBS quantitative analysis method based on RVM is presented. The intensities of analytical lines and the elemental concentrations of certified standard samples are used to train the RVM regression model. The predicted elemental concentration analysis results will be given with a form of confidence interval of probabilistic distribution, which is helpful for evaluating the uncertainness contained in the measured spectra. Chromium concentration analysis experiments of 23 certified standard high-alloy steel samples have been carried out. The multiple correlation coefficient of the prediction was up to 98.85%, and the average relative error of the prediction was 4.01%. The experiment results showed that the proposed LIBS quantitative analysis method achieved better prediction accuracy and better modeling robustness compared with the methods based on partial least squares regression, artificial neural network and standard support vector machine.