The endothelial glycocalyx prefers albumin for evoking shear stress-induced, nitric oxide-mediated coronary dilatation

Background: Shear stress induces coronary dilatation via production of nitric oxide ( NO). This should involve the endothelial glycocalyx ( EG). A greater effect was expected of albumin versus hydroxyethyl starch ( HES) perfusion, because albumin seals coronary leaks more effectively than HES in an EG-dependent way. Methods: Isolated hearts ( guinea pigs) were perfused at constant pressure with Krebs-Henseleit buffer augmented with 1/3 volume 5% human albumin or 6% HES ( 200/0.5 or 450/0.7). Coronary flow was also determined after EG digestion ( heparinase) and with nitro-L-arginine ( NO-L-Ag). Results: Coronary flow ( 9.50 +/- 1.09, 5.10 +/- 0.49, 4.87 +/- 1.19 and 4.15 +/- 0.09 ml/ min/ g for `albumin', `HES 200', `HES 450' and `control', respectively, n = 5-6) did not correlate with perfusate viscosity ( 0.83, 1.02, 1.24 and 0.77 cP, respectively). NO-L-Ag and heparinase diminished dilatation by albumin, but not additively. Alone NO-L-Ag suppressed coronary flow during infusion of HES 450. Electron microscopy revealed a coronary EG of 300 nm, reduced to 20 nm after heparinase. Cultured endothelial cells possessed an EG of 20 nm to begin with. Conclusions: Albumin induces greater endothelial shear stress than HES, despite lower viscosity, provided the EG contains negative groups. HES 450 causes some NO-mediated dilatation via even a rudimentary EG. Cultured endothelial cells express only a rudimentary glycocalyx, limiting their usefulness as a model system. Copyright (c) 2007 S. Karger AG, Basel

Combination of M-Estimators and Neural Network Model to Analyze Inside/Outside Bark Tree Diameters

Part 1: ANN-Classification and Pattern RecognitionInternational audienceOne of the most important statistical tools is linear regression analysis for many fields such as medical sciences, social sciences, econometrics and more. Regression techniques are commonly used for modelling the relationship between response variables and explanatory variables. In this study, inside bark tree diameter was used as the dependent variable and outside bark diameter and site type as independents. While generally it is assumed that inside and outside bark diameters are linearly correlated, linear regression application is weak in the presence of outliers. The purpose of this study was to develop a Multi-Layer Perceptron neural network model which considered significant variables from an a priori developed robust regression model. The application of robust regression could be considered in selecting the input variables in a neural network model