Ghrelin and des-acyl ghrelin inhibit cell death in cardiomyocytes and endothelial cells through ERK1/2 and PI 3-kinase/AKT

Ghrelin is an acyl-peptide gastric hormone acting on the pituitary and hypothalamus to stimulate growth hormone (GH) release, adiposity, and appetite. Ghrelin endocrine activities are entirely dependent on its acylation and are mediated by GH secretagogue (GHS) receptor (GHSR)-1a, a G protein–coupled receptor mostly expressed in the pituitary and hypothalamus, previously identified as the receptor for a group of synthetic molecules featuring GH secretagogue (GHS) activity. Des-acyl ghrelin, which is far more abundant than ghrelin, does not bind GHSR-1a, is devoid of any endocrine activity, and its function is currently unknown. Ghrelin, which is expressed in heart, albeit at a much lower level than in the stomach, also exerts a cardio protective effect through an unknown mechanism, independent of GH release. Here we show that both ghrelin and des-acyl ghrelin inhibit apoptosis of primary adult and H9c2 cardiomyocytes and endothelial cells in vitro through activation of extracellular signal–regulated kinase-1/2 and Akt serine kinases. In addition, ghrelin and des-acyl ghrelin recognize common high affinity binding sites on H9c2 cardiomyocytes, which do not express GHSR-1a. Finally, both MK-0677 and hexarelin, a nonpeptidyl and a peptidyl synthetic GHS, respectively, recognize the common ghrelin and des-acyl ghrelin binding sites, inhibit cell death, and activate MAPK and Akt

A heuristic search procedure for the automated analysis of dynamic cardiac scintigrams

Le but de cette comunication est la description d'un procédé pour l'extraction du contour des objets convexes dans des images avec bruit. Cette opération peut être réalisée comme une recherche d'un chemin optimal dans un graphe avec coût minimal en utilisant des techniques propres à l'Intelligence Artificielle Cette approche devient plus facile en changeant la représentation de l'image de coordonnées cartésiennes à polaires avec origine dans le centre approximatif de l'objet et en reliant chaque point de l'image à un noeud du graphe. Cette recherche est effectué par l'algorithme A*, qui recherche le chemin avec coût minimal par la fonction d'évaluation suivante: f(n) = g(n) + h(n) où g(n) est le coût du chemin avec coût minimal du noeud initial au noeud n; et h(n) est l'estimation du coût du chemin minimal du noeud n au noeud final. Cet algorithme devient optimal lorsque des convenables fonctions sont choisies. Le traitement proposé à été appliqué à l'extraction du contour du ventricle gauche (VG) dans une séquence des scintigraphies. Le centre approximatif du VG est déterminé comme le centre de gravité du signal le long de l'axe du ventricle. Ce centre devient l'origine pour la transformation des coordonnées. La deuxième dérivée partielle du signal le long de la coordonnée radiale peut donner une contribution utile à la fonction d'évaluation g(n). Ultérieures contributions à g(n) peuvent être ajoutées en tenant compte de la connaissance à priori de la courbure du contour du ventricle et de sa position par rapport à son centre. L'extraction du contour est effectuée au débout dans l'image diastolique, ensuite presque le même procédé est appliqué dans les images suivantes, en modifiant la fonction g(n) pour limiter la zone de recherche par rapport au contour de l'image précédente

Model system to study the influence of aggregation on the hemolytic potential of silica nanoparticles

A well-defined silica nanoparticle model system was developed to study the effect of the size and structure of aggregates on their membranolytic activity. The aggregates were stable and characterized using transmission electron microscopy, dynamic light scattering, nitrogen adsorption, small-angle X-ray scattering, infrared spectroscopy, and electron paramagnetic resonance. Human red blood cells were used for assessing the membranolytic activity of aggregates. We found a decreasing hemolytic activity for increasing hydrodynamic diameter of the nanoparticle aggregates, in contrast to trends observed for isolated particles. We propose here a qualitative model that considers the fractal structure of the aggregates and its influence on membrane deformation to explain these observations. The open structure of the aggregates means that only a limited number of primary particles, from which the aggregates are built up, are in contact with the cell membrane. The adhesion energy is thus expected to decrease resulting in an overall lowered driving force for membrane deformation. Hence, the hemolytic activity of aggregates, following an excessive deformation of the cell membrane, decreases as the aggregate size increases. Our results indicate that the aggregate size and structure determine the hemolytic activity of silica nanoparticle aggregates.status: publishe

Model system to study the influence of aggregation on the hemolytic potential of silica nanoparticles.

A well-defined silica nanoparticle model system was developed to study the effect of the size and structure of aggregates on their membranolytic activity. The aggregates were stable and characterized using transmission electron microscopy, dynamic light scattering, nitrogen adsorption, small-angle X-ray scattering, infrared spectroscopy, and electron paramagnetic resonance. Human red blood cells were used for assessing the membranolytic activity of aggregates. We found a decreasing hemolytic activity for increasing hydrodynamic diameter of the nanoparticle aggregates, in contrast to trends observed for isolated particles. We propose here a qualitative model that considers the fractal structure of the aggregates and its influence on membrane deformation to explain these observations. The open structure of the aggregates means that only a limited number of primary particles, from which the aggregates are built up, are in contact with the cell membrane. The adhesion energy is thus expected to decrease resulting in an overall lowered driving force for membrane deformation. Hence, the hemolytic activity of aggregates, following an excessive deformation of the cell membrane, decreases as the aggregate size increases. Our results indicate that the aggregate size and structure determine the hemolytic activity of silica nanoparticle aggregates