A new fibrin sealant as a three-dimensional scaffold candidate for mesenchymal stem cells

Introduction: The optimization of an organic scaffold for specific types of applications and cells is vital to successful tissue engineering. In this study, we investigated the effects of a new fibrin sealant derived from snake venom as a scaffold for mesenchymal stem cells, to demonstrate the ability of cells to affect and detect the biological microenvironment.Methods: The characterization of CD34, CD44 and CD90 expression on mesenchymal stem cells was performed by flow cytometry. In vitro growth and cell viability were evaluated by light and electron microscopy. Differentiation into osteogenic, adipogenic and chondrogenic lineages was induced.Results: The fibrin sealant did not affect cell adhesion, proliferation or differentiation and allowed the adherence and growth of mesenchymal stem cells on its surface. Hoechst 33342 and propidium iodide staining demonstrated the viability of mesenchymal stem cells in contact with the fibrin sealant and the ability of the biomaterial to maintain cell survival.Conclusions: The new fibrin sealant is a three-dimensional scaffolding candidate that is capable of maintaining cell survival without interfering with differentiation, and might also be useful in drug delivery. Fibrin sealant has a low production cost, does not transmit infectious diseases from human blood and has properties of a suitable scaffold for stem cells because it permits the preparation of differentiated scaffolds that are suitable for every need

LMP based bid formation for virtual power players operating in smart grids

Power system organization has gone through huge changes in the recent years. Significant increase in distributed generation (DG) and operation in the scope of liberalized markets are two relevant driving forces for these changes. More recently, the smart grid (SG) concept gained increased importance, and is being seen as a paradigm able to support power system requirements for the future. This paper proposes a computational architecture to support day-ahead Virtual Power Player (VPP) bid formation in the smart grid context. This architecture includes a forecasting module, a resource optimization and Locational Marginal Price (LMP) computation module, and a bid formation module. Due to the involved problems characteristics, the implementation of this architecture requires the use of Artificial Intelligence (AI) techniques. Artificial Neural Networks (ANN) are used for resource and load forecasting and Evolutionary Particle Swarm Optimization (EPSO) is used for energy resource scheduling. The paper presents a case study that considers a 33 bus distribution network that includes 67 distributed generators, 32 loads and 9 storage units