Motion analysis of right ventricular wall based on an electromechanical biventricular model

Based on our previously developed electrical heart model, an electromechanical biventricular model, which couples the electrical property and mechanical property of the heart, was constructed and the right ventricular wall motion and deformation was simulated using this model. The model was developed on the basis of composite material theory and finite element method. The excitation propagation was simulated by electrical heart model, and the resultant active forces were used to study the ventricular wall motion during systole. The simulation results show that: (1) The right ventricular free wall moves towards the septum, and at the same time, the base and middle of free wall move towards the apex, which reduce the volume of right ventricle; (2) The minimum principle strain (E3) is largest at the apex, then at the middle of free wall, and its direction is in the approximate direction of epicardial muscle fibers. These results are in good accordance with solutions obtained from MR tagging images. It suggests that such electromechanical biventricular model can be used to assess the mechanical function of two ventricles.

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.