Biofabrication of Sodium Alginate Hydrogel Scaffolds for Heart Valve Tissue Engineering.

Every year, thousands of aortic valve replacements must take place due to valve diseases. Tissue-engineered heart valves represent promising valve substitutes with remodeling, regeneration, and growth capabilities. However, the accurate reproduction of the complex three-dimensional (3D) anatomy of the aortic valve remains a challenge for current biofabrication methods. We present a novel technique for rapid fabrication of native-like tricuspid aortic valve scaffolds made of an alginate-based hydrogel. Using this technique, a sodium alginate hydrogel formulation is injected into a mold produced using a custom-made sugar glass 3D printer. The mold is then dissolved using a custom-made dissolving module, revealing the aortic valve scaffold. To assess the reproducibility of the technique, three scaffolds were thoroughly compared. CT (computed tomography) scans showed that the scaffolds respect the complex native geometry with minimal variations. The scaffolds were then tested in a cardiac bioreactor specially designed to reproduce physiological flow and pressure (aortic and ventricular) conditions. The flow and pressure profiles were similar to the physiological ones for the three valve scaffolds, with small variabilities. These early results establish the functional repeatability of this new biofabrication method and suggest its application for rapid fabrication of ready-to-use cell-seeded sodium alginate scaffolds for heart valve tissue engineering.

A New Approach Based on a Multiobjective Evolutionary Algorithm for Accurate Control of Flow Rate and Blood Pressure in Cardiac Bioreactors.

PURPOSE: Accurately reproducing physiological and time-varying variables in cardiac bioreactors is a difficult task for conventional control methods. This paper presents a new controller based on a genetic algorithm for the control of a cardiac bioreactor dedicated to the study and conditioning of heart valve substitutes. METHODS: A multi-objective genetic algorithm was designed to obtain an accurate simultaneous reproduction of physiological periodic time functions of the three most relevant variables characterizing the blood flow in the aortic valve. These three controlled variables are the flow rate and the pressures upstream and downstream of the aortic valve. RESULTS: Experimental results obtained with this new algorithm showed an accurate dynamic reproduction of these three controlled variables. Moreover, the controller can react and adapt continuously to changes happening over time in the cardiac bioreactor, which is a major advantage when working with living biological valve substitutes. CONCLUSION: The strong non-linear interaction that exists between the three controlled variables makes it difficult to obtain a precise control of any of these, let alone all three simultaneously. However, the results showed that this new control algorithm can efficiently overcome such difficulties. In the particular field of bioreactors reproducing the cardiovascular environment, such a flexible, versatile and accurate reproduction of these three interdependent controlled variables is unprecedented.