Decellularized Extracellular Matrix Scaffolds for Cardiovascular Tissue Engineering: Current Techniques and Challenges.

Cardiovascular diseases are the leading cause of global mortality. Over the past two decades, researchers have tried to provide novel solutions for end-stage heart failure to address cardiac transplantation hurdles such as donor organ shortage, chronic rejection, and life-long immunosuppression. Cardiac decellularized extracellular matrix (dECM) has been widely explored as a promising approach in tissue-regenerative medicine because of its remarkable similarity to the original tissue. Optimized decellularization protocols combining physical, chemical, and enzymatic agents have been developed to obtain the perfect balance between cell removal, ECM composition, and function maintenance. However, proper assessment of decellularized tissue composition is still needed before clinical translation. Recellularizing the acellular scaffold with organ-specific cells and evaluating the extent of cardiomyocyte repopulation is also challenging. This review aims to discuss the existing literature on decellularized cardiac scaffolds, especially on the advantages and methods of preparation, pointing out areas for improvement. Finally, an overview of the state of research regarding the application of cardiac dECM and future challenges in bioengineering a human heart suitable for transplantation is provided.

Optimization of Complete Rat Heart Decellularization Using Artificial Neural Networks.

Whole organ decellularization techniques have facilitated the fabrication of extracellular matrices (ECMs) for engineering new organs. Unfortunately, there is no objective gold standard evaluation of the scaffold without applying a destructive method such as histological analysis or DNA removal quantification of the dry tissue. Our proposal is a software application using deep convolutional neural networks (DCNN) to distinguish between different stages of decellularization, determining the exact moment of completion. Hearts from male Sprague Dawley rats (n = 10) were decellularized using 1% sodium dodecyl sulfate (SDS) in a modified Langendorff device in the presence of an alternating rectangular electric field. Spectrophotometric measurements of deoxyribonucleic acid (DNA) and total proteins concentration from the decellularization solution were taken every 30 min. A monitoring system supervised the sessions, collecting a large number of photos saved in corresponding folders. This system aimed to prove a strong correlation between the data gathered by spectrophotometry and the state of the heart that could be visualized with an OpenCV-based spectrometer. A decellularization completion metric was built using a DCNN based classifier model trained using an image set comprising thousands of photos. Optimizing the decellularization process using a machine learning approach launches exponential progress in tissue bioengineering research.