Investigation of Hydrogel and Gelatin Bath Formulations for Extrusion-Based 3D Bioprinting using Deep Learning

Abstract

Bioprinting is a direct method to fabricate tissue constructs that can support cell growth. Hydrogels are 3D printed viscous gel-like substances made from materials like sodium alginate, collagen, and hyaluronic acid that are used for bio-constructs. Popular uses include being used for contact lenses, tissue constructs, dressings for wound healing and more. Extrusion-based bioprinting is concerned with balancing the ability of the hydrogel to maintain and grow cells while also being strong enough to withstand the pressure of the human body and the printing process itself. This paper utilizes the freeform reversible embedding of suspended hydrogels (FRESH) method which consists of using a 3D printer that uses air pressure to extrude the hydrogels into a petri dish filled with baths made of gelatin and crosslinking agents to help bind the extruded material. It investigates hydrogels made of alginate and utilized calcium chloride as a crosslinking agent to aid in hydrogel stability and utilizes deep learning method to train and predict the bioprint quality. This research identifies the combinations and ratios of alginate that makes a practical hydrogel that produce bio constructs as related to filament size. The results demonstrate the viability of extrusion-based bioprinting using the FRESH method in a gelatin bath and discussed issues, challenges, and limitations for future research.