Data-driven supervised machine learning to predict the compressive response of porous PVA/Gelatin hydrogels and in-vitro assessments: Employing design of experiments

Abstract

The purpose of this study is to design and evaluate a series of porous hydrogels by considering three independent variables using the Box-Behnken method. Accordingly, concentrations of the constituent macromolecules of the hydrogels, Polyvinyl Alcohol and Gelatin, and concentration of the crosslinking agent are varied to fabricate sixteen different porous samples utilizing the lyophilization process. Subsequently, the porous hydrogels are subjected to a battery of tests, including Fourier Transform Infrared spectroscopy, morphology assessment, pore-size study, porosimetry, uniaxial compression, and swelling measurements. Additionally, in-vitro cell assessments are performed by culturing mouse fibroblast cells (L-929) on the hydrogels, where viability, proliferation, adhesion, and morphology of the L-929 cells are monitored over 24, 48, and 72 h to evaluate the biocompatibility of these biomaterials. To better understand the mechanical behavior of the hydrogels under compressive loadings, Deep Neural Networks (DNNs) are implemented to predict and capture their compressive stress-strain responses as a function of the constituent materials' concentrations and duration of the performed mechanical tests. Overall, this study emphasizes the importance of considering multiple variables in the design of porous hydrogels, provides a comprehensive evaluation of their mechanical and biological properties, and, particularly, implements DNNs in the prediction of the hydrogels' stress-strain responses.