Machine Learning Integrated Workflow for Predicting Schwann Cell Viability on Conductive MXene Biointerfaces

Severe injuries to the peripheral nervous system (PNS) require Schwann cells to aid in neuronal regeneration. Low-frequency electrical stimulation is known to induce the cogrowth of neurons and Schwann cells in an injured PNS. However, the correlations between electrical stimulation and Schwann cell viability are complex and not well understood. In this work, we develop a machine learning (ML)-integrated workflow that uses conductive hydrogel biointerfaces to evaluate the impacts of fabrication parameters and electrical stimulation on the Schwann cell viability. First, a hydrogel array with varying MXene and peptide loadings is fabricated, which serves as conductive biointerfaces to incubate Schwann cells and introduce various electrical stimulation (at different voltages and frequencies). Upon specific fabrication parameters and stimulation, the cell viability is evaluated and input into an artificial neural network model to train the model. Additionally, a data augmentation method is applied to synthesize 1000-fold virtual data points, enabling the construction of a high-accuracy prediction model (with a testing mean absolute error ≤11%). By harnessing the model's predictive power, we can accurately predict Schwann cell viability based on a given set of fabrication/stimulation parameters. Finally, the SHapley Additive exPlanations model interpretation provides several data-scientific insights that are validated by microscopic cellular observations. Our hybrid approach, involving conductive biointerface fabrication, ML algorithms, and data analysis, offers an unconventional platform to construct a preclinical prediction model at the cellular level.