Smart hydrogel dressing for machine learning-enabled visual monitoring and promote diabetic wound healing

Abstract

Diabetic wounds are complex complications characterized by long-term chronic inflammation, vascular damage, and difficulties in healing. Monitoring wound pH can serve as an early warning system for infection risk and enhance wound management by tracking changes in wound pH. In this study, a machine learning-assisted analysis smart hydrogel as wound dressing was developed by utilizing a double cross-linked network hydrogel of gelatin methacrylate (GelMA) and chitosan methacrylate (CMCSMA) as the matrix, a compound of cobalt-gallic acid based metal-phenolic nanoparticles (GACo MPNs) as the active ingredients, and phenol red as the pH indicator. This smart hydrogel exhibits excellent injection performance, shape adaptability and mechanical strength. Besides, a series of in vitro experiments demonstrated the favorable biocompatibility and bioactivity of GelMA/CMCSMAP-GACo hydrogel, encompassing its antibacterial, anti-inflammatory, antioxidant, and angiogenic properties. In vivo experiments show that this hydrogel significantly improved the repair of diabetic wounds in mice. Interestingly, the hydrogel exhibited unique visual pH monitoring properties, which can be seamlessly integrated with a smartphone for image visualization and further enable reliable wound pH assessment using machine learning algorithms to enhance wound management based on wound pH. Overall, this study presented a comprehensive regenerative strategy for the management of diabetic wounds.