Self-Sterilizing Microneedle Sensing Patches for Machine Learning-Enabled Wound pH Visual Monitoring

Abstract

The skin, as the body's largest organ, is closely linked to an individual's health. Delayed diagnosis and treatment of skin infections can lead to complications such as non-healing wounds and sepsis. Despite significant, early identification of wound infections and timely treatment of non-healing wounds remain a challenge that requires continuous management. This work presents a novel strategy that combines smart microneedle sensing to inhibit wound infection and track wound healing status. The microneedle tip based on metal-organic frameworks (MOF) hydrogel allows rapid self-sterilization and promotes wound healing. The substrate of the microneedle patch based on pH–sensitive fluorescent reagents, can integrate with a smartphone to visualize images. Furthermore, it can be further reliably evaluated wound pH by applying a machine-learning algorithm. The multifunctional microneedle sensing patch establishes a strategy that combines therapy and sensing to address delayed wound management, promotes the design and optimization of MOF hydrogels, and contributes a facile way for disease diagnosis and personalized health management.