Battery-free and AI-enabled multiplexed sensor patches for wound monitoring.

Wound healing is a dynamic process with multiple phases. Rapid profiling and quantitative characterization of inflammation and infection remain challenging. We report a paper-like battery-free in situ AI-enabled multiplexed (PETAL) sensor for holistic wound assessment by leveraging deep learning algorithms. This sensor consists of a wax-printed paper panel with five colorimetric sensors for temperature, pH, trimethylamine, uric acid, and moisture. Sensor images captured by a mobile phone were analyzed by neural network-based machine learning algorithms to determine healing status. For ex situ detection via exudates collected from rat perturbed wounds and burn wounds, the PETAL sensor can classify healing versus nonhealing status with an accuracy as high as 97%. With the sensor patches attached on rat burn wound models, in situ monitoring of wound progression or severity is demonstrated. This PETAL sensor allows early warning of adverse events, which could trigger immediate clinical intervention to facilitate wound care management.

Carbon Dot-Doped Hydrogel Sensor Array for Multiplexed Colorimetric Detection of Wound Healing.

Effective wound care and treatment require a quick and comprehensive assessment of healing status. Here, we develop a carbon dot-doped hydrogel sensor array in polydimethylsiloxane (PDMS) for simultaneous colorimetric detections of five wound biomarkers and/or wound condition indicators (pH, glucose, urea, uric acid, and total protein), leading to the holistic assessment of inflammation and infection. A biogenic carbon dot synthesized using an amino acid and a polymer precursor is doped in an agarose hydrogel matrix for constructing enzymatic sensors (glucose, urea, and uric acid) and dye-based sensors (pH and total protein). The encapsulated enzymes in such a matrix exhibit improved enzyme kinetics and stability compared to those in pure hydrogels. Such a matrix also provides stable colorimetric responses for all five sensors. The sensor array exhibits high accuracy (recovery rates of 91.5-113.1%) and clinically relevant detection ranges for all five wound markers. The sensor array is established for simulated wound fluids and validated with rat wound fluids from perturbed wound models. Distinct color patterns are obtained that can clearly distinguish healing vs nonhealing wounds visually and quantitatively. This hydrogel sensor array shows great potential for on-site wound sensing due to its long-term stability, lightweight, and flexibility.