Chronic wounds arise from interruption of normal healing due to many potential pathophysiological factors. Monitoring these multivariate factors can provide personalized diagnostic information for wound management, but current sensing technologies use complex laboratory tests or track a limited number of wound parameters. We report a flexible biosensing platform for multiplexed profiling of the wound microenvironment, inflammation, and infection state at the point of care. This platform integrates a sensor array for measuring inflammatory mediators [tumor necrosis factor–α, interleukin-6 (IL-6), IL-8, and transforming growth factor–β1], microbial burden (Staphylococcus aureus), and physicochemical parameters (temperature and pH) with a microfluidic wound exudate collector and flexible electronics for wireless, smartphone-based data readout. We demonstrate in situ multiplexed monitoring in a mouse wound model and also profile wound exudates from patients with venous leg ulcers. This technology may facilitate more timely and personalized wound management to improve chronic wound healing outcomes.

A wireless sensor based on bioresponsive DNA hydrogel provides smartphone-based detection of wound infection.

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.

Enterococcus faecalis is one of the most frequently isolated bacterial species in wounds yet little is known about its pathogenic mechanisms in this setting. Here, we used a mouse wound excisional model to characterize the infection dynamics of E faecalis and show that infected wounds result in 2 different states depending on the initial inoculum. Low-dose inocula were associated with short-term, low-titer colonization whereas high-dose inocula were associated with acute bacterial replication and long-term persistence. High-dose infection and persistence were also associated with immune cell infiltration, despite suppression of some inflammatory cytokines and delayed wound healing. During high-dose infection, the multiple peptide resistance factor, which is involved in resisting immune clearance, contributes to E faecalis fitness. These results comprehensively describe a mouse model for investigating E faecalis wound infection determinants, and suggest that both immune modulation and resistance contribute to persistent, nonhealing wounds.