A Miniaturized, Battery-Free, Wireless Wound Monitor That Predicts Wound Closure Rate Early

Abstract

Diabetic foot ulcers are chronic wounds that affect millions and increase the risk of amputation and mortality, highlighting the critical need for their early detection. Recent demonstrations of wearable sensors enable real-time wound assessment, but they rely on bulky electronics, making them difficult to interface with wounds. Herein, a miniaturized, wireless, battery-free wound monitor that measures lactate in real-time and seamlessly integrates with bandages for conformal attachment to the wound bed is introduced. Lactate is selected due to its multifaceted role in initiating healing. Studies in healthy and diabetic mice reveal distinct lactate profiles for normal and impaired healing wounds. A mathematical model based on the sensor data predicts wound closure rate within the first 3 days post-injury with ≈76% accuracy, which increases to ≈83% when pH is included. These studies underscore the significance of monitoring biomarkers during the inflammation phase, which can offer several benefits, including short-term use of wound monitors and their easy removal, resulting in lower risks of injury and infection at the wound site. Improvements in prediction accuracy can be achieved by designing mathematical models that build on multiple wound parameters such as pro-inflammatory and metabolic markers. Achieving this goal will require designing multi-analyte wound monitors.

Keywords: chronic wounds; diabetic ulcers; lactate sensing; wireless electronics; wound sensing.

Figure 1.

Wireless, battery-free wound lactate monitor for predicting wound closure. Schematic representation showing (A) the wireless monitoring of wound lactate and its potential use in identifying impaired healing and (B) exploded view of the wireless sensor. Image showing (C) the NFC-based, battery-free wireless electronics and (D) the complete system. (C, D) Scale bar: 5 mm.

Figure 2.

Key components of biofuel cell-based lactate sensor and its in vitro characterization. (A) Schematic representation showing the key components of the lactate sensor. (B) Image of the lactate sensor. Scale bar 2 mm. (C, D) Reversibility of the lactate sensor signal. (E) Real-time response of the lactate sensor to increasing concentrations and (F) the corresponding calibration plot. (G) Effect of common interfering biochemicals on sensor response. (H) Effect of the UV protective coating on sensor response before and after UV sterilization. Red: without UV protective membrane; Blue: with UV protective membrane (I) Sensor stability studied over a period of 10 days at ambient conditions. (J) Effect of biofouling using artificial wound fluid studied over 7 days. (G-J) Data presented as mean ± S.D., n=3.

Figure 3.

In vivo biocompatibility of the lactate sensor. (A) Live/dead staining assay of mouse fibroblasts (L929) after 4 days of culture. PC: positive control (no sensor). Scale bar: 500μm. (B) Normalized viability assay data (data presented as mean ± S.D., n=5) (C) Effect of attached sensor on wound closure process. The inner diameter of the splint is 10mm. Quantitative analysis of wound closure with and without sensor for wounds in (D) healthy and (E) diabetic mice. Data presented as mean ± S.D., n=6. (F) Histology of healthy and diabetic mouse with and without the sensor attached after day 10 (partial wound closure) and day 30 (complete wound closure). Scale bar=100 μm.

Figure 4.

In vivo studies of the lactate sensor using a mouse model of full thickness dermal wound. (A) Scheme showing the setup for acquiring data using wired sensors (B) Photo of a mouse with the sensor attached onto the wound. Scale bar: 1 cm. (C) Measured lactate levels and % wound closure in healthy and diabetic mice by time (data presented as mean ± S.D., n=10 for each cohort). (D) Relationship between % wound closure on day 10 and lactate level on day 3 of wounding. The pink region shows normal wound lactate range. The blue dotted horizontal line separates wounds that heal normally (wound closure rate > 70% by day 10) from those that show impaired healing (wound closure rate < 70% by day 10). (E) pH levels measured in wounds in healthy and diabetic mice over a period of 4 days (data presented as mean ± S.D, n=6 for each cohort).

Figure 5.

A mathematical model for predicting wound closure rate. (A) t-SNE visualization of the lactate time series in diabetic group (blue) and healthy group (black). Outliers marked in red circle. (B) Mean classification accuracy of wound closure prediction using each type of feature. Lac.: only lactate time series; Freq: frequency features of the lactate sequence obtained via FFT; All: Lac. + Freq. (C) Combining pH features and lactate features increases the prediction accuracy compared to using only lactate features or pH. (B,C) Data presented as mean ± S.D., n=5.

Figure 6.

In vitro and in vivo evaluation of wireless, battery-free wound lactate monitor. (A) Functional block diagram showing the working principle of the wireless electronics for power and data transfer. (B) Correlation between signal output from sensor and that measured by the wireless device (data presented as mean ± S.D., n=3). Effect of performance of the device based on its (C) relative inclination with respect to the transmitter antenna (data presented as mean ± S.D., n=3) and (D) spatial location inside the experimental arena. (E) Photo of the wireless sensor attached to a wound in a diabetic mouse. Scale bar: 5 mm. (F) Daily monitoring of wound lactate in a diabetic mouse over 4 days (data presented as mean ± S.D., n=60).