Machine Learning-Assisted Real-Time Inflammation Monitoring and Optimal Treatment of Diabetic Wounds Based on a...

Area of Science:

• Biomaterials Science
• Wound Healing Research
• Medical Diagnostics

Background:

• Diabetic chronic wounds present significant clinical challenges due to difficulties in managing inflammation.
• Lack of real-time monitoring techniques hinders effective inflammation management and wound healing.
• Reactive oxygen species (ROS) are key indicators of the inflammatory microenvironment in wounds.

Purpose of the Study:

• To develop a peptide hydrogel for simultaneous inflammation monitoring and wound healing promotion.
• To enable real-time detection and quantification of ROS in the wound microenvironment.
• To facilitate dynamic, closed-loop therapeutic adjustments based on inflammation status.

Main Methods:

• Coassembly of polyglutamic acid and modified polylysine (with coumarin 7 and tetraphenylethene) to form a peptide hydrogel.
• Utilizing fluorophore colorimetric changes in response to ROS for semiquantitative detection.
• Developing a K-Nearest Neighbors (KNN) model trained on 1500 mouse wound images for real-time in situ ROS level calculation.

Main Results:

• The developed hydrogel successfully monitors inflammation by detecting ROS through colorimetric changes.
• A KNN model was established for accurate, real-time calculation of in situ ROS levels.
• Dynamic adjustment of treatment strategies based on calculated ROS levels was demonstrated.

Conclusions:

• The peptide hydrogel system offers a promising tool for real-time inflammation monitoring in chronic wounds.
• This technology facilitates personalized, closed-loop therapeutic interventions for enhanced wound healing.
• The study highlights the potential of smart hydrogels in advancing wound care management.