文献类型：期刊文献

英文题名：Machine Learning-Assisted Real-Time Inflammation Monitoring and Optimal Treatment of Diabetic Wounds Based on a Ratiometric Fluorescent Sensing Peptide Hydrogel

作者：He, Le[1,2];Ge, Zhenghong[3];Teng, Runxin[3];Liu, Danqing[3];Zhang, Wenqing[3];Liu, Shangpeng[3];Hu, Wei[3];Tang, Junpeng[1];Zhou, Yuxiao[2];Sun, Min[1,2];Fan, Zhen[3];Du, Jianzhong[1,2,3]

机构：[1]East China Univ Sci & Technol, Minist Educ, Sch Mat Sci & Engn, Key Lab Ultrafine Mat, Shanghai 200237, Peoples R China;[2]Tongji Univ, Translat Res Inst Brain & Brain Like Intelligence, Shanghai Peoples Hosp 4,Dept Gynaecol & Obstet, Clin Res Ctr Anesthesiol & Perioperat Med,Shanghai, Shanghai 200434, Peoples R China;[3]Tongji Univ, Sch Mat Sci & Engn, Dept Polymer Mat, Shanghai 201804, Peoples R China

年份：2025

卷号：25

期号：35

起止页码：13284

外文期刊名：NANO LETTERS

收录：;EI(收录号：20253719140712);WOS:【SCI-EXPANDED(收录号:WOS:001555719800001)】；

基金：This research was supported by the National Natural Science Foundation of China (52222306, 22335005, 22475154, and 22305177), the international scientific collaboration fund of the Science and Technology Commission of Shanghai Municipality (23520710900), the Innovation Program of Shanghai Municipal Education Commission (2023ZKZD28), the Shanghai Rising-Star Program (Sailing, 23YF1433000), the Postdoctoral Fellowship Program of CPSF (GZC20250042), and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：hydrogel; peptide; wound healing; antibacterial

摘要：Managing inflammation in diabetic chronic wounds remains a major clinical challenge, primarily due to the lack of real-time monitoring techniques. To address this issue, we developed a peptide hydrogel capable of simultaneously monitoring the inflammation status and promoting healing. The hydrogel was formed by coassembling polyglutamic acid with polylysine that was premodified with coumarin 7 (an ACQ fluorophore) and tetraphenylethene (an AIE fluorophore). In the inflammatory microenvironment, released fluorophores undergo colorimetric changes that enable semiquantitative detection of reactive oxygen species (ROS). Leveraging this response, we imaged 1500 wound areas from mouse skin samples exhibiting varying ROS concentrations to create a training data set for the K-Nearest Neighbors (KNN) model, which allows real-time calculation of in situ ROS levels. Crucially, treatment strategies were dynamically adjusted based on such calculated ROS levels. Collectively, this system represents a promising approach for real-time inflammation monitoring and toward closed-loop therapy.