材料科学
双层
纳米技术
模块化设计
生物传感器
生物医学工程
对偶(语法数字)
图层(电子)
计算机科学
医学
操作系统
文学类
艺术
作者
Ying Liu,Xiaomin Luo,Lijuan Chen,Zhilong Qiao,Si Chen,Xinhua Liu
标识
DOI:10.1002/adfm.202517521
摘要
Abstract Chronic wound management presents formidable clinical challenges due to compromised skin barrier function and pathological microenvironment characterized by persistent microbial infection, oxidative stress imbalance, and dysregulated inflammation. Traditional interventions inadequately address the need for real‐time monitoring and multi‐modal therapeutic integration. Here, a dual‐layer multifunctional bioelectronic microneedle patch (DMB‐MN) engineered with synergistic functionalities including localized photothermal antibacterial action is developed, intelligent thermoresponsive drug release, electrical stimulation, and pH‐actuated closed‐loop wound diagnostics. The photothermal platform achieves targeted hyperthermia (50.4 °C) under near‐infrared irradiation, demonstrating exceptional antimicrobial performance with 99.88% Staphylococcus aureus eradication and accelerated wound closure (99.78% contraction in 14 days). Mechanistic studies reveal the therapeutic triad's profound healing effects through oxidative stress mitigation (72.66% DPPH radical scavenging), collagen matrix restructuring, and vascular regeneration alongside inflammatory pathway modulation (39.51% TNF‐α reduction). Integrated biosensing components enable real‐time wound pH tracking with 86.650 mV decade −1 sensitivity, while maintaining biosafety parameters. This bioelectronic‐pharmacological DMB‐MN platform establishes an advantageous “diagnose‐actuate” therapeutic paradigm, synergistically combining critical chronic wound management modalities: antibacterial action, oxidative stress resolution, immunomodulation, and tissue regeneration guidance, which represents a transformative advance in precision chronic wound management through its closed‐loop monitoring‐treatment integration and bioelectronic control of healing microenvironments.
科研通智能强力驱动
Strongly Powered by AbleSci AI