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A microfluidic wearable device for wound exudate management and analysis in human chronic wounds

渗出液 慢性伤口 伤口愈合 生物医学工程 医学 微流控 可穿戴计算机 外科 纳米技术 病理 计算机科学 材料科学 嵌入式系统
作者
Canran Wang,Kexin Fan,Ehsan Shirzaei Sani,José A. Lasalde‐Ramírez,Wenzheng Heng,Jihong Min,Samuel A. Solomon,Minqiang Wang,Jiahong Li,Hong Han,Gwangmook Kim,Shik Shin,Alex Seder,Chia-Ding Shih,David G. Armstrong,Wei Gao
出处
期刊:Science Translational Medicine [American Association for the Advancement of Science]
卷期号:17 (795)
标识
DOI:10.1126/scitranslmed.adt0882
摘要

Chronic wounds are a major global health challenge associated with substantial economic burden and a negative impact on patient quality of life. Real-time analysis of biomarkers like reactive oxygen and nitrogen species could guide treatment, but existing systems lack the capacity required for continuous monitoring. Wound exudate is secreted slowly and has a complex composition, making efficient fluid collection and real-time analysis challenging. To address these issues, we introduce iCares, a wearable device for wound exudate management and continuous in situ analysis of wound biomarkers. iCares contains a flexible nanoengineered sensor array that measures reactive species such as NO, H 2 O 2 , and O 2 , along with pH and temperature, providing multiparameter data to inform wound status. The device features pump-free triad microfluidic modules with a superhydrophobic-superhydrophilic Janus membrane, bioinspired wedge channels, and three-dimensional graded micropillars for efficient unidirectional exudate collection, transport, and refreshing. The sensors demonstrated a consistent response and analyte selectivity in vitro and in wound exudate. iCares was designed for rapid scalable manufacturing through advanced printing and laser patterning. Wireless connectivity supported long-term continuous monitoring in wounds. The iCares system real-time monitoring was tested in a murine model of diabetic skin wound during infection and antimicrobial treatment. Clinical wound evaluation was conducted in 20 patients with chronic wounds and in two patients before and after surgery. A machine learning analysis of the multiplexed data successfully classified wounds and healing times, indicating that wound exudate analysis by iCares could offer insight into chronic wound status to aid in treatment decisions.
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