Tebyani Maryam, Keller Gordon, Hee Wan Shen, Baniya Prabhat, Spaeth Alex, Nguyen Tiffany, Dechiraju Harika, Gallegos Anthony, Carrión Héctor, Hamersly Derek, Hernandez Cristian, Barbee Alexie, Hsieh Hao-Chieh, Aslankoohi Elham, Yang Hsin-Ya, Norouzi Narges, Zhao Min, Sher Alexander, Isseroff R Rivkah, Rolandi Marco, Teodorescu Mircea
Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA.
Commun Biol. 2025 Jul 5;8(1):1010. doi: 10.1038/s42003-025-08423-y.
Advanced imaging tools are revolutionizing the diagnosis, treatment, and monitoring of medical conditions, offering unprecedented insights into live cell behavior and biophysical markers. We introduce a modular, hand-held fluorescent microscope featuring rapid set-up and sub-millimeter resolution for real-time biological analysis. We apply our system to map pH and nitric oxide (NO), biomarkers central to wound healing, in subcutaneous wounds. Using machine learning to cluster pH reveals spatiotemporal trends, including a concentric gradient peaking at the center and stabilization at the wound edge. NO clustering shows high-concentration structures that decrease in size but intensify as healing progresses from hemostasis to proliferation, enabling prediction of the healing day and re-epithelialization. These biomarker mappings offer insights poised to inform future wound healing studies. This research lays the groundwork for integrating the modular imaging unit with bioelectronic devices in closed-loop feedback systems, using machine learning to guide optimal wound treatment and accelerate healing.
先进的成像工具正在彻底改变医疗状况的诊断、治疗和监测方式，为活细胞行为和生物物理标志物提供前所未有的见解。我们推出了一种模块化手持荧光显微镜，具有快速设置和亚毫米分辨率，用于实时生物分析。我们将我们的系统应用于绘制皮下伤口中pH值和一氧化氮（NO）的图谱，这两种生物标志物是伤口愈合的核心。使用机器学习对pH值进行聚类揭示了时空趋势，包括在中心达到峰值并在伤口边缘稳定的同心梯度。NO聚类显示出高浓度结构，其尺寸减小但随着愈合从止血阶段进展到增殖阶段而增强，从而能够预测愈合天数和重新上皮化。这些生物标志物图谱提供的见解有望为未来的伤口愈合研究提供信息。这项研究为在闭环反馈系统中将模块化成像单元与生物电子设备集成奠定了基础，利用机器学习来指导最佳伤口治疗并加速愈合。
