A modular fluorescent camera unit for wound imaging

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.

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Publication Details

Journal
Communications Biology
Published
2025-07-05
DOI
https://doi.org/10.1038/s42003-025-08423-y
Primary Topic
Wound Healing and Treatments
Type
article
Field-Weighted Citation Impact
0.00

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article

A modular fluorescent camera unit for wound imaging

Wan Shen Hee, Min Zhao, Anthony Gallegos, Marco Rolandi et al.
Communications Biology
Wound Healing and Treatments
article

A modular fluorescent camera unit for wound imaging

Wan Shen Hee, Min Zhao, Anthony Gallegos, Marco Rolandi, Mircea Teodorescu, R. Rivkah Isseroff, Cristián E. Hernández, Tiffany Nguyen, Harika Dechiraju, Alex Spaeth, Elham Aslankoohi, Alexander Sher, Héctor Carrión, Maryam Tebyani, Hao‐Chieh Hsieh, Gordon Keller, Prabhat Baniya, Hsin‐ya Yang, Narges Norouzi, Alexie Barbee, Derek Hamersly
article en

Abstract

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.

Communications BiologyVol. 8(1)
University of California, Santa Cruz (US), University of California, Davis (US), University of California, Berkeley (US)
U.S. Department of the Interior, Defense Advanced Research Projects Agency, Advanced Research Projects Agency, Interior Business Center
Good health and well-being
Openalex Percentile: Top 79%
Wound Healing and Treatments
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