A modular fluorescent camera unit for wound imaging.

Journal: Communications biology
Published Date:

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.

Authors

  • Maryam Tebyani
    Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America.
  • Gordon Keller
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Wan Shen Hee
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Prabhat Baniya
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Alex Spaeth
    Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America.
  • Tiffany Nguyen
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Harika Dechiraju
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Anthony Gallegos
    Department of Dermatology, University of California, Davis, CA, 95616, USA.
  • Héctor Carrión
    Department of Computer Science and Engineering, University of California, Santa Cruz, California, United States of America.
  • Derek Hamersly
    Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Cristian Hernandez
    School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
  • Alexie Barbee
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Hao-Chieh Hsieh
    Department of Electrical and Computer Engineering, University of California Santa Cruz, Santa Cruz, CA, USA.
  • Elham Aslankoohi
    Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA.
  • Hsin-Ya Yang
    Department of Dermatology, University of California, Davis, Sacramento, California, United States of America.
  • Narges Norouzi
  • Min Zhao
    Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Alexander Sher
    Santa Cruz Institute for Particle Physics, University of California Santa Cruz, Santa Cruz, CA, USA.
  • R Rivkah Isseroff
    Department of Dermatology, School of Medicine, University of California Davis, Sacramento, CA, USA.
  • Marco Rolandi
    Department of Electrical and Computer Engineering, University of California, Santa Cruz, CA, 95064, USA.
  • Mircea Teodorescu
    Department of Electrical and Computer Engineering, University of California, Santa Cruz, Santa Cruz, California, United States of America.