Kim, WS, Khot, MI orcid.org/0000-0002-5062-2284, Woo, H-M et al. (8 more authors) (2022) AI-enabled, implantable, multichannel wireless telemetry for photodynamic therapy. Nature Communications, 13 (1). 2178. ISSN 2041-1723
Abstract
Photodynamic therapy (PDT) offers several advantages for treating cancers, but its efficacy is highly dependent on light delivery to activate a photosensitizer. Advances in wireless technologies enable remote delivery of light to tumors, but suffer from key limitations, including low levels of tissue penetration and photosensitizer activation. Here, we introduce DeepLabCut (DLC)-informed low-power wireless telemetry with an integrated thermal/light simulation platform that overcomes the above constraints. The simulator produces an optimized combination of wavelengths and light sources, and DLC-assisted wireless telemetry uses the parameters from the simulator to enable adequate illumination of tumors through high-throughput (<20 mice) and multi-wavelength operation. Together, they establish a range of guidelines for effective PDT regimen design. In vivo Hypericin and Foscan mediated PDT, using cancer xenograft models, demonstrates substantial suppression of tumor growth, warranting further investigation in research and/or clinical settings.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © The Author(s) 2022. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Medicine (Leeds) > Inst of Biomed & Clin Sciences (LIBACS) (Leeds) > Trans Anaesthetics & Surgical Sciences (Leeds) |
Funding Information: | Funder Grant number Wellcome Trust 204825/Z/16/Z |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Apr 2022 14:17 |
Last Modified: | 25 Jun 2023 22:57 |
Status: | Published |
Publisher: | Nature Research |
Identification Number: | 10.1038/s41467-022-29878-1 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:185970 |