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Sarri B, Chevrier V, Poizat F, Heuke S, Franchi F, De Franqueville L, Traversari E, Ratone JP, Caillol F, Dahel Y, Hoibian S, Giovannini M, de Chaisemartin C, Appay R, Guasch G, Rigneault H. In vivo organoid growth monitoring by stimulated Raman histology. NPJ IMAGING 2024; 2:18. [PMID: 38948153 PMCID: PMC11213706 DOI: 10.1038/s44303-024-00019-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/21/2024] [Indexed: 07/02/2024]
Abstract
Patient-derived tumor organoids have emerged as a crucial tool for assessing the efficacy of chemotherapy and conducting preclinical drug screenings. However, the conventional histological investigation of these organoids necessitates their devitalization through fixation and slicing, limiting their utility to a single-time analysis. Here, we use stimulated Raman histology (SRH) to demonstrate non-destructive, label-free virtual staining of 3D organoids, while preserving their viability and growth. This novel approach provides contrast similar to conventional staining methods, allowing for the continuous monitoring of organoids over time. Our results demonstrate that SRH transforms organoids from one-time use products into repeatable models, facilitating the efficient selection of effective drug combinations. This advancement holds promise for personalized cancer treatment, allowing for the dynamic assessment and optimization of chemotherapy treatments in patient-specific contexts.
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Affiliation(s)
- Barbara Sarri
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
- Ligthcore Technologies, Marseille, France
| | | | - Flora Poizat
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Sandro Heuke
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
| | - Florence Franchi
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | | | | | - Jean-Philippe Ratone
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Fabrice Caillol
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Yanis Dahel
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Solène Hoibian
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Marc Giovannini
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
| | - Cécile de Chaisemartin
- Institut Paoli-Calmettes, Endoscopy and Gastroenterology Departement, Marseille, France
- Department of Surgical Oncology, Institut Paoli-Calmette, Marseille, France
| | - Romain Appay
- Aix Marseille Univ, CNRS, Neurophysiopathology Institute, Marseille, France
| | | | - Hervé Rigneault
- Aix Marseille Univ, CNRS, Centrale Med, Institut Fresnel, Marseille, France
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Tweel JED, Ecclestone BR, Boktor M, Dinakaran D, Mackey JR, Reza PH. Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy. IEEE Trans Biomed Eng 2024; 71:1901-1912. [PMID: 38231822 DOI: 10.1109/tbme.2024.3355296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
OBJECTIVE Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. However, the chemical labeling process is destructive and often irreversible or challenging to undo, imposing practical limits on the number of stains that can be applied to the same tissue section. Here we present an automated label-free whole slide scanner using a PARS microscope designed for imaging thin, transmissible samples. METHODS Peak SNR and in-focus acquisitions are achieved across entire tissue sections using the scattering signal from the PARS detection beam to measure the optimal focal plane. Whole slide images (WSI) are seamlessly stitched together using a custom contrast leveling algorithm. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. RESULTS PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. We show correspondence of subcellular diagnostic details in both PARS and H&E WSIs and demonstrate virtual H&E staining of an entire PARS WSI. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information. CONCLUSION PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. SIGNIFICANCE This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.
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Jin L, Tang Y, Coole JB, Tan MT, Zhao X, Badaoui H, Robinson JT, Williams MD, Vigneswaran N, Gillenwater AM, Richards-Kortum RR, Veeraraghavan A. DeepDOF-SE: affordable deep-learning microscopy platform for slide-free histology. Nat Commun 2024; 15:2935. [PMID: 38580633 PMCID: PMC10997797 DOI: 10.1038/s41467-024-47065-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 03/19/2024] [Indexed: 04/07/2024] Open
Abstract
Histopathology plays a critical role in the diagnosis and surgical management of cancer. However, access to histopathology services, especially frozen section pathology during surgery, is limited in resource-constrained settings because preparing slides from resected tissue is time-consuming, labor-intensive, and requires expensive infrastructure. Here, we report a deep-learning-enabled microscope, named DeepDOF-SE, to rapidly scan intact tissue at cellular resolution without the need for physical sectioning. Three key features jointly make DeepDOF-SE practical. First, tissue specimens are stained directly with inexpensive vital fluorescent dyes and optically sectioned with ultra-violet excitation that localizes fluorescent emission to a thin surface layer. Second, a deep-learning algorithm extends the depth-of-field, allowing rapid acquisition of in-focus images from large areas of tissue even when the tissue surface is highly irregular. Finally, a semi-supervised generative adversarial network virtually stains DeepDOF-SE fluorescence images with hematoxylin-and-eosin appearance, facilitating image interpretation by pathologists without significant additional training. We developed the DeepDOF-SE platform using a data-driven approach and validated its performance by imaging surgical resections of suspected oral tumors. Our results show that DeepDOF-SE provides histological information of diagnostic importance, offering a rapid and affordable slide-free histology platform for intraoperative tumor margin assessment and in low-resource settings.
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Affiliation(s)
- Lingbo Jin
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Yubo Tang
- Department of Bioengineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Jackson B Coole
- Department of Bioengineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Melody T Tan
- Department of Bioengineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Xuan Zhao
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Hawraa Badaoui
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, USA
| | - Jacob T Robinson
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX, USA
| | - Michelle D Williams
- Department of Pathology, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, USA
| | - Nadarajah Vigneswaran
- Department of Diagnostic and Biomedical Sciences, University of Texas Health Science Center at Houston School of Dentistry, 7500 Cambridge St, Houston, TX, USA
| | - Ann M Gillenwater
- Department of Head and Neck Surgery, University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, USA
| | | | - Ashok Veeraraghavan
- Department of Electrical and Computer Engineering, Rice University, 6100 Main St, Houston, TX, USA.
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Abraham TM, Levenson R. Current Landscape of Advanced Imaging Tools for Pathology Diagnostics. Mod Pathol 2024; 37:100443. [PMID: 38311312 DOI: 10.1016/j.modpat.2024.100443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 12/13/2023] [Accepted: 01/26/2024] [Indexed: 02/10/2024]
Abstract
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two-dimensional views. Emerging technologies promise to enhance and accelerate histopathology. Slide-free microscopy allows rapid imaging of fresh, unsectioned specimens, overcoming slide preparation delays. Methods such as fluorescence confocal microscopy, multiphoton microscopy, along with more recent innovations including microscopy with UV surface excitation and fluorescence-imitating brightfield imaging can generate images resembling conventional histology directly from the surface of tissue specimens. Slide-free microscopy enable applications such as rapid intraoperative margin assessment and, with appropriate technology, three-dimensional histopathology. Multiomics profiling techniques, including imaging mass spectrometry and Raman spectroscopy, provide highly multiplexed molecular maps of tissues, although clinical translation remains challenging. Artificial intelligence is aiding the adoption of new imaging modalities via virtual staining, which converts methods such as slide-free microscopy into synthetic brightfield-like or even molecularly informed images. Although not yet commonplace, these emerging technologies collectively demonstrate the potential to modernize histopathology. Artificial intelligence-assisted workflows will ease the transition to new imaging modalities. With further validation, these advances may transform the century-old conventional histopathology pipeline to better serve 21st-century medicine. This review provides an overview of these enabling technology platforms and discusses their potential impact.
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Affiliation(s)
- Tanishq Mathew Abraham
- Department of Biomedical Engineering, University of California, Davis, Davis, California
| | - Richard Levenson
- Department of Pathology and Laboratory Medicine, UC Davis Health, Sacramento, California.
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Li Y, Pillar N, Li J, Liu T, Wu D, Sun S, Ma G, de Haan K, Huang L, Zhang Y, Hamidi S, Urisman A, Keidar Haran T, Wallace WD, Zuckerman JE, Ozcan A. Virtual histological staining of unlabeled autopsy tissue. Nat Commun 2024; 15:1684. [PMID: 38396004 PMCID: PMC10891155 DOI: 10.1038/s41467-024-46077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.
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Affiliation(s)
- Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Nir Pillar
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Jingxi Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Di Wu
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Songyu Sun
- Computer Science Department, University of California, Los Angeles, CA, 90095, USA
| | - Guangdong Ma
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- School of Physics, Xi'an Jiaotong University, Xi'an, Shaanxi, 710049, China
| | - Kevin de Haan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA
| | - Sepehr Hamidi
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Anatoly Urisman
- Department of Pathology, University of California, San Francisco, CA, 94143, USA
| | - Tal Keidar Haran
- Department of Pathology, Hadassah Hebrew University Medical Center, Jerusalem, 91120, Israel
| | - William Dean Wallace
- Department of Pathology, Keck School of Medicine, University of Southern California, Los Angeles, CA, 90033, USA
| | - Jonathan E Zuckerman
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA.
- Bioengineering Department, University of California, Los Angeles, CA, 90095, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA.
- Department of Surgery, University of California, Los Angeles, CA, 90095, USA.
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Fakhoury JW, Lara JB, Manwar R, Zafar M, Xu Q, Engel R, Tsoukas MM, Daveluy S, Mehregan D, Avanaki K. Photoacoustic imaging for cutaneous melanoma assessment: a comprehensive review. JOURNAL OF BIOMEDICAL OPTICS 2024; 29:S11518. [PMID: 38223680 PMCID: PMC10785699 DOI: 10.1117/1.jbo.29.s1.s11518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 12/07/2023] [Accepted: 12/21/2023] [Indexed: 01/16/2024]
Abstract
Significance Cutaneous melanoma (CM) has a high morbidity and mortality rate, but it can be cured if the primary lesion is detected and treated at an early stage. Imaging techniques such as photoacoustic (PA) imaging (PAI) have been studied and implemented to aid in the detection and diagnosis of CM. Aim Provide an overview of different PAI systems and applications for the study of CM, including the determination of tumor depth/thickness, cancer-related angiogenesis, metastases to lymph nodes, circulating tumor cells (CTCs), virtual histology, and studies using exogenous contrast agents. Approach A systematic review and classification of different PAI configurations was conducted based on their specific applications for melanoma detection. This review encompasses animal and preclinical studies, offering insights into the future potential of PAI in melanoma diagnosis in the clinic. Results PAI holds great clinical potential as a noninvasive technique for melanoma detection and disease management. PA microscopy has predominantly been used to image and study angiogenesis surrounding tumors and provide information on tumor characteristics. Additionally, PA tomography, with its increased penetration depth, has demonstrated its ability to assess melanoma thickness. Both modalities have shown promise in detecting metastases to lymph nodes and CTCs, and an all-optical implementation has been developed to perform virtual histology analyses. Animal and human studies have successfully shown the capability of PAI to detect, visualize, classify, and stage CM. Conclusions PAI is a promising technique for assessing the status of the skin without a surgical procedure. The capability of the modality to image microvasculature, visualize tumor boundaries, detect metastases in lymph nodes, perform fast and label-free histology, and identify CTCs could aid in the early diagnosis and classification of CM, including determination of metastatic status. In addition, it could be useful for monitoring treatment efficacy noninvasively.
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Affiliation(s)
- Joseph W. Fakhoury
- Wayne State University School of Medicine, Detroit, Michigan, United States
| | - Juliana Benavides Lara
- University of Illinois at Chicago, Richard and Loan Hill Department of Bioengineering, Chicago, Illinois, United States
| | - Rayyan Manwar
- University of Illinois at Chicago, Richard and Loan Hill Department of Bioengineering, Chicago, Illinois, United States
| | - Mohsin Zafar
- University of Illinois at Chicago, Richard and Loan Hill Department of Bioengineering, Chicago, Illinois, United States
| | - Qiuyun Xu
- Wayne State University, Department of Biomedical Engineering, Detroit, Michigan, United States
| | - Ricardo Engel
- Wayne State University School of Medicine, Detroit, Michigan, United States
| | - Maria M. Tsoukas
- University of Illinois at Chicago, Department of Dermatology, Chicago, Illinois, United States
| | - Steven Daveluy
- Wayne State University School of Medicine, Department of Dermatology, Detroit, Michigan, United States
| | - Darius Mehregan
- Wayne State University School of Medicine, Department of Dermatology, Detroit, Michigan, United States
| | - Kamran Avanaki
- University of Illinois at Chicago, Richard and Loan Hill Department of Bioengineering, Chicago, Illinois, United States
- University of Illinois at Chicago, Department of Dermatology, Chicago, Illinois, United States
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Tweel JED, Ecclestone BR, Gaouda H, Dinakaran D, Wallace MP, Bigras G, Mackey JR, Reza PH. Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment. Curr Oncol 2023; 30:9760-9771. [PMID: 37999128 PMCID: PMC10670721 DOI: 10.3390/curroncol30110708] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/27/2023] [Accepted: 11/02/2023] [Indexed: 11/25/2023] Open
Abstract
Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis.
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Affiliation(s)
- James E. D. Tweel
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Benjamin R. Ecclestone
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Hager Gaouda
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Deepak Dinakaran
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Michael P. Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Gilbert Bigras
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2R3, Canada;
| | - John R. Mackey
- Illumisonics Inc., 22 King Street South, Suite 300, Waterloo, ON N2J 1N8, Canada; (D.D.); (J.R.M.)
| | - Parsin Haji Reza
- PhotoMedicine Labs, University of Waterloo, Waterloo, ON N2L 3G1, Canada; (J.E.D.T.); (B.R.E.); (H.G.)
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