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Wang S, Pan J, Zhang X, Li Y, Liu W, Lin R, Wang X, Kang D, Li Z, Huang F, Chen L, Chen J. Towards next-generation diagnostic pathology: AI-empowered label-free multiphoton microscopy. LIGHT, SCIENCE & APPLICATIONS 2024; 13:254. [PMID: 39277586 PMCID: PMC11401902 DOI: 10.1038/s41377-024-01597-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 08/04/2024] [Accepted: 08/21/2024] [Indexed: 09/17/2024]
Abstract
Diagnostic pathology, historically dependent on visual scrutiny by experts, is essential for disease detection. Advances in digital pathology and developments in computer vision technology have led to the application of artificial intelligence (AI) in this field. Despite these advancements, the variability in pathologists' subjective interpretations of diagnostic criteria can lead to inconsistent outcomes. To meet the need for precision in cancer therapies, there is an increasing demand for accurate pathological diagnoses. Consequently, traditional diagnostic pathology is evolving towards "next-generation diagnostic pathology", prioritizing on the development of a multi-dimensional, intelligent diagnostic approach. Using nonlinear optical effects arising from the interaction of light with biological tissues, multiphoton microscopy (MPM) enables high-resolution label-free imaging of multiple intrinsic components across various human pathological tissues. AI-empowered MPM further improves the accuracy and efficiency of diagnosis, holding promise for providing auxiliary pathology diagnostic methods based on multiphoton diagnostic criteria. In this review, we systematically outline the applications of MPM in pathological diagnosis across various human diseases, and summarize common multiphoton diagnostic features. Moreover, we examine the significant role of AI in enhancing multiphoton pathological diagnosis, including aspects such as image preprocessing, refined differential diagnosis, and the prognostication of outcomes. We also discuss the challenges and perspectives faced by the integration of MPM and AI, encompassing equipment, datasets, analytical models, and integration into the existing clinical pathways. Finally, the review explores the synergy between AI and label-free MPM to forge novel diagnostic frameworks, aiming to accelerate the adoption and implementation of intelligent multiphoton pathology systems in clinical settings.
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Affiliation(s)
- Shu Wang
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Junlin Pan
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
| | - Xiao Zhang
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Yueying Li
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
| | - Wenxi Liu
- College of Computer and Data Science, Fuzhou University, Fuzhou, 350108, China
| | - Ruolan Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Xingfu Wang
- Department of Pathology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350005, China
| | - Deyong Kang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Zhijun Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China
| | - Feng Huang
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China.
| | - Liangyi Chen
- New Cornerstone Laboratory, State Key Laboratory of Membrane Biology, Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Institute of Molecular Medicine, National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, 100091, China.
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, China.
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Maramraju S, Kowalczewski A, Kaza A, Liu X, Singaraju JP, Albert MV, Ma Z, Yang H. AI-organoid integrated systems for biomedical studies and applications. Bioeng Transl Med 2024; 9:e10641. [PMID: 38435826 PMCID: PMC10905559 DOI: 10.1002/btm2.10641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.
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Affiliation(s)
- Sudhiksha Maramraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Andrew Kowalczewski
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Anirudh Kaza
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace EngineeringSyracuse UniversitySyracuseNew YorkUSA
| | - Jathin Pranav Singaraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Mark V. Albert
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Department of Computer Science and EngineeringUniversity of North TexasDentonTexasUSA
| | - Zhen Ma
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Huaxiao Yang
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
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Gómez-de-Mariscal E, Del Rosario M, Pylvänäinen JW, Jacquemet G, Henriques R. Harnessing artificial intelligence to reduce phototoxicity in live imaging. J Cell Sci 2024; 137:jcs261545. [PMID: 38324353 PMCID: PMC10912813 DOI: 10.1242/jcs.261545] [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] [Indexed: 02/08/2024] Open
Abstract
Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.
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Affiliation(s)
| | | | - Joanna W. Pylvänäinen
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
| | - Guillaume Jacquemet
- Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, Turku 20500, Finland
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, Turku 20520, Finland
- Turku Bioimaging, University of Turku and Åbo Akademi University, Turku 20520, Finland
- InFLAMES Research Flagship Center, Åbo Akademi University, Turku 20100, Finland
| | - Ricardo Henriques
- Instituto Gulbenkian de Ciência, Oeiras 2780-156, Portugal
- UCL Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK
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Hernández IC, Yau J, Rishøj L, Cui N, Minderler S, Jowett N. Tutorial: multiphoton microscopy to advance neuroscience research. Methods Appl Fluoresc 2023; 11. [PMID: 36753763 DOI: 10.1088/2050-6120/acba66] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/08/2023] [Indexed: 02/10/2023]
Abstract
Multiphoton microscopy (MPM) employs ultrafast infrared lasers for high-resolution deep three-dimensional imaging of live biological samples. The goal of this tutorial is to provide a practical guide to MPM imaging for novice microscopy developers and life-science users. Principles of MPM, microscope setup, and labeling strategies are discussed. Use of MPM to achieve unprecedented imaging depth of whole mounted explants and intravital imaging via implantable glass windows of the mammalian nervous system is demonstrated.
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Affiliation(s)
- Iván Coto Hernández
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Jenny Yau
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Lars Rishøj
- Technical University of Denmark, DTU Electro, Ørsteds Plads 343, 2800 Kgs. Lyngby, Denmark
| | - Nanke Cui
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Steven Minderler
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
| | - Nate Jowett
- Surgical Photonics & Engineering Laboratory, Mass Eye and Ear, Harvard Medical School, 243 Charles St, Boston, MA, United States of America
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Baldi PF, Abdelkarim S, Liu J, To JK, Ibarra MD, Browne AW. Vitreoretinal Surgical Instrument Tracking in Three Dimensions Using Deep Learning. Transl Vis Sci Technol 2023; 12:20. [PMID: 36648414 PMCID: PMC9851279 DOI: 10.1167/tvst.12.1.20] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
Purpose To evaluate the potential for artificial intelligence-based video analysis to determine surgical instrument characteristics when moving in the three-dimensional vitreous space. Methods We designed and manufactured a model eye in which we recorded choreographed videos of many surgical instruments moving throughout the eye. We labeled each frame of the videos to describe the surgical tool characteristics: tool type, location, depth, and insertional laterality. We trained two different deep learning models to predict each of the tool characteristics and evaluated model performances on a subset of images. Results The accuracy of the classification model on the training set is 84% for the x-y region, 97% for depth, 100% for instrument type, and 100% for laterality of insertion. The accuracy of the classification model on the validation dataset is 83% for the x-y region, 96% for depth, 100% for instrument type, and 100% for laterality of insertion. The close-up detection model performs at 67 frames per second, with precision for most instruments higher than 75%, achieving a mean average precision of 79.3%. Conclusions We demonstrated that trained models can track surgical instrument movement in three-dimensional space and determine instrument depth, tip location, instrument insertional laterality, and instrument type. Model performance is nearly instantaneous and justifies further investigation into application to real-world surgical videos. Translational Relevance Deep learning offers the potential for software-based safety feedback mechanisms during surgery or the ability to extract metrics of surgical technique that can direct research to optimize surgical outcomes.
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Affiliation(s)
- Pierre F. Baldi
- Department of Computer Science, University of California, Irvine, CA, USA,Institute for Genomics and Bioinformatics, University of California, Irvine, CA, USA,Department of Biomedical Engineering, University of California, Irvine, CA, USA,Center for Translational Vision Research, Department of Ophthalmology, University of California, Irvine, CA, USA
| | - Sherif Abdelkarim
- Department of Computer Science, University of California, Irvine, CA, USA,Institute for Genomics and Bioinformatics, University of California, Irvine, CA, USA
| | - Junze Liu
- Department of Computer Science, University of California, Irvine, CA, USA,Institute for Genomics and Bioinformatics, University of California, Irvine, CA, USA
| | - Josiah K. To
- Center for Translational Vision Research, Department of Ophthalmology, University of California, Irvine, CA, USA
| | | | - Andrew W. Browne
- Department of Biomedical Engineering, University of California, Irvine, CA, USA,Center for Translational Vision Research, Department of Ophthalmology, University of California, Irvine, CA, USA,Gavin Herbert Eye Institute, Department of Ophthalmology, University of California, Irvine, CA, USA
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Hilzenrat G, Gill ET, McArthur SL. Imaging approaches for monitoring three-dimensional cell and tissue culture systems. JOURNAL OF BIOPHOTONICS 2022; 15:e202100380. [PMID: 35357086 DOI: 10.1002/jbio.202100380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 06/14/2023]
Abstract
The past decade has seen an increasing demand for more complex, reproducible and physiologically relevant tissue cultures that can mimic the structural and biological features of living tissues. Monitoring the viability, development and responses of such tissues in real-time are challenging due to the complexities of cell culture physical characteristics and the environments in which these cultures need to be maintained in. Significant developments in optics, such as optical manipulation, improved detection and data analysis, have made optical imaging a preferred choice for many three-dimensional (3D) cell culture monitoring applications. The aim of this review is to discuss the challenges associated with imaging and monitoring 3D tissues and cell culture, and highlight topical label-free imaging tools that enable bioengineers and biophysicists to non-invasively characterise engineered living tissues.
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Affiliation(s)
- Geva Hilzenrat
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Emma T Gill
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
| | - Sally L McArthur
- Bioengineering Engineering Group, School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, Victoria, Australia
- Biomedical Manufacturing, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Clayton, Victoria, Australia
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So WZ, Teo RZC, Ooi LY, Goh BYS, Lu J, Vathsala A, Thamboo TP, Tiong HY. Multi-photon microscopy for the evaluation of interstitial fibrosis in extended criteria donor kidneys: A Proof-of-Concept Study. Clin Transplant 2022; 36:e14717. [PMID: 35598116 DOI: 10.1111/ctr.14717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 11/03/2022]
Abstract
INTRODUCTION To evaluate the initial use of label-free second harmonic generation (SHG) imaging with two-photon excitation (2PE) auto-fluorescence in multi-photon microscopy (MPM) for the quantification of collagen/fibrosis on pre-implantation biopsies of extended criteria donors (ECD). MATERIALS AND METHODS 20 pre-implantation core biopsies were extracted from 10 donor kidney samples, of which originated from 7 donors. Kidney Donor Profile Index (KDPI) and Remuzzi scores of biopsies were calculated. Collagen parameters measured included quantification by the Collagen Area Ratio in Total Tissue (CART) and qualitative measurements by Collagen Reticulation Index (CRI). RESULTS Biopsies classified with > 85% KDPI scores had significantly higher CART (p = 0.011) and lower CRI values (p = 0.025) than biopsies with ≤ 85% KDPI scores. Increase in CRI values correlated significantly with rise in recipient creatinine levels 1-year post-transplant (p = 0.027; 95% CI: 4.635-66.797). CONCLUSION MPM is an evolving technology that enables the quantification of the amount (CART) and quality (CRI) of collagen deposition in unstained pre-implantation biopsies of donor kidneys stratified by KDPI scores. This initial evaluation found significant differences in both parameters between donor kidneys with more or less than 85% KDPI. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Wei Zheng So
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore
| | - Rachel Zui Chih Teo
- Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Li Yin Ooi
- Department of Pathology, National University Hospital, Singapore, Singapore
| | - Benjamin Yen Seow Goh
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.,Department of Urology, National University Hospital, Singapore, Singapore.,National University Centre for Organ Transplantation, National University Hospital, Singapore, Singapore
| | - Jirong Lu
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.,Department of Urology, National University Hospital, Singapore, Singapore.,National University Centre for Organ Transplantation, National University Hospital, Singapore, Singapore
| | - Anantharaman Vathsala
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.,Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore.,National University Centre for Organ Transplantation, National University Hospital, Singapore, Singapore
| | - Thomas Paulraj Thamboo
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.,Division of Nephrology, Department of Medicine, National University Hospital, Singapore, Singapore
| | - Ho Yee Tiong
- Yong Loo Lin School of Medicine, National University Singapore, Singapore, Singapore.,Department of Urology, National University Hospital, Singapore, Singapore.,National University Centre for Organ Transplantation, National University Hospital, Singapore, Singapore
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Bishop KW, Maitland KC, Rajadhyaksha M, Liu JTC. In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220032-PER. [PMID: 35478042 PMCID: PMC9043840 DOI: 10.1117/1.jbo.27.4.040601] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 04/05/2022] [Indexed: 05/05/2023]
Abstract
SIGNIFICANCE There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, commercialized products, and publications have resulted from these efforts, mainstream clinical adoption has been relatively slow other than for a few clinical applications (e.g., dermatology). AIM Here, our goals are threefold: (1) to introduce and motivate the need for in vivo microscopy (IVM) as an adjunctive tool for clinical detection, diagnosis, and treatment, (2) to discuss the key translational challenges facing the field, and (3) to propose best practices and recommendations to facilitate clinical adoption. APPROACH We will provide concrete examples from various clinical domains, such as dermatology, oral/gastrointestinal oncology, and neurosurgery, to reinforce our observations and recommendations. RESULTS While the incremental improvement and optimization of IVM technologies should and will continue to occur, future translational efforts would benefit from the following: (1) integrating clinical and industry partners upfront to define and maintain a compelling value proposition, (2) identifying multimodal/multiscale imaging workflows, which are necessary for success in most clinical scenarios, and (3) developing effective artificial intelligence tools for clinical decision support, tempered by a realization that complete adoption of such tools will be slow. CONCLUSIONS The convergence of imaging modalities, academic-industry-clinician partnerships, and new computational capabilities has the potential to catalyze rapid progress and adoption of IVM in the next few decades.
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Affiliation(s)
- Kevin W. Bishop
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
| | - Kristen C. Maitland
- Texas A&M University, Department of Biomedical Engineering, College Station, Texas, United States
| | - Milind Rajadhyaksha
- Memorial Sloan Kettering Cancer Center, Dermatology Service, New York, New York, United States
| | - Jonathan T. C. Liu
- University of Washington, Department of Bioengineering, Seattle, Washington, United States
- University of Washington, Department of Mechanical Engineering, Seattle, Washington, United States
- University of Washington, Department of Laboratory Medicine and Pathology, Seattle, Washington, United States
- Address all correspondence to Jonathan T.C. Liu,
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