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Rahimnejad M, Makkar H, Dal-Fabbro R, Malda J, Sriram G, Bottino MC. Biofabrication Strategies for Oral Soft Tissue Regeneration. Adv Healthc Mater 2024; 13:e2304537. [PMID: 38529835 PMCID: PMC11254569 DOI: 10.1002/adhm.202304537] [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: 12/19/2023] [Revised: 03/01/2024] [Indexed: 03/27/2024]
Abstract
Gingival recession, a prevalent condition affecting the gum tissues, is characterized by the exposure of tooth root surfaces due to the displacement of the gingival margin. This review explores conventional treatments, highlighting their limitations and the quest for innovative alternatives. Importantly, it emphasizes the critical considerations in gingival tissue engineering leveraging on cells, biomaterials, and signaling factors. Successful tissue-engineered gingival constructs hinge on strategic choices such as cell sources, scaffold design, mechanical properties, and growth factor delivery. Unveiling advancements in recent biofabrication technologies like 3D bioprinting, electrospinning, and microfluidic organ-on-chip systems, this review elucidates their precise control over cell arrangement, biomaterials, and signaling cues. These technologies empower the recapitulation of microphysiological features, enabling the development of gingival constructs that closely emulate the anatomical, physiological, and functional characteristics of native gingival tissues. The review explores diverse engineering strategies aiming at the biofabrication of realistic tissue-engineered gingival grafts. Further, the parallels between the skin and gingival tissues are highlighted, exploring the potential transfer of biofabrication approaches from skin tissue regeneration to gingival tissue engineering. To conclude, the exploration of innovative biofabrication technologies for gingival tissues and inspiration drawn from skin tissue engineering look forward to a transformative era in regenerative dentistry with improved clinical outcomes.
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Affiliation(s)
- Maedeh Rahimnejad
- Department of Cariology, Restorative Sciences, and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Hardik Makkar
- Faculty of Dentistry, National University of Singapore, Singapore
| | - Renan Dal-Fabbro
- Department of Cariology, Restorative Sciences, and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
| | - Jos Malda
- Regenerative Medicine Center Utrecht, Utrecht, The Netherlands
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands
- Department of Orthopedics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gopu Sriram
- Faculty of Dentistry, National University of Singapore, Singapore
- NUS Centre for Additive Manufacturing (AM.NUS), National University of Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Marco C. Bottino
- Department of Cariology, Restorative Sciences, and Endodontics, School of Dentistry, University of Michigan, Ann Arbor, MI, USA
- Department of Biomedical Engineering, College of Engineering, University of Michigan, Ann Arbor, MI, USA
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2
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Wei ML, Tada M, So A, Torres R. Artificial intelligence and skin cancer. Front Med (Lausanne) 2024; 11:1331895. [PMID: 38566925 PMCID: PMC10985205 DOI: 10.3389/fmed.2024.1331895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Accepted: 02/26/2024] [Indexed: 04/04/2024] Open
Abstract
Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.
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Affiliation(s)
- Maria L. Wei
- Department of Dermatology, University of California, San Francisco, San Francisco, CA, United States
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
| | - Mikio Tada
- Institute for Neurodegenerative Diseases, University of California, San Francisco, San Francisco, CA, United States
| | - Alexandra So
- School of Medicine, University of California, San Francisco, San Francisco, CA, United States
| | - Rodrigo Torres
- Dermatology Service, San Francisco VA Health Care System, San Francisco, CA, United States
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3
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Bishop KW, Hu B, Vyawhare R, Yang Z, Liang DC, Gao G, Baraznenok E, Han Q, Lan L, Chow SSL, Sanai N, Liu JTC. Miniature line-scanned dual-axis confocal microscope for versatile clinical use. BIOMEDICAL OPTICS EXPRESS 2023; 14:6048-6059. [PMID: 38021137 PMCID: PMC10659777 DOI: 10.1364/boe.503478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/17/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023]
Abstract
A miniature optical-sectioning fluorescence microscope with high sensitivity and resolution would enable non-invasive and real-time tissue inspection, with potential use cases including early disease detection and intraoperative guidance. Previously, we developed a miniature MEMS-based dual-axis confocal (DAC) microscope that enabled video-rate optically sectioned in vivo microscopy of human tissues. However, the device's clinical utility was limited due to a small field of view, a non-adjustable working distance, and a lack of a sterilization strategy. In our latest design, we have made improvements to achieve a 2x increase in the field of view (600 × 300 µm) and an adjustable working distance range of 150 µm over a wide range of excitation/emission wavelengths (488-750 nm), all while maintaining a high frame rate of 15 frames per second (fps). Furthermore, the device is designed to image through a disposable sterile plastic drape for convenient clinical use. We rigorously characterize the performance of the device and show example images of ex vivo tissues to demonstrate the optical performance of our new design, including fixed mouse skin and human prostate, as well as fresh mouse kidney, mouse intestine, and human head and neck surgical specimens with corresponding H&E histology. These improvements will facilitate clinical testing and translation.
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Affiliation(s)
- Kevin W. Bishop
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Bingwen Hu
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Rajat Vyawhare
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Zelin Yang
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - David C. Liang
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Gan Gao
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Elena Baraznenok
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Qinghua Han
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
| | - Lydia Lan
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Biology, University of Washington, Seattle, Washington 98195, USA
| | - Sarah S. L. Chow
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
| | - Nader Sanai
- Ivy Brain Tumor Center, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix 85013, AZ, USA
- Department of Neurosurgery, Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix 85013, AZ, USA
| | - Jonathan T. C. Liu
- Department of Bioengineering, University of Washington, Seattle, WA 98195, USA
- Department of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA
- Department of Laboratory Medicine & Pathology, University of Washington, Seattle, WA 98195, USA
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4
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Mandal A, Priyam S, Chan HH, Gouveia BM, Guitera P, Song Y, Baker MAB, Vafaee F. Computer-Aided Diagnosis of Melanoma Subtypes Using Reflectance Confocal Images. Cancers (Basel) 2023; 15:1428. [PMID: 36900219 PMCID: PMC10000703 DOI: 10.3390/cancers15051428] [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: 01/11/2023] [Revised: 02/16/2023] [Accepted: 02/20/2023] [Indexed: 03/03/2023] Open
Abstract
Lentigo maligna (LM) is an early form of pre-invasive melanoma that predominantly affects sun-exposed areas such as the face. LM is highly treatable when identified early but has an ill-defined clinical border and a high rate of recurrence. Atypical intraepidermal melanocytic proliferation (AIMP), also known as atypical melanocytic hyperplasia (AMH), is a histological description that indicates melanocytic proliferation with uncertain malignant potential. Clinically and histologically, AIMP can be difficult to distinguish from LM, and indeed AIMP may, in some cases, progress to LM. The early diagnosis and distinction of LM from AIMP are important since LM requires a definitive treatment. Reflectance confocal microscopy (RCM) is an imaging technique often used to investigate these lesions non-invasively, without biopsy. However, RCM equipment is often not readily available, nor is the associated expertise for RCM image interpretation easy to find. Here, we implemented a machine learning classifier using popular convolutional neural network (CNN) architectures and demonstrated that it could correctly classify lesions between LM and AIMP on biopsy-confirmed RCM image stacks. We identified local z-projection (LZP) as a recent fast approach for projecting a 3D image into 2D while preserving information and achieved high-accuracy machine classification with minimal computational requirements.
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Affiliation(s)
- Ankita Mandal
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- Department of Mechanical Engineering, Indian Institute of Technology (IIT Delhi), Delhi 110016, India
| | - Siddhaant Priyam
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- Department of Electrical Engineering, Indian Institute of Technology (IIT Delhi), Delhi 110016, India
| | - Hsien Herbert Chan
- Department of Dermatology, Princess Alexandra Hospital, Brisbane 4102, Australia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Bruna Melhoranse Gouveia
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Pascale Guitera
- Sydney Melanoma Diagnostic Centre, Royal Prince Alfred Hospital, Sydney 2006, Australia
- Melanoma Institute Australia, The University of Sydney, Sydney 2006, Australia
| | - Yang Song
- School of Computer Science and Engineering, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
| | | | - Fatemeh Vafaee
- School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
- UNSW Data Science Hub, University of New South Wales (UNSW Sydney), Sydney 2052, Australia
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5
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Sahu A, Kose K, Kraehenbuehl L, Byers C, Holland A, Tembo T, Santella A, Alfonso A, Li M, Cordova M, Gill M, Fox C, Gonzalez S, Kumar P, Wang AW, Kurtansky N, Chandrani P, Yin S, Mehta P, Navarrete-Dechent C, Peterson G, King K, Dusza S, Yang N, Liu S, Phillips W, Guitera P, Rossi A, Halpern A, Deng L, Pulitzer M, Marghoob A, Chen CSJ, Merghoub T, Rajadhyaksha M. In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response. Nat Commun 2022; 13:5312. [PMID: 36085288 PMCID: PMC9463451 DOI: 10.1038/s41467-022-32738-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Accepted: 08/12/2022] [Indexed: 12/03/2022] Open
Abstract
Response to immunotherapies can be variable and unpredictable. Pathology-based phenotyping of tumors into ‘hot’ and ‘cold’ is static, relying solely on T-cell infiltration in single-time single-site biopsies, resulting in suboptimal treatment response prediction. Dynamic vascular events (tumor angiogenesis, leukocyte trafficking) within tumor immune microenvironment (TiME) also influence anti-tumor immunity and treatment response. Here, we report dynamic cellular-level TiME phenotyping in vivo that combines inflammation profiles with vascular features through non-invasive reflectance confocal microscopic imaging. In skin cancer patients, we demonstrate three main TiME phenotypes that correlate with gene and protein expression, and response to toll-like receptor agonist immune-therapy. Notably, phenotypes with high inflammation associate with immunostimulatory signatures and those with high vasculature with angiogenic and endothelial anergy signatures. Moreover, phenotypes with high inflammation and low vasculature demonstrate the best treatment response. This non-invasive in vivo phenotyping approach integrating dynamic vasculature with inflammation serves as a reliable predictor of response to topical immune-therapy in patients. Standard assessment of immune infiltration of biopsies is not sufficient to accurately predict response to immunotherapy. Here, the authors show that reflectance confocal microscopy can be used to quantify dynamic vasculature and inflammatory features to better predict treatment response in skin cancers.
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Affiliation(s)
- Aditi Sahu
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Kivanc Kose
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Lukas Kraehenbuehl
- Parker Institute for Cancer Immunotherapy, Ludwig Collaborative and Swim Across America Laboratory, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Candice Byers
- Roux Institute, Northeastern University, Portland, ME, USA.,Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Aliya Holland
- Parker Institute for Cancer Immunotherapy, Ludwig Collaborative and Swim Across America Laboratory, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Teguru Tembo
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,SUNY Downstate Health Sciences University, Brooklyn, NY, USA
| | | | - Anabel Alfonso
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Madison Li
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Miguel Cordova
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Melissa Gill
- SUNY Downstate Health Sciences University, Brooklyn, NY, USA.,Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital Solna, Stockholm, Sweden.,Faculty of Medicine and Health Sciences, University of Alcala, Madrid, Spain
| | - Christi Fox
- Caliber Imaging and Diagnostics, Rochester, NY, USA
| | - Salvador Gonzalez
- Faculty of Medicine and Health Sciences, University of Alcala, Madrid, Spain
| | - Piyush Kumar
- Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | | | | | - Shen Yin
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Paras Mehta
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Cristian Navarrete-Dechent
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Gary Peterson
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Kimeil King
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Stephen Dusza
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ning Yang
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Shuaitong Liu
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Pascale Guitera
- Sydney Melanoma Diagnostic Center, Sydney, NSW, Australia.,Melanoma Institute Australia, Wollstonecraft, NSW, Australia
| | - Anthony Rossi
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Allan Halpern
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Liang Deng
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Weill Cornell Medicine, New York, NY, USA
| | | | | | | | - Taha Merghoub
- Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Parker Institute for Cancer Immunotherapy, Ludwig Collaborative and Swim Across America Laboratory, Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Weill Cornell Medicine, New York, NY, USA
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Lboukili I, Stamatas G, Descombes X. Automating reflectance confocal microscopy image analysis for dermatological research: a review. JOURNAL OF BIOMEDICAL OPTICS 2022; 27:JBO-220021VRR. [PMID: 35879817 PMCID: PMC9309100 DOI: 10.1117/1.jbo.27.7.070902] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 07/08/2022] [Indexed: 05/31/2023]
Abstract
SIGNIFICANCE Reflectance confocal microscopy (RCM) is a noninvasive, in vivo technology that offers near histopathological resolution at the cellular level. It is useful in the study of phenomena for which obtaining a biopsy is impractical or would cause unnecessary tissue damage and trauma to the patient. AIM This review covers the use of RCM in the study of skin and the use of machine learning to automate information extraction. It has two goals: (1) an overview of information provided by RCM on skin structure and how it changes over time in response to stimuli and in disease and (2) an overview of machine learning approaches developed to automate the extraction of key morphological features from RCM images. APPROACH A PubMed search was conducted with additional literature obtained from references lists. RESULTS The application of RCM as an in vivo tool in dermatological research and the biologically relevant information derived from it are presented. Algorithms for image classification to epidermal layers, delineation of the dermal-epidermal junction, classification of skin lesions, and demarcation of individual cells within an image, all important factors in the makeup of the skin barrier, were reviewed. Application of image analysis methods in RCM is hindered by low image quality due to noise and/or poor contrast. Use of supervised machine learning is limited by time-consuming manual labeling of RCM images. CONCLUSIONS RCM has great potential in the study of skin structures. The use of artificial intelligence could enable an easier, more reproducible, precise, and rigorous study of RCM images for the understanding of skin structures, skin barrier, and skin inflammation and lesions. Although several attempts have been made, further work is still needed to provide a definite gold standard and overcome issues related to image quality, limited labeled datasets, and lack of phenotype variability in available databases.
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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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