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Chalian M, Pooyan A, Alipour E, Roemer FW, Guermazi A. What is New in Osteoarthritis Imaging? Radiol Clin North Am 2024; 62:739-753. [PMID: 39059969 DOI: 10.1016/j.rcl.2024.02.006] [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] [Indexed: 07/28/2024]
Abstract
Osteoarthritis (OA) is the leading joint disorder globally, affecting a significant proportion of the population. Recent studies have changed our understanding of OA, viewing it as a complex pathology of the whole joint with a multifaceted etiology, encompassing genetic, biological, and biomechanical elements. This review highlights the role of imaging in diagnosing and monitoring OA. Today's role of radiography is discussed, while also elaborating on the advances in computed tomography and magnetic resonance imaging, discussing semiquantitative methods, quantitative morphologic and compositional techniques, and giving an outlook on the potential role of artificial intelligence in OA research.
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Affiliation(s)
- Majid Chalian
- Department of Radiology, University of Washington, Seattle, USA; Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology, Roosevelt Clinic, 4245 Roosevelt Way, NE Box 354755, Seattle, WA 98105, USA
| | - Atefe Pooyan
- Department of Radiology, University of Washington, Seattle, USA; Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology, Roosevelt Clinic, 4245 Roosevelt Way, NE Box 354755, Seattle, WA 98105, USA
| | - Ehsan Alipour
- Department of Radiology, University of Washington, Seattle, USA; Musculoskeletal Imaging and Intervention, University of Washington, UW Radiology, Roosevelt Clinic, 4245 Roosevelt Way, NE Box 354755, Seattle, WA 98105, USA
| | - Frank W Roemer
- Department of Radiology, Friedrich-Alexander University Erlangen-Nürnberg; Universitätsklinikum Erlangen, Erlangen, Germany; Department of Radiology, Quantitative Imaging Center, Boston University School of Medicine
| | - Ali Guermazi
- Department of Radiology, Quantitative Imaging Center, Boston University School of Medicine; Department of Radiology, VA Boston Healthcare System, Boston, MA, USA.
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Liu S, Zhang Y, Liu W, Yin T, Yuan J, Ran J, Li X. Simultaneous multi-slice technique for reducing acquisition times in diffusion tensor imaging of the knee: a feasibility study. Skeletal Radiol 2024:10.1007/s00256-024-04719-y. [PMID: 38913177 DOI: 10.1007/s00256-024-04719-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 05/30/2024] [Accepted: 05/30/2024] [Indexed: 06/25/2024]
Abstract
OBJECTIVES To explore the feasibility of simultaneous multi-slice (SMS) technique for reducing acquisition times in readout-segmented echo planar imaging (RESOLVE) for diffusion tensor imaging (DTI) of the knee. MATERIALS AND METHODS A total of 30 healthy volunteers and 23 patients with knee acute injury (12 cases with anterior ligament (ACL) tears and 16 cases with patellar cartilage (PC) injury) were enrolled in this prospective study. Three DTI protocols were used: conventional RESOLVE-DTI with 12 directions (protocol 1), SMS-RESOLVE-DTI with 12 directions (protocol 2) and 20 directions (protocol 3). DTI parameters of gastrocnemius, ACL and posterior cruciate ligament (PCL), and PC from three protocols were quantitatively assessed. RESULTS For volunteers, protocol 2 significantly reduced acquisition time by 38.6% and 34.2% compared to protocols 1 and 3 while maintaining similar high-quality images and similar diffusive parameters, except for the fractional anisotropy (FA) and axial diffusivity (AD) of the PC between protocols 2 and 1 (P < 0.05). For injured ACL and PC, protocols 1 and 2 showed similar accurate diffusive parameters (except for AD, P = 0.025) and similar diagnostic efficacy, which demonstrated significantly lower FA and higher radial diffusivity (RD) in protocols 1 and 2 compared to volunteers (P < 0.05). CONCLUSIONS The 12-direction SMS-RESOLVE-DTI demonstrated a favorable balance between acquisition time and image quality, making it a promising alternative to conventional DTI for evaluating ligament and cartilage injuries. ADVANCES IN KNOWLEDGE The SMS technique greatly reduces acquisition time while maintaining image quality, which signified the possibility of DTI's clinical application.
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Affiliation(s)
- Simin Liu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China
| | - Yao Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China
| | - Wei Liu
- MR Application Development, Siemens Shenzhen Magnetic Resonance Ltd., No. 32 Gaoxin C. Ave., 2nd, Shenzhen, China
| | - Ting Yin
- MR Collaborations, Siemens Healthineers Ltd., Chengdu, China
| | - Jie Yuan
- Department of Radiology, Zhongxiang People's Hospital, Zhongxiang City, China
| | - Jun Ran
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China.
| | - Xiaoming Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095, Jiefang Road, Wuhan, Hubei Province, China.
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Hu K, Ou Y, Xiao L, Gu R, He F, Peng J, Shu Y, Li T, Hao L. Identification and Construction of a Disulfidptosis-Mediated Diagnostic Model and Associated Immune Microenvironment of Osteoarthritis from the Perspective of PPPM. J Inflamm Res 2024; 17:3753-3770. [PMID: 38882183 PMCID: PMC11179642 DOI: 10.2147/jir.s462179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Accepted: 05/29/2024] [Indexed: 06/18/2024] Open
Abstract
Background Osteoarthritis (OA) is a major cause of human disability. Despite receiving treatment, patients with the middle and late stage of OA have poor survival outcomes. Therefore, within the framework of predictive, preventive, and personalized medicine (PPPM/3PM), early personalized diagnosis of OA is particularly prominent. PPPM aims to accurately identify disease by integrating multiple omic techniques; however, the efficiency of currently available methods and biomarkers in predicting and diagnosing OA should be improved. Disulfidptosis, a novel programmed cell death mechanism and appeared in particular metabolic status, plays a mysterious characteristic in the occurrence and development of OA, which warrants further investigation. Methods In this study, we integrated three public datasets from the Gene Expression Omnibus (GEO) database, including 26 OA samples and 20 normal samples. Via a series of bioinformatic analysis and machine learning, we identified the diagnostic biomarkers and several subtypes of OA. Moreover, the expression of these biomarkers were verified in our in-house cohort and the single cell dataset. Results Three significant regulators of disulfidptosis (NCKAP1, OXSM, and SLC3A2) were identified through differential expression analysis and machine learning. And a nomogram constructed based on these three regulators exhibited ideal efficiency in predicting early- and late-stage OA. Furthermore, based on the expression of three regulators, we identified two disulfidptosis-related subtypes of OA with different infiltration of immune cells and personalized expression level of immune checkpoints. Notably, the expression of the three regulators was demonstrated in a single-cell RNA profile and verified in the synovial tissue in our in-house cohort including 6 OA patients and 6 normal people. Finally, an efficient disulfidptosis-mediated diagnostic model was constructed for OA, with the AUC value of 97.6923% in the training set and 93.3333% and 100% in two validation sets. Conclusion Overall, with regard to PPPM, this study provided novel insights into the role of disulfidptosis regulators in the personalized diagnosis and treatment of OA.
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Affiliation(s)
- Kaibo Hu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yanghuan Ou
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
| | - Leyang Xiao
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ruonan Gu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Fei He
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Jie Peng
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Yuan Shu
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Ting Li
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
- The Second Clinical Medical College, Nanchang University, Nanchang, 330006, People's Republic of China
| | - Liang Hao
- Department of Orthopedics, Second Affiliated Hospital of Nanchang University, Nanchang, 330006, People's Republic of China
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Vijayvargiya A, Sinha A, Gehlot N, Jena A, Kumar R, Moran K. S-WD-EEMD: A hybrid framework for imbalanced sEMG signal analysis in diagnosis of human knee abnormality. PLoS One 2024; 19:e0301263. [PMID: 38820390 PMCID: PMC11142505 DOI: 10.1371/journal.pone.0301263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Accepted: 03/13/2024] [Indexed: 06/02/2024] Open
Abstract
The diagnosis of human knee abnormalities using the surface electromyography (sEMG) signal obtained from lower limb muscles with machine learning is a major problem due to the noisy nature of the sEMG signal and the imbalance in data corresponding to healthy and knee abnormal subjects. To address this challenge, a combination of wavelet decomposition (WD) with ensemble empirical mode decomposition (EEMD) and the Synthetic Minority Oversampling Technique (S-WD-EEMD) is proposed. In this study, a hybrid WD-EEMD is considered for the minimization of noises produced in the sEMG signal during the collection, while the Synthetic Minority Oversampling Technique (SMOTE) is considered to balance the data by increasing the minority class samples during the training of machine learning techniques. The findings indicate that the hybrid WD-EEMD with SMOTE oversampling technique enhances the efficacy of the examined classifiers when employed on the imbalanced sEMG data. The F-Score of the Extra Tree Classifier, when utilizing WD-EEMD signal processing with SMOTE oversampling, is 98.4%, whereas, without the SMOTE oversampling technique, it is 95.1%.
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Affiliation(s)
- Ankit Vijayvargiya
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
- Department of Electrical Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, Rajasthan, India
| | - Aparna Sinha
- Department of Information Technology, Bansthali Vidyapeeth, Radha Kishnpura, Rajasthan, India
| | - Naveen Gehlot
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Ashutosh Jena
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Rajesh Kumar
- Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India
| | - Kieran Moran
- Insight Science Foundation Ireland Research Centre for Data Analytics, School of Human and Health Performance, Dublin City University, Dublin, Ireland
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Yu AS, Yang M, Lartey R, Holden W, Ok AH, Khan S, Kim J, Winalski C, Subhas N, Chaudhary V, Li X. Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models. Bioengineering (Basel) 2024; 11:526. [PMID: 38927762 PMCID: PMC11200419 DOI: 10.3390/bioengineering11060526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/07/2024] [Accepted: 05/17/2024] [Indexed: 06/28/2024] Open
Abstract
Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance.
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Affiliation(s)
- Andrew Seohwan Yu
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Mingrui Yang
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Richard Lartey
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William Holden
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Ahmet Hakan Ok
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Sameed Khan
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Jeehun Kim
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Electrical, Computer, and Systems Engineering, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Carl Winalski
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Naveen Subhas
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Vipin Chaudhary
- Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH 44106, USA;
| | - Xiaojuan Li
- Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH 44195, USA; (M.Y.); (R.L.); (W.H.); (A.H.O.); (S.K.); (J.K.); (C.W.); (N.S.); (X.L.)
- Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Diagnostic Radiology, Imaging Institute, Cleveland Clinic, Cleveland, OH 44195, USA
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Minnig MCC, Golightly YM, Nelson AE. Epidemiology of osteoarthritis: literature update 2022-2023. Curr Opin Rheumatol 2024; 36:108-112. [PMID: 38240280 PMCID: PMC10965245 DOI: 10.1097/bor.0000000000000985] [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: 01/23/2024]
Abstract
PURPOSE OF REVIEW This review highlights recently published studies on osteoarthritis (OA) epidemiology, including topics related to understudied populations and joints, imaging, and advancements in artificial intelligence (AI) methods. RECENT FINDINGS Contemporary research has improved our understanding of the burden of OA in typically understudied regions, including ethnic and racial minorities in high-income countries, the Middle East and North Africa (MENA) and Latin America. Efforts have also been made to explore the burden and risk factors in OA in previously understudied joints, such as the hand, foot, and ankle. Advancements in OA imaging techniques have occurred alongside the developments of AI methods aiming to predict disease phenotypes, progression, and outcomes. SUMMARY Continuing efforts to expand our knowledge around OA in understudied populations will allow for the creation of targeted and specific interventions and inform policy changes aimed at reducing disease burden in these groups. The burden and disability associated with OA is notable in understudied joints, warranting further research efforts that may lead to effective therapeutic options. AI methods show promising results of predicting OA phenotypes and progression, which also may encourage the creation of targeted disease modifying OA drugs (DMOADs).
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Affiliation(s)
- Mary Catherine C. Minnig
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yvonne M. Golightly
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
- College of Allied Health Professions, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Amanda E. Nelson
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Thurston Arthritis Research Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Medicine, University of North Carolina, Chapel Hill, NC, USA
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7
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Zhang X, Liu Q, Zhang J, Song C, Han Z, Wang J, Shu L, Liu W, He J, Wang P. The emerging role of lncRNAs in osteoarthritis development and potential therapy. Front Genet 2023; 14:1273933. [PMID: 37779916 PMCID: PMC10538550 DOI: 10.3389/fgene.2023.1273933] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023] Open
Abstract
Osteoarthritis impairs the functions of various joints, such as knees, hips, hands and spine, which causes pain, swelling, stiffness and reduced mobility in joints. Multiple factors, including age, joint injuries, obesity, and mechanical stress, could contribute to osteoarthritis development and progression. Evidence has demonstrated that genetics and epigenetics play a critical role in osteoarthritis initiation and progression. Noncoding RNAs (ncRNAs) have been revealed to participate in osteoarthritis development. In this review, we describe the pivotal functions and molecular mechanisms of numerous lncRNAs in osteoarthritis progression. We mention that long noncoding RNAs (lncRNAs) could be biomarkers for osteoarthritis diagnosis, prognosis and therapeutic targets. Moreover, we highlight the several compounds that alleviate osteoarthritis progression in part via targeting lncRNAs. Furthermore, we provide the future perspectives regarding the potential application of lncRNAs in diagnosis, treatment and prognosis of osteoarthritis.
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Affiliation(s)
- Xiaofeng Zhang
- Department of Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Qishun Liu
- Department of Orthopedics, Zhejiang Medical & Health Group Hangzhou Hospital, Hang Gang Hospital, Hangzhou, China
| | - Jiandong Zhang
- Department of Orthopedics and Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Caiyuan Song
- Department of Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Zongxiao Han
- Department of Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Jinjie Wang
- Department of Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Lilu Shu
- Zhejiang Zhongwei Medical Research Center, Department of Medicine, Hangzhou, Zhejiang, China
| | - Wenjun Liu
- Zhejiang Zhongwei Medical Research Center, Department of Medicine, Hangzhou, Zhejiang, China
| | - Jinlin He
- Department of Traumatology, Hangzhou Fuyang Hospital of TCM Orthopedics and Traumatology, Hangzhou, Zhejiang, China
| | - Peter Wang
- Zhejiang Zhongwei Medical Research Center, Department of Medicine, Hangzhou, Zhejiang, China
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8
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Park EH, Fritz J. The role of imaging in osteoarthritis. Best Pract Res Clin Rheumatol 2023; 37:101866. [PMID: 37659890 DOI: 10.1016/j.berh.2023.101866] [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: 04/24/2023] [Revised: 08/08/2023] [Accepted: 08/13/2023] [Indexed: 09/04/2023]
Abstract
Osteoarthritis is a complex whole-organ disorder that involves molecular, anatomic, and physiologic derangement. Advances in imaging techniques have expanded the role of imaging in evaluating osteoarthritis and functional changes. Radiography, magnetic resonance imaging, computed tomography (CT), and ultrasonography are commonly used imaging modalities, each with advantages and limitations in evaluating osteoarthritis. Radiography comprehensively analyses alignment and osseous features, while MRI provides detailed information about cartilage damage, bone marrow edema, synovitis, and soft tissue abnormalities. Compositional imaging derives quantitative data for detecting cartilage and tendon degeneration before structural damage occurs. Ultrasonography permits real-time scanning and dynamic joint evaluation, whereas CT is useful for assessing final osseous detail. Imaging plays an essential role in the diagnosis, management, and research of osteoarthritis. The use of imaging can help differentiate osteoarthritis from other diseases with similar symptoms, and recent advances in deep learning have made the acquisition, management, and interpretation of imaging data more efficient and accurate. Imaging is useful in monitoring and predicting the prognosis of osteoarthritis, expanding our understanding of its pathophysiology. Ultimately, this enables early detection and personalized medicine for patients with osteoarthritis. This article reviews the current state of imaging in osteoarthritis, focusing on the strengths and limitations of various imaging modalities, and introduces advanced techniques, including deep learning, applied in clinical practice.
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Affiliation(s)
- Eun Hae Park
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA; Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, Jeonju, Republic of Korea
| | - Jan Fritz
- Division of Musculoskeletal Radiology, Department of Radiology, NYU Grossman School of Medicine, New York, USA.
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9
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Walsh C, Rajora MA, Ding L, Nakamura S, Endisha H, Rockel J, Chen J, Kapoor M, Zheng G. Protease-Activatable Porphyrin Molecular Beacon for Osteoarthritis Management. CHEMICAL & BIOMEDICAL IMAGING 2023; 1:66-80. [PMID: 37122828 PMCID: PMC10131263 DOI: 10.1021/cbmi.3c00005] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 02/06/2023] [Accepted: 02/12/2023] [Indexed: 05/02/2023]
Abstract
Despite the substantial burden posed by osteoarthritis (OA) globally, difficult challenges remain in achieving early OA diagnosis and adopting effective disease-modifying treatments. In this study, we use a biomolecular approach to address these limitations by creating an inherently theranostic molecular beacon whose imaging and therapeutic capabilities are activated by early pathological changes in OA. This platform comprised (1) a peptide linker substrate for metalloproteinase-13 (MMP-13), a pathological protease upregulated in OA, which was conjugated to (2) a porphyrin moiety with inherent multimodal imaging, photodynamic therapy, and drug delivery capabilities, and (3) a quencher that silences the porphyrin's endogenous fluorescence and photoreactivity when the beacon is intact. In diseased OA tissue with upregulated MMP-13 expression, this porphyrin molecular beacon (PPMMP13B) was expected to undergo sequence-specific cleavage, yielding porphyrin fragments with restored fluorescence and photoreactivity that could, respectively, be used as a readout of MMP-13 activity within the joint for early OA imaging and disease-targeted photodynamic therapy. This study focused on the synthesis and characterization of PPMMP13B, followed by a proof-of-concept evaluation of its OA imaging and drug delivery potential. In solution, PPMMP13B demonstrated 90% photoactivity quenching in its intact form and robust MMP-13 activation, yielding a 13-fold increase in fluorescence post-cleavage. In vitro, PPMMP13B was readily uptaken and activated in an MMP-13 cell expression-dependent manner in primary OA synoviocytes without exuding significant cytotoxicity. This translated into effective intra-articular cartilage (to a 50 μm depth) and synovial uptake and activation of PPMMP13B in a destabilization of the medial meniscus OA mouse model, yielding strong fluorescence contrast (7-fold higher signal than background) at the diseased joint site. These results provide the foundation for further exploration of porphyrin molecular beacons for image-guided OA disease stratification, effective articular delivery of disease-modify agents, and OA photodynamic therapy.
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Affiliation(s)
- Connor Walsh
- Princess
Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
- Institute
of Biomedical Engineering, University of
Toronto, Toronto, ON M5S 3G9, Canada
| | - Maneesha A. Rajora
- Princess
Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
- Institute
of Biomedical Engineering, University of
Toronto, Toronto, ON M5S 3G9, Canada
| | - Lili Ding
- Princess
Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Sayaka Nakamura
- Schroeder
Arthritis Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Krembil
Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Helal Endisha
- Schroeder
Arthritis Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Krembil
Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Jason Rockel
- Schroeder
Arthritis Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Krembil
Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Juan Chen
- Princess
Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Mohit Kapoor
- Schroeder
Arthritis Institute, University Health Network, Toronto, ON M5T 0S8, Canada
- Krembil
Research Institute, University Health Network, Toronto, ON M5T 0S8, Canada
| | - Gang Zheng
- Princess
Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
- Institute
of Biomedical Engineering, University of
Toronto, Toronto, ON M5S 3G9, Canada
- Department
of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
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Neubauer M, Moser L, Neugebauer J, Raudner M, Wondrasch B, Führer M, Emprechtinger R, Dammerer D, Ljuhar R, Salzlechner C, Nehrer S. Artificial-Intelligence-Aided Radiographic Diagnostic of Knee Osteoarthritis Leads to a Higher Association of Clinical Findings with Diagnostic Ratings. J Clin Med 2023; 12:jcm12030744. [PMID: 36769394 PMCID: PMC9917552 DOI: 10.3390/jcm12030744] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2022] [Revised: 01/10/2023] [Accepted: 01/13/2023] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND Radiographic knee osteoarthritis (OA) severity and clinical severity are often dissociated. Artificial intelligence (AI) aid was shown to increase inter-rater reliability in radiographic OA diagnosis. Thus, AI-aided radiographic diagnoses were compared against AI-unaided diagnoses with regard to their correlations with clinical severity. METHODS Seventy-one DICOMs (m/f = 27:42, mean age: 27.86 ± 6.5) (X-ray format) were used for AI analysis (KOALA software, IB Lab GmbH). Subjects were recruited from a physiotherapy trial (MLKOA). At baseline, each subject received (i) a knee X-ray and (ii) an assessment of five main scores (Tegner Scale (TAS); Knee Injury and Osteoarthritis Outcome Score (KOOS); International Physical Activity Questionnaire; Star Excursion Balance Test; Six-Minute Walk Test). Clinical assessments were repeated three times (weeks 6, 12 and 24). Three physicians analyzed the presented X-rays both with and without AI via KL grading. Analyses of the (i) inter-rater reliability (IRR) and (ii) Spearman's Correlation Test for the overall KL score for each individual rater with clinical score were performed. RESULTS We found that AI-aided diagnostic ratings had a higher association with the overall KL score and the KOOS. The amount of improvement due to AI depended on the individual rater. CONCLUSION AI-guided systems can improve the ratings of knee radiographs and show a stronger association with clinical severity. These results were shown to be influenced by individual readers. Thus, AI training amongst physicians might need to be increased. KL might be insufficient as a single tool for knee OA diagnosis.
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Affiliation(s)
- Markus Neubauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Lukas Moser
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Johannes Neugebauer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Marcus Raudner
- Medical University of Vienna, High-Field MR Center, Department of Biomedical Imaging and Image-Guided Therapy, Währinger-Gürtel 18-20, 1090 Vienna, Austria
| | - Barbara Wondrasch
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Magdalena Führer
- Department of Health and Social Sciences, St. Poelten University of Applied Sciences, Campus-Platz 1, 3100 St. Poelten, Austria
| | - Robert Emprechtinger
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
| | - Dietmar Dammerer
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
| | - Richard Ljuhar
- ImageBiopsy Lab GmbH, Zehetnergasse 6/2/2, 1140 Vienna, Austria
| | | | - Stefan Nehrer
- Danube University Krems, Center for Regenerative Medicine, Dr. Karl-Dorrek-Str. 30, 3500 Krems, Austria
- Karl Landsteiner University of Health Sciences, Department for Orthopedics and Traumatology, University Hospital Krems, Dr. Karl-Dorrek-Straße 30, 3500 Krems, Austria
- Correspondence:
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