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Hamada M, Eskelinen ASA, Florea C, Mikkonen S, Nieminen P, Grodzinsky AJ, Tanska P, Korhonen RK. Loss of collagen content is localized near cartilage lesions on the day of injurious loading and intensified on day 12. J Orthop Res 2024. [PMID: 39312444 DOI: 10.1002/jor.25975] [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: 01/21/2024] [Revised: 07/19/2024] [Accepted: 09/01/2024] [Indexed: 09/25/2024]
Abstract
Joint injury can lead to articular cartilage damage, excessive inflammation, and post-traumatic osteoarthritis (PTOA). Collagen is an essential component for cartilage function, yet current literature has limited understanding of how biochemical and biomechanical factors contribute to collagen loss in injured cartilage. Our aim was to investigate spatially dependent changes in collagen content and collagen integrity of injured cartilage, with an explant model of early-stage PTOA. We subjected calf knee cartilage explants to combinations of injurious loading (INJ), interleukin-1α-challenge (IL) and physiological cyclic loading (CL). Using Fourier transform infrared microspectroscopy, collagen content (Amide I band) and collagen integrity (Amide II/1338 cm-1 ratio) were estimated on days 0 and 12 post-injury. We found that INJ led to lower collagen content near lesions compared to intact regions on day 0 (p < 0.001). On day 12, near-lesion collagen content was lower compared to day 0 (p < 0.05). Additionally, on day 12, INJ, IL, and INJ + IL groups exhibited lower collagen content along most of tissue depth compared to free-swelling control group (p < 0.05). CL groups showed higher collagen content along most of tissue depth compared to corresponding groups without CL (p < 0.05). Immunohistochemical analysis revealed higher MMP-1 and MMP-3 staining intensities localized within cell lacunae in INJ group compared to CTRL group on day 0. Our results suggest that INJ causes rapid loss of collagen content near lesions, which is intensified on day 12. Additionally, CL could mitigate the loss of collagen content at intact regions after 12 days.
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Affiliation(s)
- Moustafa Hamada
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Atte S A Eskelinen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Cristina Florea
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Santtu Mikkonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Petteri Nieminen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Alan J Grodzinsky
- Departments of Biological Engineering, Electrical Engineering and Computer Science, and Mechanical Engineering, Massachusetts Institute of Technology, Massachusetts Avenue, Cambridge, Massachusetts, USA
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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Linus A, Tanska P, Nippolainen E, Tiitu V, Töyras J, Korhonen RK, Afara IO, Mononen ME. Site-specific elastic and viscoelastic biomechanical properties of healthy and osteoarthritic human knee joint articular cartilage. J Biomech 2024; 169:112135. [PMID: 38744145 DOI: 10.1016/j.jbiomech.2024.112135] [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: 08/21/2023] [Revised: 04/07/2024] [Accepted: 05/02/2024] [Indexed: 05/16/2024]
Abstract
Articular cartilage exhibits site-specific biomechanical properties. However, no study has comprehensively characterized site-specific cartilage properties from the same knee joints at different stages of osteoarthritis (OA). Cylindrical osteochondral explants (n = 381) were harvested from donor-matched lateral and medial tibia, lateral and medial femur, patella, and trochlea of cadaveric knees (N = 17). Indentation test was used to measure the elastic and viscoelastic mechanical properties of the samples, and Osteoarthritis Research Society International (OARSI) grading system was used to categorize the samples into normal (OARSI 0-1), early OA (OARSI 2-3), and advanced OA (OARSI 4-5) groups. OA-related changes in cartilage mechanical properties were site-specific. In the lateral and medial tibia and trochlea sites, equilibrium, instantaneous and dynamic moduli were higher (p < 0.001) in normal tissue than in early and advanced OA tissue. In lateral and medial femur, equilibrium, instantaneous and dynamic moduli were smaller in advanced OA, but not in early OA, than in normal tissue. The phase difference (0.1-0.25 Hz) between stress and strain was significantly smaller (p < 0.05) in advanced OA than in normal tissue across all sites except medial tibia. Our results indicated that in contrast to femoral and patellar cartilage, equilibrium, instantaneous and dynamic moduli of the tibia and trochlear cartilage decreased in early OA. These may suggest that the tibia and trochlear cartilage degrades faster than the femoral and patellar cartilage. The information is relevant for developing site-specific computational models and engineered cartilage constructs.
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Affiliation(s)
- Awuniji Linus
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Ervin Nippolainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Virpi Tiitu
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Juha Töyras
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland; Science Service Center, Kuopio University Hospital, Kuopio, Finland; School of Electrical Engineering and Computer Science, The University of Queensland, Brisbane, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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Sharma VJ, Singh A, Grant JL, Raman J. Point-of-care diagnosis of tissue fibrosis: a review of advances in vibrational spectroscopy with machine learning. Pathology 2024; 56:313-321. [PMID: 38341306 DOI: 10.1016/j.pathol.2023.11.008] [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: 05/30/2023] [Revised: 09/24/2023] [Accepted: 11/01/2023] [Indexed: 02/12/2024]
Abstract
Histopathology is the gold standard for diagnosing fibrosis, but its routine use is constrained by the need for additional stains, time, personnel and resources. Vibrational spectroscopy is a novel technique that offers an alternative atraumatic approach, with short scan times, while providing metabolic and morphological data. This review evaluates vibrational spectroscopy for the assessment of fibrosis, with a focus on point-of-care capabilities. OVID Medline, Embase and Cochrane databases were systematically searched using PRISMA guidelines for search terms including vibrational spectroscopy, human tissue and fibrosis. Studies were stratified based on imaging modality and tissue type. Outcomes recorded included tissue type, machine learning technique, metrics for accuracy and author conclusions. Systematic review yielded 420 articles, of which 14 were relevant. Ten of these articles considered mid-infrared spectroscopy, three dealt with Raman spectroscopy and one with near-infrared spectroscopy. The metrics for detecting fibrosis were Pearson correlation coefficients ranging from 0.65-0.98; sensitivity from 76-100%; specificity from 90-99%; area under receiver operator curves from 0.83-0.98; and accuracy of 86-99%. Vibrational spectroscopy identified fibrosis in myeloproliferative neoplasms in bone, cirrhotic and hepatocellular carcinoma in liver, end-stage heart failure in cardiac tissue and following laser ablation for acne in skin. It also identified interstitial fibrosis as a predictor of early renal transplant rejection in renal tissue. Vibrational spectroscopic techniques can therefore accurately identify fibrosis in a range of human tissues. Emerging data show that it can be used to quantify, classify and provide data about the nature of fibrosis with a high degree of accuracy with potential scope for point-of-care use.
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Affiliation(s)
- Varun J Sharma
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Health, Heidelberg, Melbourne, Vic, Australia; Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Spectromix Laboratory, Melbourne, Vic, Australia
| | - Aashima Singh
- Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Melbourne Medical School, The University of Melbourne, Vic, Australia
| | | | - Jaishankar Raman
- Brian F. Buxton Department of Cardiac and Thoracic Aortic Surgery, Austin Health, Heidelberg, Melbourne, Vic, Australia; Department of Surgery (Austin Health), Melbourne Medical School, The University of Melbourne, Vic, Australia; Spectromix Laboratory, Melbourne, Vic, Australia; Department of Cardiac Surgery, St Vincent's Hospital, Fitzroy, Melbourne, Vic, Australia.
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4
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Zupančič B, Ugwoke CK, Abdelmonaem MEA, Alibegović A, Cvetko E, Grdadolnik J, Šerbec A, Umek N. Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy. Front Endocrinol (Lausanne) 2023; 14:1308373. [PMID: 38189046 PMCID: PMC10769457 DOI: 10.3389/fendo.2023.1308373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 11/29/2023] [Indexed: 01/09/2024] Open
Abstract
Introduction The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents. Materials and methods In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach. Results A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles. Conclusion We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.
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Affiliation(s)
- Barbara Zupančič
- Laboratory for Molecular Structural Dynamics, Theory Department, National Institute of Chemistry, Ljubljana, Slovenia
| | | | - Mohamed Elwy Abdelhamed Abdelmonaem
- Laboratory for Molecular Structural Dynamics, Theory Department, National Institute of Chemistry, Ljubljana, Slovenia
- Biotechnical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Armin Alibegović
- Department of Forensic Medicine and Deontology, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Erika Cvetko
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Jože Grdadolnik
- Laboratory for Molecular Structural Dynamics, Theory Department, National Institute of Chemistry, Ljubljana, Slovenia
| | - Anja Šerbec
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Nejc Umek
- Institute of Anatomy, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
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Sharma VJ, Green A, McLean A, Adegoke J, Gordon CL, Starkey G, D'Costa R, James F, Afara I, Lal S, Wood B, Raman J. Towards a point-of-care multimodal spectroscopy instrument for the evaluation of human cardiac tissue. Heart Vessels 2023; 38:1476-1485. [PMID: 37608153 PMCID: PMC10602956 DOI: 10.1007/s00380-023-02292-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 07/13/2023] [Indexed: 08/24/2023]
Abstract
To demonstrate that point-of-care multimodal spectroscopy using Near-Infrared (NIR) and Raman Spectroscopy (RS) can be used to diagnose human heart tissue. We generated 105 spectroscopic scans, which comprised 4 NIR and 3 RS scans per sample to generate a "multimodal spectroscopic scan" (MSS) for each heart, done across 15 patients, 5 each from the dilated cardiomyopathy (DCM), Ischaemic Heart Disease (IHD) and Normal pathologies. Each of the MSS scans was undertaken in 3 s. Data were entered into machine learning (ML) algorithms to assess accuracy of MSS in diagnosing tissue type. The median age was 50 years (IQR 49-52) for IHD, 47 (IQR 45-50) for DCM and 36 (IQR 33-52) for healthy patients (p = 0.35), 60% of which were male. MSS identified key differences in IHD, DCM and normal heart samples in regions typically associated with fibrosis and collagen (NIR wavenumbers: 1433, 1509, 1581, 1689 and 1725 nm; RS wavelengths: 1658, 1450 and 1330 cm-1). In principal component (PC) analyses, these differences explained 99.2% of the variation in 4 PCs for NIR, 81.6% in 10 PCs for Raman, and 99.0% in 26 PCs for multimodal spectroscopic signatures. Using a stack machine learning algorithm with combined NIR and Raman data, our model had a precision of 96.9%, recall of 96.6%, specificity of 98.2% and Area Under Curve (AUC) of 0.989 (Table 1). NIR and Raman modalities alone had similar levels of precision at 94.4% and 89.8% respectively (Table 1). MSS combined with ML showed accuracy of 90% for detecting dilated cardiomyopathy, 100% for ischaemic heart disease and 100% for diagnosing healthy tissue. Multimodal spectroscopic signatures, based on NIR and Raman spectroscopy, could provide cardiac tissue scans in 3-s to aid accurate diagnoses of fibrosis in IHD, DCM and normal hearts. Table 1 Machine learning performance metrics for validation data sets of (a) Near-Infrared (NIR), (b) Raman and (c and d) multimodal data using logistic regression (LR), stochastic gradient descent (SGD) and support vector machines (SVM), with combined "stack" (LR + SGD + SVM) AUC Precision Recall Specificity (a) NIR model Logistic regression 0.980 0.944 0.933 0.967 SGD 0.550 0.281 0.400 0.700 SVM 0.840 0.806 0.800 0.900 Stack 0.933 0.794 0.800 0.900 (b) Raman model Logistic regression 0.985 0.940 0.929 0.960 SGD 0.892 0.869 0.857 0.932 SVM 0.992 0.940 0.929 0.960 Stack 0.954 0.869 0.857 0.932 (c) MSS: multimodal (NIR + Raman) to detect DCM vs. IHD vs. normal patients Logistic regression 0.975 0.841 0.828 0.917 SGD 0.847 0.803 0.793 0.899 SVM 0.971 0.853 0.828 0.917 Stack 0.961 0.853 0.828 0.917 (d) MSS: multimodal (NIR + Raman) to detect pathological vs. normal patients Logistic regression 0.961 0.969 0.966 0.984 SGD 0.944 0.967 0.966 0.923 SVM 1.000 1.000 1.000 1.000 Stack 1.000 0.944 0.931 0.969 Bold values indicate values obtained from the stack algorithm and used for analyses.
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Affiliation(s)
- Varun J Sharma
- Department of Surgery, Melbourne Medical School, University of Melbourne, Melbourne, Australia.
- Brian F. Buxton Department of Cardiac Surgery, Austin Hospital, Melbourne, Australia.
- Spectromix Laboratory, Melbourne, VIC, Australia.
| | - Alexander Green
- Spectromix Laboratory, Melbourne, VIC, Australia
- Monash Biospectroscopy, Monash University, Melbourne, Australia
| | - Aaron McLean
- Spectromix Laboratory, Melbourne, VIC, Australia
- Monash Biospectroscopy, Monash University, Melbourne, Australia
| | - John Adegoke
- Spectromix Laboratory, Melbourne, VIC, Australia
- Monash Biospectroscopy, Monash University, Melbourne, Australia
| | - Claire L Gordon
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia
- Department of Microbiology and Immunology, The University of Melbourne, The Peter Doherty Institute for Infection and Immunity, Melbourne, VIC, Australia
- North Eastern Public Health Unit, Austin Health, Melbourne, VIC, Australia
| | - Graham Starkey
- Liver Transplant Unit, Austin Hospital, Melbourne, Australia
| | - Rohit D'Costa
- DonateLife Victoria, Carlton, Melbourne, VIC, Australia
- Department of Intensive Care Medicine, Melbourne Health, Melbourne, VIC, Australia
| | - Fiona James
- Department of Infectious Diseases, Austin Health, Melbourne, VIC, Australia
- North Eastern Public Health Unit, Austin Health, Melbourne, VIC, Australia
| | - Isaac Afara
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Sean Lal
- Department of Cardiology, Royal Prince Alfred Hospital, Sydney, Australia
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Bayden Wood
- Spectromix Laboratory, Melbourne, VIC, Australia
- Monash Biospectroscopy, Monash University, Melbourne, Australia
| | - Jaishankar Raman
- Department of Surgery, Melbourne Medical School, University of Melbourne, Melbourne, Australia
- Brian F. Buxton Department of Cardiac Surgery, Austin Hospital, Melbourne, Australia
- Spectromix Laboratory, Melbourne, VIC, Australia
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6
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Sharma VJ, Adegoke JA, Fasulakis M, Green A, Goh SK, Peng X, Liu Y, Jackett L, Vago A, Poon EKW, Starkey G, Moshfegh S, Muthya A, D'Costa R, James F, Gordon CL, Jones R, Afara IO, Wood BR, Raman J. Point-of-care detection of fibrosis in liver transplant surgery using near-infrared spectroscopy and machine learning. Health Sci Rep 2023; 6:e1652. [PMID: 37920655 PMCID: PMC10618569 DOI: 10.1002/hsr2.1652] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/27/2023] [Accepted: 10/11/2023] [Indexed: 11/04/2023] Open
Abstract
Introduction Visual assessment and imaging of the donor liver are inaccurate in predicting fibrosis and remain surrogates for histopathology. We demonstrate that 3-s scans using a handheld near-infrared-spectroscopy (NIRS) instrument can identify and quantify fibrosis in fresh human liver samples. Methods We undertook NIRS scans on 107 samples from 27 patients, 88 from 23 patients with liver disease, and 19 from four organ donors. Results Liver disease patients had a median immature fibrosis of 40% (interquartile range [IQR] 20-60) and mature fibrosis of 30% (10%-50%) on histopathology. The organ donor livers had a median fibrosis (both mature and immature) of 10% (IQR 5%-15%). Using machine learning, this study detected presence of cirrhosis and METAVIR grade of fibrosis with a classification accuracy of 96.3% and 97.2%, precision of 96.3% and 97.0%, recall of 96.3% and 97.2%, specificity of 95.4% and 98.0% and area under receiver operator curve of 0.977 and 0.999, respectively. Using partial-least square regression machine learning, this study predicted the percentage of both immature (R 2 = 0.842) and mature (R 2 = 0.837) with a low margin of error (root mean square of error of 9.76% and 7.96%, respectively). Conclusion This study demonstrates that a point-of-care NIRS instrument can accurately detect, quantify and classify liver fibrosis using machine learning.
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Affiliation(s)
- Varun J. Sharma
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Brian F. Buxton Department of Cardiac and Thoracic Aortic SurgeryAustin HospitalMelbourneVictoriaAustralia
| | - John A. Adegoke
- Centre for BiospectroscopyMonash UniversityMelbourneVictoriaAustralia
| | - Michael Fasulakis
- Department of EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
| | - Alexander Green
- Centre for BiospectroscopyMonash UniversityMelbourneVictoriaAustralia
| | - Su K. Goh
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Liver & Intestinal Transplant UnitAustin HealthMelbourneVictoriaAustralia
| | - Xiuwen Peng
- Department of EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
| | - Yifan Liu
- Department of EngineeringUniversity of MelbourneMelbourneVictoriaAustralia
| | - Louise Jackett
- Department of Anatomical PathologyAustin HealthMelbourneVictoriaAustralia
| | - Angela Vago
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Liver & Intestinal Transplant UnitAustin HealthMelbourneVictoriaAustralia
| | - Eric K. W. Poon
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and ImmunityUniversity of MelbourneMelbourneVictoriaAustralia
| | - Graham Starkey
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Liver & Intestinal Transplant UnitAustin HealthMelbourneVictoriaAustralia
| | - Sarina Moshfegh
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
| | - Ankita Muthya
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
| | - Rohit D'Costa
- DonateLife VictoriaCarltonVictoriaAustralia
- Department of Intensive Care MedicineMelbourne HealthMelbourneVictoriaAustralia
| | - Fiona James
- Department of Infectious DiseasesAustin HealthMelbourneVictoriaAustralia
| | - Claire L. Gordon
- Department of Microbiology and Immunology, Peter Doherty Institute for Infection and ImmunityUniversity of MelbourneMelbourneVictoriaAustralia
- Department of Infectious DiseasesAustin HealthMelbourneVictoriaAustralia
| | - Robert Jones
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Liver & Intestinal Transplant UnitAustin HealthMelbourneVictoriaAustralia
| | - Isaac O. Afara
- School of Information Technology and Electrical EngineeringFaculty of Engineering, Architecture, and Information TechnologyBrisbaneQueenslandAustralia
- Biomedical Spectroscopy Laboratory, Department of Applied PhysicsUniversity of Eastern FinlandKuopioFinland
| | - Bayden R. Wood
- Centre for BiospectroscopyMonash UniversityMelbourneVictoriaAustralia
| | - Jaishankar Raman
- Department of Surgery, Melbourne Medical SchoolUniversity of MelbourneMelbourneVictoriaAustralia
- Brian F. Buxton Department of Cardiac and Thoracic Aortic SurgeryAustin HospitalMelbourneVictoriaAustralia
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7
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Linus A, Tanska P, Sarin JK, Nippolainen E, Tiitu V, Mäkelä JTA, Töyräs J, Korhonen RK, Mononen ME, Afara IO. Visible and Near-Infrared Spectroscopy Enables Differentiation of Normal and Early Osteoarthritic Human Knee Joint Articular Cartilage. Ann Biomed Eng 2023; 51:2245-2257. [PMID: 37332006 PMCID: PMC10518273 DOI: 10.1007/s10439-023-03261-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 05/27/2023] [Indexed: 06/20/2023]
Abstract
Osteoarthritis degenerates cartilage and impairs joint function. Early intervention opportunities are missed as current diagnostic methods are insensitive to early tissue degeneration. We investigated the capability of visible light-near-infrared spectroscopy (Vis-NIRS) to differentiate normal human cartilage from early osteoarthritic one. Vis-NIRS spectra, biomechanical properties and the state of osteoarthritis (OARSI grade) were quantified from osteochondral samples harvested from different anatomical sites of human cadaver knees. Two support vector machines (SVM) classifiers were developed based on the Vis-NIRS spectra and OARSI scores. The first classifier was designed to distinguish normal (OARSI: 0-1) from general osteoarthritic cartilage (OARSI: 2-5) to check the general suitability of the approach yielding an average accuracy of 75% (AUC = 0.77). Then, the second classifier was designed to distinguish normal from early osteoarthritic cartilage (OARSI: 2-3) yielding an average accuracy of 71% (AUC = 0.73). Important wavelength regions for differentiating normal from early osteoarthritic cartilage were related to collagen organization (wavelength region: 400-600 nm), collagen content (1000-1300 nm) and proteoglycan content (1600-1850 nm). The findings suggest that Vis-NIRS allows objective differentiation of normal and early osteoarthritic tissue, e.g., during arthroscopic repair surgeries.
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Affiliation(s)
- Awuniji Linus
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland.
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Jaakko K Sarin
- Department of Medical Physics, Medical Imaging Center, Pirkanmaa Hospital District, Tampere, Finland
| | - Ervin Nippolainen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Virpi Tiitu
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Janne T A Mäkelä
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Juha Töyräs
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
| | - Rami K Korhonen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Mika E Mononen
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, 70211, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia
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8
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Kosonen JP, Eskelinen ASA, Orozco GA, Nieminen P, Anderson DD, Grodzinsky AJ, Korhonen RK, Tanska P. Injury-related cell death and proteoglycan loss in articular cartilage: Numerical model combining necrosis, reactive oxygen species, and inflammatory cytokines. PLoS Comput Biol 2023; 19:e1010337. [PMID: 36701279 PMCID: PMC9879441 DOI: 10.1371/journal.pcbi.1010337] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/06/2022] [Indexed: 01/27/2023] Open
Abstract
Osteoarthritis (OA) is a common musculoskeletal disease that leads to deterioration of articular cartilage, joint pain, and decreased quality of life. When OA develops after a joint injury, it is designated as post-traumatic OA (PTOA). The etiology of PTOA remains poorly understood, but it is known that proteoglycan (PG) loss, cell dysfunction, and cell death in cartilage are among the first signs of the disease. These processes, influenced by biomechanical and inflammatory stimuli, disturb the normal cell-regulated balance between tissue synthesis and degeneration. Previous computational mechanobiological models have not explicitly incorporated the cell-mediated degradation mechanisms triggered by an injury that eventually can lead to tissue-level compositional changes. Here, we developed a 2-D mechanobiological finite element model to predict necrosis, apoptosis following excessive production of reactive oxygen species (ROS), and inflammatory cytokine (interleukin-1)-driven apoptosis in cartilage explant. The resulting PG loss over 30 days was simulated. Biomechanically triggered PG degeneration, associated with cell necrosis, excessive ROS production, and cell apoptosis, was predicted to be localized near a lesion, while interleukin-1 diffusion-driven PG degeneration was manifested more globally. Interestingly, the model also showed proteolytic activity and PG biosynthesis closer to the levels of healthy tissue when pro-inflammatory cytokines were rapidly inhibited or cleared from the culture medium, leading to partial recovery of PG content. The numerical predictions of cell death and PG loss were supported by previous experimental findings. Furthermore, the simulated ROS and inflammation mechanisms had longer-lasting effects (over 3 days) on the PG content than localized necrosis. The mechanobiological model presented here may serve as a numerical tool for assessing early cartilage degeneration mechanisms and the efficacy of interventions to mitigate PTOA progression.
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Affiliation(s)
- Joonas P. Kosonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- * E-mail:
| | | | - Gustavo A. Orozco
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| | - Petteri Nieminen
- Institute of Biomedicine, University of Eastern Finland, Kuopio, Finland
| | - Donald D. Anderson
- Departments of Orthopedics & Rehabilitation and Biomedical Engineering, University of Iowa, Iowa City, Iowa, United States of America
| | - Alan J. Grodzinsky
- Departments of Biological Engineering, Electrical Engineering and Computer Science, and Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Rami K. Korhonen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
| | - Petri Tanska
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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