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Cook H, Crisford A, Bourdakos K, Dunlop D, Oreffo ROC, Mahajan S. Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis. BIOMEDICAL OPTICS EXPRESS 2024; 15:4264-4280. [PMID: 39022535 PMCID: PMC11249685 DOI: 10.1364/boe.520171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 05/29/2024] [Accepted: 05/31/2024] [Indexed: 07/20/2024]
Abstract
Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.
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Affiliation(s)
- Hiroki Cook
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Anna Crisford
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Human Development Health, Faculty of Medicine, Southampton SO16 6YD, UK
| | - Konstantinos Bourdakos
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
| | - Douglas Dunlop
- University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK
| | - Richard O. C. Oreffo
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Human Development Health, Faculty of Medicine, Southampton SO16 6YD, UK
| | - Sumeet Mahajan
- School of Chemistry, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Institute for Life Sciences, University of Southampton, Southampton SO17 1BJ, UK
- Department of Biotechnology, Inland Norway University of Applied Sciences, N-2317 Hamar, Norway
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2
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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [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: 09/05/2023] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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3
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Zeng R, Ye Y, Ou H, Hua Y, Su Y, Hu J, Lu H, Tang J, Liu J, Xiao T, Wu Z, Tang W, Li ZY, Lin SJ, Zhuang S, Xu G, Lin Y, Li Y, Huang F, Zhang HT. Early osteoarthritis diagnosis based on near-infrared spectroscopy combined with aquaphotomics. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 302:123120. [PMID: 37453381 DOI: 10.1016/j.saa.2023.123120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 06/09/2023] [Accepted: 07/07/2023] [Indexed: 07/18/2023]
Abstract
Osteoarthritis (OA) is the most common joint disease and the leading cause of disability in elderly individuals. Despite rapid advances in imaging techniques, early OA diagnosis remains a clinical challenge. In the present study, the feasibility of early OA diagnosis was explored via near-infrared spectroscopy (NIRS) combined with aquaphotomics. Synovial fluid samples from 65 cases of OA categorized as mild, moderate, and severe according to theKellgrenandLawrence classification criteria were analyzed via NIRS. The 1st overtone of water (1300-1600 nm) was considered as the research object for an aquaphotomics model, and aquagrams of the mild, moderate, and severe OA cases were generated using 12 water absorption patterns for early OA diagnosis.The aquaphotomics results exhibited clear differences in the region of 1300-1500 nm, and the number of hydrogen bonds of different water species (1412,1424, 1482, and 1496 nm) evidently correlated with OA occurrence and development. With OA progression, the absorption intensity of water molecules without hydrogen bonds (1412 nm/1424 nm) became stronger, while the absorption intensity of water molecules with four hydrogen bonds (1482 nm/1496 nm) decreased.These results together reveal that the established accurate and rapid early OA diagnosis model based on NIRS combined with aquaphotomics is effective and feasible, and that the number of hydrogen bonds can be used as a biomarker for early OA diagnosis.
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Affiliation(s)
- Rui Zeng
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Yongsheng Ye
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China; Department of Orthopedics, Dongguan Hospital of Traditional Chinese Medicine, Dongguan, Guangdong 523000, China
| | - Haisheng Ou
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Yisheng Hua
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Yuancui Su
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Junhui Hu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Hanglin Lu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Jian Tang
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Jun Liu
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China
| | - Teng Xiao
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Zhaosheng Wu
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Wang Tang
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Zhen-Yan Li
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Su-Juan Lin
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China
| | - Shabin Zhuang
- Department of Orthopedics, Dongguan Hospital of Traditional Chinese Medicine, Dongguan, Guangdong 523000, China
| | - Guisheng Xu
- Department of Joint and Sports Medicine, the First People's Hospital of Zhaoqing, Zhaoqing, Guangdong 526000, China
| | - Yuning Lin
- Department of Joint and Sports Medicine, the First People's Hospital of Zhaoqing, Zhaoqing, Guangdong 526000, China
| | - Yuanpeng Li
- College of Physical Science and Technology, Guangxi Normal University, Guilin, Guangxi 541004, China.
| | - Furong Huang
- Department of Optoelectronic Engineering, Jinan University, Guangzhou, Guangdong 510632, China.
| | - Huan-Tian Zhang
- Department of Bone and Joint Surgery, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong 510632, China.
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4
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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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5
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Zhang W, Kasun LC, Wang QJ, Zheng Y, Lin Z. A Review of Machine Learning for Near-Infrared Spectroscopy. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22249764. [PMID: 36560133 PMCID: PMC9784128 DOI: 10.3390/s22249764] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 12/04/2022] [Accepted: 12/05/2022] [Indexed: 06/01/2023]
Abstract
The analysis of infrared spectroscopy of substances is a non-invasive measurement technique that can be used in analytics. Although the main objective of this study is to provide a review of machine learning (ML) algorithms that have been reported for analyzing near-infrared (NIR) spectroscopy from traditional machine learning methods to deep network architectures, we also provide different NIR measurement modes, instruments, signal preprocessing methods, etc. Firstly, four different measurement modes available in NIR are reviewed, different types of NIR instruments are compared, and a summary of NIR data analysis methods is provided. Secondly, the public NIR spectroscopy datasets are briefly discussed, with links provided. Thirdly, the widely used data preprocessing and feature selection algorithms that have been reported for NIR spectroscopy are presented. Then, the majority of the traditional machine learning methods and deep network architectures that are commonly employed are covered. Finally, we conclude that developing the integration of a variety of machine learning algorithms in an efficient and lightweight manner is a significant future research direction.
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Affiliation(s)
- Wenwen Zhang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | | | - Qi Jie Wang
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
- School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore 637371, Singapore
| | - Yuanjin Zheng
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Zhiping Lin
- School of Electrical and Electronic Engnineering, Nanyang Technological University, Singapore 639798, Singapore
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6
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Clarke EJ, Lima C, Anderson JR, Castanheira C, Beckett A, James V, Hyett J, Goodacre R, Peffers MJ. Optical photothermal infrared spectroscopy can differentiate equine osteoarthritic plasma extracellular vesicles from healthy controls. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2022; 14:3661-3670. [PMID: 36066093 PMCID: PMC9521322 DOI: 10.1039/d2ay00779g] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 08/25/2022] [Indexed: 05/26/2023]
Abstract
Equine osteoarthritis is a chronic degenerative disease of the articular joint, characterised by cartilage degradation resulting in pain and reduced mobility and thus is a prominent equine welfare concern. Diagnosis is usually at a late stage through clinical examination and radiographic imaging, whilst treatment is symptomatic not curative. Extracellular vesicles are nanoparticles that are involved in intercellular communication. The objective of this study was to investigate the feasibility of Raman and Optical Photothermal Infrared Spectroscopies to detect osteoarthritis using plasma-derived extracellular vesicles, specifically differentiating extracellular vesicles in diseased and healthy controls within the parameters of the techniques used. Plasma samples were derived from thoroughbred racehorses. A total of 14 samples were selected (control; n = 6 and diseased; n = 8). Extracellular vesicles were isolated using differential ultracentrifugation and characterised using nanoparticle tracking analysis, transmission electron microscopy, and human tetraspanin chips. Samples were then analysed using combined Raman and Optical Photothermal Infrared Spectroscopies. Infrared spectra were collected between 950-1800 cm-1. Raman spectra had bands between the wavelengths of 900-1800 cm-1 analysed. Spectral data for both Raman and Optical Photothermal Infrared Spectroscopy were used to generate clustering via principal components analysis and classification models were generated using partial least squared discriminant analysis in order to characterize the techniques' ability to distinguish diseased samples. Optical Photothermal Infrared Spectroscopy could differentiate osteoarthritic extracellular vesicles from healthy with good classification (93.4% correct classification rate) whereas Raman displayed poor classification (correct classification rate = -64.3%). Inspection of the infrared spectra indicated that plasma-derived extracellular vesicles from osteoarthritic horses contained increased signal for proteins, lipids and nucleic acids. For the first time we demonstrated the ability to use optical photothermal infrared spectroscopy combined with Raman spectroscopy to interrogate extracellular vesicles and osteoarthritis-related samples. Optical Photothermal Infrared Spectroscopy was superior to Raman in this study, and could distinguish osteoarthritis samples, suggestive of its potential use diagnostically to identify osteoarthritis in equine patients. This study demonstrates the potential of Raman and Optical Photothermal Infrared Spectroscopy to be used as a future diagnostic tool in clinical practice, with the capacity to detect changes in extracellular vesicles from clinically derived samples.
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Affiliation(s)
- Emily J Clarke
- Department of Musculoskeletal Biology and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, William Henry Duncan Building, 6 W Derby St, Liverpool L7 8TX, UK.
| | - Cassio Lima
- Centre for Metabolomics Research, Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7BE, UK
| | - James R Anderson
- Department of Musculoskeletal Biology and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, William Henry Duncan Building, 6 W Derby St, Liverpool L7 8TX, UK.
| | - Catarina Castanheira
- Department of Musculoskeletal Biology and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, William Henry Duncan Building, 6 W Derby St, Liverpool L7 8TX, UK.
| | - Alison Beckett
- Biomedical Electron Microscopy Unit, University of Liverpool, UK
| | - Victoria James
- School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington, Loughborough LE12 5RD, UK
| | - Jacob Hyett
- Department of Musculoskeletal Biology and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, William Henry Duncan Building, 6 W Derby St, Liverpool L7 8TX, UK.
| | - Royston Goodacre
- Centre for Metabolomics Research, Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Biosciences Building, Crown Street, Liverpool, L69 7BE, UK
| | - Mandy J Peffers
- Department of Musculoskeletal Biology and Ageing Science, Institute of Life Course and Medical Sciences, University of Liverpool, William Henry Duncan Building, 6 W Derby St, Liverpool L7 8TX, UK.
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7
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Cui A, Nippolainen E, Shaikh R, Torniainen J, Ristaniemi A, Finnilä M, Korhonen RK, Saarakkala S, Herzog W, Töyräs J, Afara IO. Assessment of Ligament Viscoelastic Properties Using Raman Spectroscopy. Ann Biomed Eng 2022; 50:1134-1142. [PMID: 35802206 PMCID: PMC9363474 DOI: 10.1007/s10439-022-02988-z] [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: 10/19/2021] [Accepted: 05/31/2022] [Indexed: 11/01/2022]
Abstract
Injuries to the ligaments of the knee commonly impact vulnerable and physically active individuals. These injuries can lead to the development of degenerative diseases such as post-traumatic osteoarthritis (PTOA). Non-invasive optical modalities, such as infrared and Raman spectroscopy, provide means for quantitative evaluation of knee joint tissues and have been proposed as potential quantitative diagnostic tools for arthroscopy. In this study, we evaluate Raman spectroscopy as a viable tool for estimating functional properties of collateral ligaments. Artificial trauma was induced by anterior cruciate ligament transection (ACLT) in the left or right knee joint of skeletally mature New Zealand rabbits. The corresponding contralateral (CL) samples were extracted from healthy unoperated joints along with a separate group of control (CNTRL) animals. The rabbits were sacrificed at 8 weeks after ACLT. The ligaments were then harvested and measured using Raman spectroscopy. A uniaxial tensile stress-relaxation testing protocol was adopted for determining several biomechanical properties of the samples. Partial least squares (PLS) regression models were then employed to correlate the spectral data with the biomechanical properties. Results show that the capacity of Raman spectroscopy for estimating the biomechanical properties of the ligament samples varies depending on the target property, with prediction error ranging from 15.78% for tissue cross-sectional area to 30.39% for stiffness. The hysteresis under cyclic loading at 2 Hz (RMSE = 6.22%, Normalized RMSE = 22.24%) can be accurately estimated from the Raman data which describes the viscous damping properties of the tissue. We conclude that Raman spectroscopy has the potential for non-destructively estimating ligament biomechanical properties in health and disease, thus enhancing the diagnostic value of optical arthroscopic evaluations of ligament integrity.
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Affiliation(s)
- Andy Cui
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
| | - Ervin Nippolainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Rubina Shaikh
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Department of Orthopedics, Traumatology and Hand Surgery, Kuopio University Hospital, Kuopio, Finland
| | - Jari Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Aapo Ristaniemi
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,AO Research Institute Davos, Davos, Switzerland
| | - Mikko Finnilä
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
| | - Rami K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Simo Saarakkala
- Research Unit of Medical Imaging, Physics and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland.,Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Walter Herzog
- Human Performance Lab, Faculty of Kinesiology, University of Calgary, Calgary, Canada
| | - Juha Töyräs
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.,Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Isaac O Afara
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.,Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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8
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Zou H, Lu C, Qiu J. Long non-coding RNA LINC00265 promotes proliferation, apoptosis, and inflammation of chondrocytes in osteoarthritis by sponging miR-101-3p. Autoimmunity 2021; 54:526-538. [PMID: 34633248 DOI: 10.1080/08916934.2021.1978432] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Long non-coding RNAs (lncRNAs) play a part in a wide variety of diseases, including osteoarthritis (OA). This study was designed to investigate the biological role of lncRNA LINC00265 in OA and its underlying mechanisms. We examined the levels of LINC00265 and miR-101-3p using RT-qPCR, inflammatory factors using ELISA, and caspase-3, c-caspase-3, Bcl-2, Bax, and MMP-13 levels using Western blot in normal and OA chondrocytes, analysed the relationship between LINC00265 and miR-101-3p using bioinformatics analysis and luciferase reporter assays, performed loss- and gain-of-function analyses. The results showed that (1) LINC00265 expression was increased in OA chondrocytes, (2) si-LINC00265 inhibited OA chondrocyte apoptosis and inflammation, and (3) LINC00265 overexpression promoted normal and OA chondrocyte apoptosis and inflammation. Furthermore, we predicted and confirmed that miR-101-3p was a target of LINC00265. LINC00265 negatively regulated miR-101-3p in OA chondrocytes and LINC00265 promoted OA and normal chondrocyte apoptosis via miR-101-3p. Overall, lncRNA LINC00265 regulates chondrocyte apoptosis by acting as a sponge of miR-101-3p in OA.
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Affiliation(s)
- Hanlin Zou
- Department of Orthopedics, Shanghai Putuo District Central Hospital, Shanghai City, P. R. China
| | - Chunde Lu
- Department of Orthopedics, Shanghai Jiangong Hospital, Shanghai City, P. R. China
| | - Jianjun Qiu
- Department of Orthopedics, Shanghai Putuo District Central Hospital, Shanghai City, P. R. China
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9
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Khan B, Kafian-Attari I, Nippolainen E, Shaikh R, Semenov D, Hauta-Kasari M, Töyräs J, Afara IO. Articular cartilage optical properties in the near-infrared (NIR) spectral range vary with depth and tissue integrity. BIOMEDICAL OPTICS EXPRESS 2021; 12:6066-6080. [PMID: 34745722 PMCID: PMC8548021 DOI: 10.1364/boe.430053] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 07/27/2021] [Accepted: 08/09/2021] [Indexed: 05/02/2023]
Abstract
Optical properties of biological tissues in the NIR spectral range have demonstrated significant potential for in vivo diagnostic applications and are critical parameters for modelling light interaction in biological tissues. This study aims to investigate the optical properties of articular cartilage as a function of tissue depth and integrity. The results suggest consistent wavelength-dependent variation in optical properties between cartilage depth-wise zones, as well as between healthy and degenerated tissue. Also, statistically significant differences (p<0.05) in both optical properties were observed between the different cartilage depth-wise zones and as a result of tissue degeneration. When taken into account, the outcome of this study could enable accurate modelling of light interaction in cartilage matrix and could provide useful diagnostic information on cartilage integrity.
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Affiliation(s)
- Bilour Khan
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Iman Kafian-Attari
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Ervin Nippolainen
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Rubina Shaikh
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
| | - Dmitry Semenov
- University of Eastern Finland, School of Computing, Lämsikatu 15, Joensuu, Finland, 80110, Finland
| | - Markku Hauta-Kasari
- University of Eastern Finland, School of Computing, Lämsikatu 15, Joensuu, Finland, 80110, Finland
| | - Juha Töyräs
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
- The University of Queensland, School of Information Technology, and Electrical Engineering, St. Lucia, Australia, QLD 4072, Australia
| | - Isaac O. Afara
- University of Eastern Finland, Department of Applied Physics, Yliopistonranta 1, Kuopio, Finland, 70120, Finland
- The University of Queensland, School of Information Technology, and Electrical Engineering, St. Lucia, Australia, QLD 4072, Australia
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10
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Vibrational Spectroscopy in Assessment of Early Osteoarthritis-A Narrative Review. Int J Mol Sci 2021; 22:ijms22105235. [PMID: 34063436 PMCID: PMC8155859 DOI: 10.3390/ijms22105235] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 05/07/2021] [Accepted: 05/13/2021] [Indexed: 12/21/2022] Open
Abstract
Osteoarthritis (OA) is a degenerative disease, and there is currently no effective medicine to cure it. Early prevention and treatment can effectively reduce the pain of OA patients and save costs. Therefore, it is necessary to diagnose OA at an early stage. There are various diagnostic methods for OA, but the methods applied to early diagnosis are limited. Ordinary optical diagnosis is confined to the surface, while laboratory tests, such as rheumatoid factor inspection and physical arthritis checks, are too trivial or time-consuming. Evidently, there is an urgent need to develop a rapid nondestructive detection method for the early diagnosis of OA. Vibrational spectroscopy is a rapid and nondestructive technique that has attracted much attention. In this review, near-infrared (NIR), infrared, (IR) and Raman spectroscopy were introduced to show their potential in early OA diagnosis. The basic principles were discussed first, and then the research progress to date was discussed, as well as its limitations and the direction of development. Finally, all methods were compared, and vibrational spectroscopy was demonstrated that it could be used as a promising tool for early OA diagnosis. This review provides theoretical support for the application and development of vibrational spectroscopy technology in OA diagnosis, providing a new strategy for the nondestructive and rapid diagnosis of arthritis and promoting the development and clinical application of a component-based molecular spectrum detection technology.
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11
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Li L, Wu J, Yang L, Wang H, Xu Y, Shen K. Fourier Transform Infrared Spectroscopy: An Innovative Method for the Diagnosis of Ovarian Cancer. Cancer Manag Res 2021; 13:2389-2399. [PMID: 33737836 PMCID: PMC7965685 DOI: 10.2147/cmar.s291906] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 02/03/2021] [Indexed: 12/24/2022] Open
Abstract
Ovarian cancer is the most lethal gynecologic malignancy due to the late diagnoses at advanced stages, drug resistance and the high recurrence rate. Thus, there is an urgent need to develop new techniques to diagnose and monitor ovarian cancer patients. Fourier transform infrared (FTIR) spectroscopy has great potential in the diagnosis of this disease, as well as the real-time monitoring of cancer development and chemoresistance. As a noninvasive, simple and convenient technique, it can not only distinguish the molecular differences between normal and malignant tissues, but also be used to identify the characteristics of different types of ovarian cancer. FTIR spectroscopy is also widely used in monitoring cancer cells in response to antitumor drugs, distinguishing cells in different growth states, and identifying new synthetic drugs. In this paper, the applications of FTIR spectroscopy for ovarian cancer diagnosis and other works carried out so far are described in detail.
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Affiliation(s)
- Lei Li
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jinguang Wu
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Limin Yang
- State Key Laboratory of Nuclear Physics and Technology, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, People's Republic of China
| | - Huizi Wang
- Medical Science Research Center, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yizhuang Xu
- Beijing National Laboratory for Molecular Sciences, State Key Laboratory for Rare Earth Materials Chemistry and Applications, College of Chemistry and Molecular Engineering, Peking University, Beijing, People's Republic of China
| | - Keng Shen
- Department of Obstetrics and Gynecology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
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12
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Sarin JK, Te Moller NCR, Mohammadi A, Prakash M, Torniainen J, Brommer H, Nippolainen E, Shaikh R, Mäkelä JTA, Korhonen RK, van Weeren PR, Afara IO, Töyräs J. Machine learning augmented near-infrared spectroscopy: In vivo follow-up of cartilage defects. Osteoarthritis Cartilage 2021; 29:423-432. [PMID: 33359249 DOI: 10.1016/j.joca.2020.12.007] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/06/2020] [Accepted: 12/11/2020] [Indexed: 02/02/2023]
Abstract
OBJECTIVE To assess the potential of near-infrared spectroscopy (NIRS) for in vivo arthroscopic monitoring of cartilage defects. METHOD Sharp and blunt cartilage grooves were induced in the radiocarpal and intercarpal joints of Shetland ponies and monitored at baseline (0 weeks) and at three follow-up timepoints (11, 23, and 39 weeks) by measuring near-infrared spectra in vivo at and around the grooves. The animals were sacrificed after 39 weeks and the joints were harvested. Spectra were reacquired ex vivo to ensure reliability of in vivo measurements and for reference analyses. Additionally, cartilage thickness and instantaneous modulus were determined via computed tomography and mechanical testing, respectively. The relationship between the ex vivo spectra and cartilage reference properties was determined using convolutional neural network. RESULTS In an independent test set, the trained networks yielded significant correlations for cartilage thickness (ρ = 0.473) and instantaneous modulus (ρ = 0.498). These networks were used to predict the reference properties at baseline and at follow-up time points. In the radiocarpal joint, cartilage thickness increased significantly with both groove types after baseline and remained swollen. Additionally, at 39 weeks, a significant difference was observed in cartilage thickness between controls and sharp grooves. For the instantaneous modulus, a significant decrease was observed with both groove types in the radiocarpal joint from baseline to 23 and 39 weeks. CONCLUSION NIRS combined with machine learning enabled determination of cartilage properties in vivo, thereby providing longitudinal evaluation of post-intervention injury development. Additionally, radiocarpal joints were found more vulnerable to cartilage degeneration after damage than intercarpal joints.
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Affiliation(s)
- J K Sarin
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - N C R Te Moller
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
| | - A Mohammadi
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - M Prakash
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
| | - J Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
| | - H Brommer
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands.
| | - E Nippolainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R Shaikh
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - J T A Mäkelä
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - R K Korhonen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - P R van Weeren
- Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Regenerative Medicine Utrecht, Utrecht, the Netherlands.
| | - I O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - J Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland; Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Australia.
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13
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Afara IO, Shaikh R, Nippolainen E, Querido W, Torniainen J, Sarin JK, Kandel S, Pleshko N, Töyräs J. Characterization of connective tissues using near-infrared spectroscopy and imaging. Nat Protoc 2021; 16:1297-1329. [PMID: 33462441 DOI: 10.1038/s41596-020-00468-z] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 11/20/2020] [Indexed: 02/06/2023]
Abstract
Near-infrared (NIR) spectroscopy is a powerful analytical method for rapid, non-destructive and label-free assessment of biological materials. Compared to mid-infrared spectroscopy, NIR spectroscopy excels in penetration depth, allowing intact biological tissue assessment, albeit at the cost of reduced molecular specificity. Furthermore, it is relatively safe compared to Raman spectroscopy, with no risk of laser-induced photothermal damage. A typical NIR spectroscopy workflow for biological tissue characterization involves sample preparation, spectral acquisition, pre-processing and analysis. The resulting spectrum embeds intrinsic information on the tissue's biomolecular, structural and functional properties. Here we demonstrate the analytical power of NIR spectroscopy for exploratory and diagnostic applications by providing instructions for acquiring NIR spectra, maps and images in biological tissues. By adapting and extending this protocol from the demonstrated application in connective tissues to other biological tissues, we expect that a typical NIR spectroscopic study can be performed by a non-specialist user to characterize biological tissues in basic research or clinical settings. We also describe how to use this protocol for exploratory study on connective tissues, including differentiating among ligament types, non-destructively monitoring changes in matrix formation during engineered cartilage development, mapping articular cartilage proteoglycan content across bovine patella and spectral imaging across the depth-wise zones of articular cartilage and subchondral bone. Depending on acquisition mode and experiment objectives, a typical exploratory study can be completed within 6 h, including sample preparation and data analysis.
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Affiliation(s)
- Isaac O Afara
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.
| | - Rubina Shaikh
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Ervin Nippolainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - William Querido
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Jari Torniainen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Jaakko K Sarin
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
| | - Shital Kandel
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Nancy Pleshko
- Department of Bioengineering, Temple University, Philadelphia, PA, USA
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia
- Diagnostic Imaging Centre, Kuopio University Hospital, Kuopio, Finland
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14
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Xu Y, Zhang Y, Shi M, Lu Z. Environmental variation, functional diversity and identity predicting community biomass in an old-growth subtropical broad-leaved forest. Glob Ecol Conserv 2020. [DOI: 10.1016/j.gecco.2020.e01093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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15
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Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy. Cell Mol Bioeng 2020; 13:219-228. [PMID: 32426059 PMCID: PMC7225230 DOI: 10.1007/s12195-020-00612-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Accepted: 02/18/2020] [Indexed: 12/15/2022] Open
Abstract
Introduction Assessment of cartilage integrity during arthroscopy is limited by the subjective visual nature of the technique. To address this shortcoming in diagnostic evaluation of articular cartilage, near infrared spectroscopy (NIRS) has been proposed. In this study, we evaluated the capacity of NIRS, combined with machine learning techniques, to classify cartilage integrity. Methods Rabbit (n = 14) knee joints with artificial injury, induced via unilateral anterior cruciate ligament transection (ACLT), and the corresponding contra-lateral (CL) joints, including joints from separate non-operated control (CNTRL) animals (n = 8), were used. After sacrifice, NIR spectra (1000–2500 nm) were acquired from different anatomical locations of the joints (nTOTAL = 313: nCNTRL = 111, nCL = 97, nACLT = 105). Machine and deep learning methods (support vector machines–SVM, logistic regression–LR, and deep neural networks–DNN) were then used to develop models for classifying the samples based solely on their NIR spectra. Results The results show that the model based on SVM is optimal of distinguishing between ACLT and CNTRL samples (ROC_AUC = 0.93, kappa = 0.86), LR is capable of distinguishing between CL and CNTRL samples (ROC_AUC = 0.91, kappa = 0.81), while DNN is optimal for discriminating between the different classes (multi-class classification, kappa = 0.48). Conclusion We show that NIR spectroscopy, when combined with machine learning techniques, is capable of holistic assessment of cartilage integrity, with potential for accurately distinguishing between healthy and diseased cartilage. Electronic supplementary material The online version of this article (10.1007/s12195-020-00612-5) contains supplementary material, which is available to authorized users.
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Meng X, Zhang X, Shi X, Qiu K, Wang Z, Wang D, Zhao J, Li X, Yang Z, Li P. Designing a super broadband near infrared material Mg3Y2Ge3O12:Cr3+ using cation inversion for future light sources. RSC Adv 2020; 10:19106-19116. [PMID: 35518283 PMCID: PMC9053909 DOI: 10.1039/d0ra01742f] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2020] [Accepted: 04/21/2020] [Indexed: 11/21/2022] Open
Abstract
A series of broad emission band near infrared materials Mg3Y2Ge3O12:Cr3+ (650–1200 nm) was prepared based on cation inversion. For trivalent chromium ions (Cr3+), garnet structural components can provide conditions for the occurrence of cation inversion. With an increase in Cr3+ concentration, the Mg2+ and Ge4+ cations are inverted to ensure valence equilibrium, which was explained by recording the low temperature spectrum of the structure and carrying out structural refinement. As a result, this structure provides a new luminescent center [GeO6] for Cr3+, leading to a secondary enhancement in emission intensity. The wavelength of the main peak was found to move from 771 to 811 nm, and the full width at half maximum (FWHM) was broadened from 180 to 226 nm. The lattice occupation, luminescence mechanism and the reasons behind the red-shift and broadening of the spectra were studied in detail. By analyzing the crystallinity and particle size distributions of the samples, as well as the Cr3+ ion energy level shift, it was determined that cation inversion is an effective method that can be used to tune the luminescence performance. Meanwhile, a super broad near infrared light emitting diode (LED) with a FWHM of 260 nm was obtained by combining a GaN chip with MYG:0.40Cr3+. A series of broad emission band near infrared materials Mg3Y2Ge3O12:Cr3+ (650–1200 nm) was prepared based on cation inversion.![]()
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Protective Effect of Genistein on Condylar Cartilage through Downregulating NF- κB Expression in Experimentally Created Osteoarthritis Rats. BIOMED RESEARCH INTERNATIONAL 2019; 2019:2629791. [PMID: 32083119 PMCID: PMC7011344 DOI: 10.1155/2019/2629791] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 11/12/2019] [Accepted: 12/04/2019] [Indexed: 12/13/2022]
Abstract
Temporomandibular joint osteoarthrosis (TMJOA) is characterised by chronic inflammatory changes, with subsequent gradual loss of joint cartilage. NF-κB is a crucial transcription factor in the course of inflammatory and immune responses, which are involved in OA pathology activated by proinflammatory cytokines. Genistein is known to have anti-inflammation and modulation of metabolic pathways through repression of the NF-κB signaling pathway in inflammatory disease. But so far, studies on the effects of genistein on TMJOA are very limited. So, the purpose of this study is to investigate the protective effect of genistein against experimentally induced condylar cartilage degradation through downregulating NF-κB expression in created osteoarthritis rats in vivo. Male SD rats were created as temporomandibular joint osteoarthritis models and administered through oral gavage with low and high dosage genistein (30 mg/kg and 180 mg/kg, respectively) daily for 4 weeks. The morphological changes of the condylar cartilage were studied with HE and Masson staining. The expressions of p65 and inflammatory cytokines (IL-1β and TNFα) were detected using immunohistochemistry and real-time PCR. The results showed that experimentally created osteoarthritis reduced the condylar cartilage thickness of rats and increased the gene expression of cytokines (IL-1β and TNFα) and positive cells of p65. Genistein treatment had positive effects on the condylar cartilage renovation, while high dose genistein treatment had more significant effects on the reversing of OA changes and reduction of the expression of p65 and inflammatory cytokines (IL-1β and TNFα). The results indicated that high dose genistein treatment had obvious therapeutic effects on condyle cartilage damages of OA rats. The mechanism may be that genistein suppresses the NF-κB expression activated by inflammatory cytokines.
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18
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Dataset on equine cartilage near infrared spectra, composition, and functional properties. Sci Data 2019; 6:164. [PMID: 31471536 PMCID: PMC6717194 DOI: 10.1038/s41597-019-0170-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Accepted: 06/19/2019] [Indexed: 12/14/2022] Open
Abstract
Near infrared (NIR) spectroscopy is a well-established technique that is widely employed in agriculture, chemometrics, and pharmaceutical engineering. Recently, the technique has shown potential in clinical orthopaedic applications, for example, assisting in the diagnosis of various knee-related diseases (e.g., osteoarthritis) and their pathologies. NIR spectroscopy (NIRS) could be especially useful for determining the integrity and condition of articular cartilage, as the current arthroscopic diagnostics is subjective and unreliable. In this work, we present an extensive dataset of NIRS measurements for evaluating the condition, mechanical properties, structure, and composition of equine articular cartilage. The dataset contains NIRS measurements from 869 different locations across the articular surfaces of five equine fetlock joints. A comprehensive library of reference values for each measurement location is also provided, including results from a mechanical indentation testing, digital densitometry imaging, polarized light microscopy, and Fourier transform infrared spectroscopy. The published data can either be used as a model of human cartilage or to advance equine veterinary research.
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19
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Morais CLM, Paraskevaidi M, Cui L, Fullwood NJ, Isabelle M, Lima KMG, Martin-Hirsch PL, Sreedhar H, Trevisan J, Walsh MJ, Zhang D, Zhu YG, Martin FL. Standardization of complex biologically derived spectrochemical datasets. Nat Protoc 2019; 14:1546-1577. [PMID: 30953040 DOI: 10.1038/s41596-019-0150-x] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 02/12/2019] [Indexed: 12/17/2022]
Abstract
Spectroscopic techniques such as Fourier-transform infrared (FTIR) spectroscopy are used to study interactions of light with biological materials. This interaction forms the basis of many analytical assays used in disease screening/diagnosis, microbiological studies, and forensic/environmental investigations. Advantages of spectrochemical analysis are its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, an urgent need exists for repetition and validation of these methods in large-scale studies and across different research groups, which would bring the method closer to clinical and/or industrial implementation. For this to succeed, it is important to understand and reduce the effect of random spectral alterations caused by inter-individual, inter-instrument and/or inter-laboratory variations, such as variations in air humidity and CO2 levels, and aging of instrument parts. Thus, it is evident that spectral standardization is critical to the widespread adoption of these spectrochemical technologies. By using calibration transfer procedures, in which the spectral response of a secondary instrument is standardized to resemble the spectral response of a primary instrument, different sources of variation can be normalized into a single model using computational-based methods, such as direct standardization (DS) and piecewise direct standardization (PDS); therefore, measurements performed under different conditions can generate the same result, eliminating the need for a full recalibration. Here, we have constructed a protocol for model standardization using different transfer technologies described for FTIR spectrochemical applications. This is a critical step toward the construction of a practical spectrochemical analysis model for daily routine analysis, where uncertain and random variations are present.
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Affiliation(s)
- Camilo L M Morais
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK.
| | - Maria Paraskevaidi
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK.
| | - Li Cui
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China
| | - Nigel J Fullwood
- Division of Biomedical and Life Sciences, Faculty of Health and Medicine, Lancaster University, Lancaster, UK
| | - Martin Isabelle
- Spectroscopy Products Division, Renishaw plc., New Mills, Wotton-under-Edge, UK
| | - Kássio M G Lima
- Institute of Chemistry, Biological Chemistry and Chemometrics, Federal University of Rio Grande do Norte, Natal, Brazil
| | - Pierre L Martin-Hirsch
- Department of Obstetrics and Gynaecology, Lancashire Teaching Hospitals NHS Foundation, Preston, UK
| | - Hari Sreedhar
- Department of Pathology, University of Illinois at Chicago, Chicago, IL, USA
| | - Júlio Trevisan
- Institute of Astronomy, Geophysics and Atmospheric Sciences, University of São Paulo, São Paulo, Brazil
| | - Michael J Walsh
- Department of Pathology, University of Illinois at Chicago, Chicago, IL, USA
| | - Dayi Zhang
- School of Environment, Tsinghua University, Beijing, China
| | - Yong-Guan Zhu
- Key Lab of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China
| | - Francis L Martin
- School of Pharmacy and Biomedical Sciences, University of Central Lancashire, Preston, UK.
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Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy. Sci Rep 2018; 8:9733. [PMID: 29950563 PMCID: PMC6021410 DOI: 10.1038/s41598-018-27786-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2018] [Accepted: 06/06/2018] [Indexed: 12/16/2022] Open
Abstract
Degenerative joint conditions are often characterized by changes in articular cartilage and subchondral bone properties. These changes are often associated with subchondral plate thickness and trabecular bone morphology. Thus, evaluating subchondral bone integrity could provide essential insights for diagnosis of joint pathologies. This study investigates the potential of optical spectroscopy for characterizing human subchondral bone properties. Osteochondral samples (n = 50) were extracted from human cadaver knees (n = 13) at four anatomical locations and subjected to NIR spectroscopy. The samples were then imaged using micro-computed tomography to determine subchondral bone morphometric properties, including: plate thickness (Sb.Th), trabecular thickness (Tb.Th), volume fraction (BV/TV), and structure model index (SMI). The relationship between the subchondral bone properties and spectral data in the 1st (650–950 nm), 2nd (1100–1350 nm) and 3rd (1600–1870 nm) optical windows were investigated using partial least squares (PLS) regression multivariate technique. Significant correlations (p < 0.0001) and relatively low prediction errors were obtained between spectral data in the 1st optical window and Sb.Th (R2 = 92.3%, error = 7.1%), Tb.Th (R2 = 88.4%, error = 6.7%), BV/TV (R2 = 83%, error = 9.8%) and SMI (R2 = 79.7%, error = 10.8%). Thus, NIR spectroscopy in the 1st tissue optical window is capable of characterizing and estimating subchondral bone properties, and can potentially be adapted during arthroscopy.
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