Document Type : RESEARCH PAPER
Authors
1
Tissue Engineering Group (TEG), National Orthopaedic Centre of Excellence for Research and Learning (NOCERAL), Department of Orthopaedic Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
2
Spine Research Unit (SRU), National Orthopaedic Centre of Excellence for Research and Learning (NOCERAL), Department of Orthopaedic Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
3
International Institute for Halal Research and Training, Level 3, KICT Building, International Islamic University Malaysia (IIUM), Kuala Lumpur 53100, Malaysia
4
Joint Reconstruction Unit (JRU), National Orthopaedic Centre of Excellence in Research and Learning (NOCERAL), Department of Orthopaedic Surgery, Faculty of Medicine, Universiti Malaya, Kuala Lumpur 50603, Malaysia
5
Department of Biochemistry, Selcuk university, Ardıçlı, İsmetpaşa Cad, 42250 Selçuklu/Konya, Türkiye
10.22038/abjs.2025.83723.3810
Abstract
Background: Osteoarthritis (OA) is the most common form of arthritis resulting in joint deterioration. Currently, OA treatment primarily focuses on symptom management. The development of tools suitable for diagnosis is needed to support a paradigm shift towards the prevention of OA rather than treatment. Therefore, having effective diagnostic tools for early detection of OA is crucial.
Methods: The present study aimed to develop a rapid, inexpensive, and reliable detection method using attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and chemometrics using synovial fluid (SF) for the early diagnosis of osteoarthritis (OA). A preliminary sample consisting of 10 participants in the OA group and 10 in the non-OA group was used to establish the feasibility of the method. All synovial fluid samples were handled uniformly, using fresh drops of 50 µL from each sample ten times, resulting in a total of 200 infrared spectra collected and analysed, revealing significant differences that effectively separated the OA and non-OA groups, demonstrating the potential of this approach for future larger-scale studies.
Results: A significant discrepancy seen in distinguishing SF samples from different categories via variance spectra specifically highlighted by wavenumber 551 cm-1. The predictive model achieved an accuracy rate of 85%, demonstrating promising results.
Conclusion: Our findings suggest that a discriminative model using the ATR-FTIR spectrum could enhance early diagnosis of human OA, providing superior results compared to using serum. This approach reflects the localized joint condition more accurately than serum, which reflects systemic condition.
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