FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Annals of biomedical engineering2016; 44(11); 3335-3345; doi: 10.1007/s10439-016-1659-6

Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage.

Abstract: Mechanical properties of articular cartilage are vital for normal joint function, which can be severely compromised by injuries. Quantitative characterization of cartilage injuries, and evaluation of cartilage stiffness and thickness by means of conventional arthroscopy is poorly reproducible or impossible. In this study, we demonstrate the potential of near infrared (NIR) spectroscopy for predicting and mapping the functional properties of equine articular cartilage at and around lesion sites. Lesion and non-lesion areas of interests (AI, N = 44) of equine joints (N = 5) were divided into grids and NIR spectra were acquired from all grid points (N = 869). Partial least squares (PLS) regression was used to investigate the correlation between the absorbance spectra and thickness, equilibrium modulus, dynamic modulus, and instantaneous modulus at the grid points of 41 AIs. Subsequently, the developed PLS models were validated with spectral data from the grid points of 3 independent AIs. Significant correlations were obtained between spectral data and cartilage thickness (R 2 = 70.3%, p < 0.0001), equilibrium modulus (R 2 = 67.8%, p < 0.0001), dynamic modulus (R 2 = 68.9%, p < 0.0001) and instantaneous modulus (R 2 = 41.8%, p < 0.0001). Relatively low errors were observed in the predicted thickness (5.9%) and instantaneous modulus (9.0%) maps. Thus, if well implemented, NIR spectroscopy could enable arthroscopic evaluation and mapping of cartilage functional properties at and around lesion sites.
Get Full Text
Publication Date: 2016-05-27 PubMed ID: 27234817DOI: 10.1007/s10439-016-1659-6Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research study evaluates the use of near infrared (NIR) spectroscopy in predicting and mapping the functional properties of horse joint cartilage, particularly around areas of injury. Conventional methods of assessing cartilage injuries can have poor reproducibility, so this new approach could potentially enhance the accuracy of diagnosis and monitoring.

Methodology

  • The researchers procured sections of equine joints, with both normal and injured areas, totaling 44 different zones of interest (AI).
  • These AI zones were then split into grids, and this resulted in 869 individual grid points, from which spectroscopic readings could be taken.
  • The researchers collected the NIR spectra – a form of light that can penetrate the surface of the tissue – from these grid points.
  • Following data collection, a statistical technique called partial least squares (PLS) regression was used to find potential correlations between the spectroscope readings and the thickness, equilibrium modulus (a measure of the cartilage stiffness when in a state of stress equilibrium), dynamic modulus (measure of stiffness under conditions of changing stress), and instantaneous modulus (a measure of stiffness at a given instant) at the grid points of 41 AIs.
  • The PLS models were then validated by using them to predict the properties of independent AIs.

Results

  • Significant correlations were found between the spectral data and each of the cartilage properties measured. Particularly, the thickness (70.3%), equilibrium modulus (67.8%). dynamic modulus (68.9%), and instantaneous modulus (41.8%).
  • When the models were used to predict the properties of the independent test samples, they exhibited relatively low error rates for thickness (5.9%) and instantaneous modulus (9.0%).

Conclusion

  • The study shows that near infrared spectroscopy has the potential to be used in the assessment and mapping of functional properties, such as thickness and stiffness, of articular (joint) cartilage, particularly around areas of injury.
  • This would be a significant advance over traditional arthroscopic (a minimally invasive procedure used to examine and treat damage within the joint) methods of evaluation, which have been found to be less reliable.

Cite This Article

APA
Sarin JK, Amissah M, Brommer H, Argüelles D, Töyräs J, Afara IO. (2016). Near Infrared Spectroscopic Mapping of Functional Properties of Equine Articular Cartilage. Ann Biomed Eng, 44(11), 3335-3345. https://doi.org/10.1007/s10439-016-1659-6

Publication

Annals of biomedical engineering
ISSN: 1573-9686
NlmUniqueID: 0361512
Country: United States
Language: English
Volume: 44
Issue: 11
Pages: 3335-3345

Researcher Affiliations

Sarin, Jaakko K
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland. jaakko.sarin@uef.fi.
  • Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland. jaakko.sarin@uef.fi.
Amissah, Michael
  • Department of Physics and Mathematics, University of Eastern Finland, Joensuu, Finland.
Brommer, Harold
  • Department of Equine Sciences, Utrecht University, Utrecht, Netherlands.
Argüelles, David
  • School of Veterinary Medicine, Veterinary Teaching Hospital, University of Helsinki, Helsinki, Finland.
Töyräs, Juha
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
Afara, Isaac O
  • Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
  • Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland.
  • Department of Electrical and Computer Engineering, Elizade University, Ondo, Nigeria.

MeSH Terms

  • Animals
  • Cartilage, Articular / injuries
  • Cartilage, Articular / metabolism
  • Cartilage, Articular / pathology
  • Cartilage, Articular / physiopathology
  • Horses
  • Joints / injuries
  • Joints / metabolism
  • Joints / pathology
  • Joints / physiopathology
  • Spectrophotometry, Infrared

Citations

This article has been cited 13 times.
  1. 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 Oct;51(10):2245-2257.
    doi: 10.1007/s10439-023-03261-7pubmed: 37332006google scholar: lookup
  2. Shehata E, Nippolainen E, Shaikh R, Ronkainen AP, Töyräs J, Sarin JK, Afara IO. Raman Spectroscopy and Machine Learning Enables Estimation of Articular Cartilage Structural, Compositional, and Functional Properties. Ann Biomed Eng 2023 Oct;51(10):2301-2312.
    doi: 10.1007/s10439-023-03271-5pubmed: 37328704google scholar: lookup
  3. Querido W, Kandel S, Pleshko N. Applications of Vibrational Spectroscopy for Analysis of Connective Tissues. Molecules 2021 Feb 9;26(4).
    doi: 10.3390/molecules26040922pubmed: 33572384google scholar: lookup
  4. 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 Feb;16(2):1297-1329.
    doi: 10.1038/s41596-020-00468-zpubmed: 33462441google scholar: lookup
  5. Ala-Myllymäki J, Paakkonen T, Joukainen A, Kröger H, Lehenkari P, Töyräs J, Afara IO. Near-Infrared Spectroscopy for Mapping of Human Meniscus Biochemical Constituents. Ann Biomed Eng 2021 Jan;49(1):469-476.
    doi: 10.1007/s10439-020-02578-xpubmed: 32720092google scholar: lookup
  6. Afara IO, Sarin JK, Ojanen S, Finnilä MAJ, Herzog W, Saarakkala S, Korhonen RK, Töyräs J. Machine Learning Classification of Articular Cartilage Integrity Using Near Infrared Spectroscopy. Cell Mol Bioeng 2020 Jun;13(3):219-228.
    doi: 10.1007/s12195-020-00612-5pubmed: 32426059google scholar: lookup
  7. Sarin JK, Torniainen J, Prakash M, Rieppo L, Afara IO, Töyräs J. Dataset on equine cartilage near infrared spectra, composition, and functional properties. Sci Data 2019 Aug 30;6(1):164.
    doi: 10.1038/s41597-019-0170-ypubmed: 31471536google scholar: lookup
  8. Sarin JK, Nykänen O, Tiitu V, Mancini IAD, Brommer H, Visser J, Malda J, van Weeren PR, Afara IO, Töyräs J. Arthroscopic Determination of Cartilage Proteoglycan Content and Collagen Network Structure with Near-Infrared Spectroscopy. Ann Biomed Eng 2019 Aug;47(8):1815-1826.
    doi: 10.1007/s10439-019-02280-7pubmed: 31062256google scholar: lookup
  9. Sarin JK, Te Moller NCR, Mancini IAD, Brommer H, Visser J, Malda J, van Weeren PR, Afara IO, Töyräs J. Arthroscopic near infrared spectroscopy enables simultaneous quantitative evaluation of articular cartilage and subchondral bone in vivo. Sci Rep 2018 Sep 7;8(1):13409.
    doi: 10.1038/s41598-018-31670-5pubmed: 30194446google scholar: lookup
  10. Afara IO, Florea C, Olumegbon IA, Eneh CT, Malo MKH, Korhonen RK, Töyräs J. Characterizing human subchondral bone properties using near-infrared (NIR) spectroscopy. Sci Rep 2018 Jun 27;8(1):9733.
    doi: 10.1038/s41598-018-27786-3pubmed: 29950563google scholar: lookup
  11. Karchner JP, Yousefi F, Bitman SR, Darvish K, Pleshko N. Non-Destructive Spectroscopic Assessment of High and Low Weight Bearing Articular Cartilage Correlates with Mechanical Properties. Cartilage 2019 Oct;10(4):480-490.
    doi: 10.1177/1947603518764269pubmed: 29690771google scholar: lookup
  12. Afara IO, Prasadam I, Arabshahi Z, Xiao Y, Oloyede A. Monitoring osteoarthritis progression using near infrared (NIR) spectroscopy. Sci Rep 2017 Sep 13;7(1):11463.
    doi: 10.1038/s41598-017-11844-3pubmed: 28904358google scholar: lookup
  13. Sarin JK, Rieppo L, Brommer H, Afara IO, Saarakkala S, Töyräs J. Combination of optical coherence tomography and near infrared spectroscopy enhances determination of articular cartilage composition and structure. Sci Rep 2017 Sep 6;7(1):10586.
    doi: 10.1038/s41598-017-10973-zpubmed: 28878384google scholar: lookup
Subscribe to our newsletter
Join 99,727 horse owners like you who receive our email updates on horse health and nutrition.