Refractive index of human articular cartilage varies with tissue structure and composition.

Optical properties of biological tissues, such as refractive index, are fundamental properties, intrinsically linked to a tissue's composition and structure. This study aims to investigate the variation of refractive index (RI) of human articular cartilage along the tissue depth (via collagen fibril orientation and optical density) and integrity (based on Mankin and Osteoarthritis Research Society International (OARSI) scores). The results show the relationship between RI and PG content (p=0.042), collagen orientation (p=0.037), and OARSI score (p=0.072). When taken into account, the outcome of this study suggests that the RI of healthy cartilage differs from that of pathological cartilage (p=0.072). This could potentially provide knowledge on how progressive tissue degeneration, such as osteoarthritis, affects changes in cartilage RI, which can, in turn, be used as a potential optical biomarker of tissue pathology.

Infrared Spectroscopy Can Differentiate Between Cartilage Injury Models: Implication for Assessment of Cartilage Integrity.

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4-13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.