BackgroundDespite it being known that subchondral bone affects the viscoelasticity of cartilage, there has been little research into the mechanical properties of osteochondral tissue as a whole system. This study aims to unearth new knowledge concerning the dynamic behaviour of human subchondral bone and how energy is transferred through the cartilage-bone interface.MethodsDynamic mechanical analysis was used to determine the frequency-dependent (1–90 Hz) viscoelastic properties of the osteochondral unit (cartilage-bone system) as well as isolated cartilage and bone specimens extracted from human femoral heads obtained from patients undergoing total hip replacement surgery, with a mean age of 78 years (N = 5, n = 22). Bone mineral density (BMD) was also determined for samples using micro-computed tomography as a marker of tissue health.ResultsCartilage storage and loss moduli along with bone storage modulus were found to increase logarithmically (p < 0.05) with frequency. The mean cartilage storage modulus was 34.4 ± 3.35 MPa and loss modulus was 6.17 ± 0.48 MPa (mean ± standard deviation). In contrast, bone loss modulus decreased logarithmically between 1 and 90 Hz (p < 0.05). The storage stiffness of the cartilage-bone-core was found to be frequency-dependent with a mean value of 1016 ± 54.0 N.mm− 1, while the loss stiffness was determined to be frequency-independent at 78.84 ± 2.48 N.mm− 1. Notably, a statistically significant (p < 0.05) linear correlation was found between the total energy dissipated from the isolated cartilage specimens, and the BMD of the isolated bone specimens at all frequencies except at 90 Hz (p = 0.09).ConclusionsThe viscoelastic properties of the cartilage-bone core were significantly different to the tissues in isolation (p < 0.05). Results from this study demonstrate that the functionality of these tissues arises because they operate as a unit. This is evidenced through the link between cartilage energy dissipated and bone BMD. The results may provide insights into the functionality of the osteochondral unit, which may offer further understanding of disease progression, such as osteoarthritis (OA). Furthermore, the results emphasise the importance of studying human tissue, as bovine models do not always display the same trends.

The objective differentiation of facets of cellular metabolism is important for several clinical applications, including accurate definition of tumour boundaries and targeted wound debridement. To this end, spectral biomarkers to differentiate live and necrotic/apoptotic cells have been defined using in vitro methods. The delineation of different cellular states using spectroscopic methods is difficult due to the complex nature of these biological processes. Sophisticated, objective classification methods will therefore be important for such differentiation. In this study, spectral data from healthy/traumatised cell samples using hyperspectral imaging between 2500-3500 nm were collected using a portable prototype device. Machine learning algorithms, in the form of clustering, have been performed on a variety of pre-processing data types including 'raw' unprocessed, smoothed resampling, background subtracted and spectral derivative. The resulting clusters were utilised as a diagnostic tool for the assessment of cellular health and quantified using both sensitivity and specificity to compare the different analysis methods. The raw data exhibited differences for one of the three different trauma types applied, although unable to accurately cluster all the traumatised samples due to signal contamination from the chemical insult. The background subtracted and smoothed data sets reduced the accuracy further, due to the apparent removal of key spectral features which exhibit cellular health. However, the spectral derivative data-types significantly improved the accuracy of clustering compared to other data types, with both sensitivity and specificity for the background subtracted data set being >94% highlighting its utility to account for unknown signal contamination while maintaining important cellular spectral features.

No abstract

Compressive sensing methods demonstrate the ability to collect large data sets using a reduced number of observations. Here we present advantages of compressive sensing applied to Spatial Frequency Domain Imaging.