A microphysiological system for studying human bone biology under simultaneous control of oxygen tension and mechanical loading.

Throughout life, continuous remodelling is part of human bone biology and depends on the simultaneous action of physicochemical parameters such as oxygen tension and varying mechanical load. Thus, suitable model systems are needed, which allow concomitant modulation of these factors to recapitulate in vivo bone formation. Here, we report on the development of a first microphysiological system (MPS) that enables perfusion, environment-independent regulation of the oxygen tension as well as precise quantification and control of mechanical load. To demonstrate the use of the MPS for future studies on the (patho-)biology of bone, we built a simplified 3D model for early de novo bone formation. Primary human osteoblasts (OBs), which are the key players during this process, were seeded onto type I collagen scaffolds and cultured in the MPS. We could not only monitor cell viability and metabolism of OBs under varied physicochemical conditions, but also visualise the mineralisation of the extracellular matrix. In summary, we present a MPS that uniquely combines the independent control of physicochemical parameters and allows investigation of their influence on bone biology. We consider our MPS highly valuable to gain deeper insights into (patho-)physiological processes of bone formation in the future.

CT- and MRI-Based 3D Reconstruction of Knee Joint to Assess Cartilage and Bone.

For the observation of human joint cartilage, X-ray, computed tomography (CT) or magnetic resonance imaging (MRI) are the main diagnostic tools to evaluate pathologies or traumas. The current work introduces a set of novel measurements and 3D features based on MRI and CT data of the knee joint, used to reconstruct bone and cartilages and to assess cartilage condition from a new perspective. Forty-seven subjects presenting a degenerative disease, a traumatic injury or no symptoms or trauma were recruited in this study and scanned using CT and MRI. Using medical imaging software, the bone and cartilage of the knee joint were segmented and 3D reconstructed. Several features such as cartilage density, volume and surface were extracted. Moreover, an investigation was carried out on the distribution of cartilage thickness and curvature analysis to identify new markers of cartilage condition. All the extracted features were used with advanced statistics tools and machine learning to test the ability of our model to predict cartilage conditions. This work is a first step towards the development of a new gold standard of cartilage assessment based on 3D measurements.