Heterotopic mineralization may result in tendon weakness, but effects on other biomechanical responses have not been reported. We used a needle injury, which accelerates spontaneous mineralization of murine Achilles tendons, to test two hypotheses: that injured tendons would demonstrate altered biomechanical responses; and that unilateral injury would accelerate mineralization bilaterally. Mice underwent left hind (LH) injury (I; n ¼ 11) and were euthanized after 20 weeks along with non-injured controls (C; n ¼ 9). All hind limbs were examined by micro computed tomography followed by biomechanical testing (I ¼ 7 and C ¼ 6). No differences were found in the biomechanical responses of injured tendons compared with controls. However, the right hind (RH) tendons contralateral to the LH injury exhibited greater static creep strain and total creep strain compared with those LH tendons (p 0.045) and RH tendons from controls (p 0.043). RH limb lesions of injured mice were three times larger compared with controls (p ¼ 0.030). Therefore, despite extensive mineralization, changes to the responses we measured were limited or absent 20 weeks postinjury. These results also suggest that bilateral occurrence should be considered where tendon mineralization is identified clinically. This experimental system may be useful to study the mechanisms of bilateral new bone formation in tendinopathy and other conditions. ß

Although alterations in knee joint loading resulting from injury have been shown to influence the development of osteoarthritis, actual in vivo loading conditions of the joint remain unknown. A method for determining in vivo ligament loads by reproducing joint specific in vivo kinematics using a robotic testing apparatus is described. The in vivo kinematics of the ovine stifle joint during walking were measured with 3D optical motion analysis using markers rigidly affixed to the tibia and femur. An additional independent single degree of freedom measuring device was also used to record a measure of motion. Following sacrifice, the joint was mounted in a robotic/universal force sensor test apparatus and referenced using a coordinate measuring machine. A parallel robot configuration was chosen over the conventional serial manipulator because of its greater accuracy and stiffness. Median normal gait kinematics were applied to the joint and the resulting accuracy compared. The mean error in reproduction as determined by the motion analysis system varied between 0.06 mm and 0.67 mm and 0.07 deg and 0.74 deg for the two individual tests. The mean error measured by the independent device was found to be 0.07 mm and 0.83 mm for the two experiments, respectively. This study demonstrates the ability of this system to reproduce in vivo kinematics of the ovine stifle joint in vitro. The importance of system stiffness is discussed to ensure accurate reproduction of joint motion.

These authors contributed equally to this study.A computational method to differentiate normal individuals, osteoarthritis and rheumatoid arthritis patients using serum biomarkers The objective of this study was to develop a method for categorizing normal individuals (normal, n ¼ 100) as well as patients with osteoarthritis (OA, n ¼ 100), and rheumatoid arthritis (RA, n ¼ 100) based on a panel of inflammatory cytokines expressed in serum samples. Two panels of inflammatory proteins were used as training sets in the construction of two separate artificial neural networks (ANNs). The first training set consisted of all proteins (38 in total) and the second consisted of only the significantly different proteins expressed (12 in total) between at least two patient groups. Both ANNs obtained high levels of sensitivity and specificity, with the first and second ANN each diagnosing 100% of test set patients correctly. These results were then verified by re-investigating the entire dataset using a decision tree algorithm. We show that ANNs can be used for the accurate differentiation between serum samples of patients with OA, a diagnosed RA patient comparator cohort and normal/control cohort. Using neural network and systems biology approaches to manage large datasets derived from highthroughput proteomics should be further explored and considered for diagnosing diseases with complex pathologies.