Effects of Controlling Abnormal Joint Movement on Expression of MMP13 and TIMP-1 in Osteoarthritis.

OBJECTIVE: Abnormal joint movement is associated with osteoarthritis (OA). Previous studies using the controlling abnormal joint movement (CAJM) model of OA reported delayed cartilage degeneration; however, none of them focused on gait performance and the localization of matrix metalloproteinase 13 (MMP13) and tissue inhibitor of metalloproteinase-1 (TIMP-1) in chondrocytes. Therefore, we aimed to investigate the effect of controlling abnormal joint movement on gait performance and the localization of MMP13 and TIMP-1, using kinematic and histological analyses. DESIGN: Rats were assigned to 2 groups: anterior cruciate ligament transection (ACL-T) group and CAJM group (n = 5/group); contralateral hind limbs of ACL-T rats were designated as intact. After 1, 2, and 4 weeks, step length was analyzed, and after 2, 4, and 8 weeks, Safranin O-Fast Green staining and immunohistochemical staining for MMP13 and TIMP-1 were performed. RESULTS: Step length did not differ significantly between the groups. However, degeneration of articular cartilage was higher in the ACL-T group than in the intact group (P < 0.05). There was no significant difference in the CAJM group at all time points. Immunohistochemical analysis of the MMP13/TIMP-1 relationship revealed a significant increase in the expression ratio of MMP13 after 4 weeks in the ACL-T group compared to the CAJM group (P < 0.05). CONCLUSIONS: Controlling abnormal joint movement may reduce mechanical stress owing to kinematic elements of small articulation including joint instability and delayed cartilage degeneration, despite the lack of kinematic change in step length.

Controlling Abnormal Joint Movement Inhibits Response of Osteophyte Formation.

Objective Osteoarthritis (OA) is induced by accumulated mechanical stress to joints; however, little has been reported regarding the cause among detailed mechanical stress on cartilage degeneration. This study investigated the influence of the control of abnormal joint movement induced by anterior cruciate ligament (ACL) injury in the articular cartilage. Design The animals were divided into 3 experimental groups: CAJM group ( n = 22: controlling abnormal joint movement), ACL-T group ( n = 22: ACL transection or knee anterior instability increased), and INTACT group ( n = 12: no surgery). After 2 and 4 weeks, the knees were harvested for digital microscopic observation, soft X-ray analysis, histological analysis, and synovial membrane molecular evaluation. Results The 4-week OARSI scores showed that cartilage degeneration was significantly inhibited in the CAJM group as compared with the ACL-T group ( P < 0.001). At 4 weeks, the osteophyte formation had also significantly increased in the ACL-T group ( P < 0.001). These results reflected the microscopic scoring and soft X-ray analysis findings at 4 weeks. Real-time synovial membrane polymerase chain reaction analysis for evaluation of the osteophyte formation-associated factors showed that the mRNA expression of BMP-2 and VEGF in the ACL-T group had significantly increased after 2 weeks. Conclusions Typically, abnormal mechanical stress induces osteophyte formation; however, our results demonstrated that CAJM group inhibited osteophyte formation. Therefore, controlling abnormal joint movement may be a beneficial precautionary measure for OA progression in the future.

Robustness of learning algorithms using hinge loss with outlier indicators.

We propose a unified formulation of robust learning methods for classification and regression problems. In the learning methods, the hinge loss is used with outlier indicators in order to detect outliers in the observed data. To analyze the robustness property, we evaluate the breakdown point of the learning methods in the situation that the outlier ratio is not necessarily small. Although minimization of the hinge loss with outlier indicators is a non-convex optimization problem, we prove that any local optimal solution of our learning algorithms has the robustness property. The theoretical findings are confirmed in numerical experiments.

DC Algorithm for Extended Robust Support Vector Machine.

Nonconvex variants of support vector machines (SVMs) have been developed for various purposes. For example, robust SVMs attain robustness to outliers by using a nonconvex loss function, while extended [Formula: see text]-SVM (E[Formula: see text]-SVM) extends the range of the hyperparameter by introducing a nonconvex constraint. Here, we consider an extended robust support vector machine (ER-SVM), a robust variant of E[Formula: see text]-SVM. ER-SVM combines two types of nonconvexity from robust SVMs and E[Formula: see text]-SVM. Because of the two nonconvexities, the existing algorithm we proposed needs to be divided into two parts depending on whether the hyperparameter value is in the extended range or not. The algorithm also heuristically solves the nonconvex problem in the extended range. In this letter, we propose a new, efficient algorithm for ER-SVM. The algorithm deals with two types of nonconvexity while never entailing more computations than either E[Formula: see text]-SVM or robust SVM, and it finds a critical point of ER-SVM. Furthermore, we show that ER-SVM includes the existing robust SVMs as special cases. Numerical experiments confirm the effectiveness of integrating the two nonconvexities.

Outlier detection at the transcriptome-proteome interface.

BACKGROUND: In high-throughput experimental biology, it is widely acknowledged that while expression levels measured at the levels of transcriptome and the corresponding proteome do not, in general, correlate well, messenger RNA levels are used as convenient proxies for protein levels. Our interest is in developing data-driven computational models that can bridge the gap between these two levels of measurement at which different mechanisms of regulation may act on different molecular species causing any observed lack of correlations. To this end, we build data-driven predictors of protein levels using mRNA levels and known proxies of translation efficiencies as covariates. Previous work showed that in such a setting, outliers with respect to the model are reliable candidates for post-translational regulation. RESULTS: Here, we present and compare two novel formulations of deriving a protein concentration predictor from which outliers may be extracted in a systematic manner. The first approach, outlier rejecting regression, allows explicit specification of a certain fraction of the data as outliers. In a regression setting, this is a non-convex optimization problem which we solve by deriving a difference of convex functions algorithm (DCA). With post-translationally regulated proteins, one expects their concentrations to be affected primarily by disruption of protein stability. Our second algorithm exploits this observation by minimizing an asymmetric loss using quantile regression and extracts outlier proteins whose measured concentrations are lower than what a genome-wide regression would predict. We validate the two approaches on a dataset of yeast transcriptome and proteome. Functional annotation check on detected outliers demonstrate that the methods are able to identify post-translationally regulated genes with high statistical confidence.

Extended robust support vector machine based on financial risk minimization.

Financial risk measures have been used recently in machine learning. For example, ν-support vector machine ν-SVM) minimizes the conditional value at risk (CVaR) of margin distribution. The measure is popular in finance because of the subadditivity property, but it is very sensitive to a few outliers in the tail of the distribution. We propose a new classification method, extended robust SVM (ER-SVM), which minimizes an intermediate risk measure between the CVaR and value at risk (VaR) by expecting that the resulting model becomes less sensitive than ν-SVM to outliers. We can regard ER-SVM as an extension of robust SVM, which uses a truncated hinge loss. Numerical experiments imply the ER-SVM's possibility of achieving a better prediction performance with proper parameter setting.

Involvement of moesin in the development of morphine analgesic tolerance through P-glycoprotein at the blood-brain barrier.

Altered expression of P-glycoprotein (P-gp), a drug efflux transporter expressed by brain capillary endothelial cells (BCECs), may contribute to the development of opioid analgesic tolerance, as demonstrated by cumulative evidence from research. However, the detailed mechanism by which chronic morphine treatment increases P-gp expression remains unexplained. Ezrin/radixin/moesin (ERM) are scaffold proteins that are known to regulate the plasma membrane localization of some drug transporters such as P-gp in peripheral tissues, although a few reports suggest its role in the central nervous system as well. In this study, we investigated the involvement of ERM in the development of morphine analgesic tolerance through altered P-gp expression in BCECs. Repeated treatment with morphine (10 mg/kg/day, s.c. for 5 days) decreased its analgesic effect in the tail-flick test and increased P-gp protein expression in BCECs, as determined by Western blotting. Furthermore, moesin protein expression increased in the same fraction whereas that of ezrin decreased; no change was observed in the radixin expression. Furthermore, immunoprecipitation and immunofluorescence assays revealed interaction between moesin and P-gp molecules, along with co-localization, in BCECs. In conclusion, an increase in moesin expression may contribute to the increased expression of P-gp in BCECs, leading to the development of morphine analgesic tolerance.