The role of CCN2 in cartilage and bone development

CCN2, a classical member of the CCN family of matricellular proteins, is a key molecule that conducts cartilage development in a harmonized manner through novel molecular actions. During vertebrate development, all cartilage is primarily formed by a process of mesenchymal condensation, while CCN2 is induced to promote this process. Afterwards, cartilage develops into several subtypes with different fates and missions, in which CCN2 plays its proper roles according to the corresponding microenvironments. The history of CCN2 in cartilage and bone began with its re-discovery in the growth cartilage in long bones, which determines the skeletal size through the process of endochondral ossification. CCN2 promotes physiological developmental processes not only in the growth cartilage but also in the other types of cartilages, i.e., Meckel’s cartilage representing temporary cartilage without autocalcification, articular cartilage representing hyaline cartilage with physical stiffness, and auricular cartilage representing elastic cartilage. Together with its significant role in intramembranous ossification, CCN2 is regarded as a conductor of skeletogenesis. During cartilage development, the CCN2 gene is dynamically regulated to yield stage-specific production of CCN2 proteins at both transcriptional and post-transcriptional levels. New functional aspects of known biomolecules have been uncovered during the course of investigating these regulatory systems in chondrocytes. Since CCN2 promotes integrated regeneration as well as generation (=development) of these tissues, its utility in regenerative therapy targeting chondrocytes and osteoblasts is indicated, as has already been supported by experimental evidence obtained in vivo

Death of a tumor: targeting CCN in pancreatic cancer

The matricellular protein CCN2 (connective tissue growth factor, CTGF) has been previously implicated in tumorigenesis. In pancreatic cancer cells, CCN2 expression occurs downstream of ras/MEK/ERK. Direct evidence that CCN2 mediates tumor progression in pancreatic cancer has been lacking. An exciting recent report by Bennewith et al. (Cancer Res 69:775–784, 2009) has used shRNA knockdown of CCN2 to illustrate that CCN2 contributes to growth of pancreatic tumor cells, both in vitro and in vivo. This report briefly summarizes these findings

A novel PI3K inhibitor iMDK suppresses non-small cell lung Cancer cooperatively with A MEK inhibitor

The PI3K–AKT pathway is expected to be a therapeutic target for non-small cell lung cancer (NSCLC) treatment. We previously reported that a novel PI3K inhibitor iMDK suppressed NSCLC cells in vitro and in vivo without harming normal cells and mice. Unexpectedly, iMDK activated the MAPK pathway, including ERK, in the NSCLC cells. Since iMDK did not eradicate such NSCLC cells completely, it is possible that the activated MAPK pathway confers resistance to the NSCLC cells against cell death induced by iMDK. In the present study, we assessed whether suppressing of iMDK-mediated activation of the MAPK pathway would enhance anti-tumorigenic activity of iMDK. PD0325901, a MAPK inhibitor, suppressed the MAPK pathway induced by iMDK and cooperatively inhibited cell viability and colony formation of NSCLC cells by inducing apoptosis in vitro. HUVEC tube formation, representing angiogenic processes in vitro, was also cooperatively inhibited by the combinatorial treatment of iMDK and PD0325901. The combinatorial treatment of iMDK with PD0325901 cooperatively suppressed tumor growth and tumor-associated angiogenesis in a lung cancer xenograft model in vivo. Here, we demonstrate a novel treatment strategy using iMDK and PD0325901 to eradicate NSCLC

PSR J1926-0652: A Pulsar with Interesting Emission Properties Discovered at FAST

We describe PSR J1926-0652, a pulsar recently discovered with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Using sensitive single-pulse detections from FAST and long-term timing observations from the Parkes 64-m radio telescope, we probed phenomena on both long and short time scales. The FAST observations covered a wide frequency range from 270 to 800 MHz, enabling individual pulses to be studied in detail. The pulsar exhibits at least four profile components, short-term nulling lasting from 4 to 450 pulses, complex subpulse drifting behaviours and intermittency on scales of tens of minutes. While the average band spacing P3 is relatively constant across different bursts and components, significant variations in the separation of adjacent bands are seen, especially near the beginning and end of a burst. Band shapes and slopes are quite variable, especially for the trailing components and for the shorter bursts. We show that for each burst the last detectable pulse prior to emission ceasing has different properties compared to other pulses. These complexities pose challenges for the classic carousel-type models.Comment: 13pages with 12 figure

Classification across gene expression microarray studies

<p>Abstract</p> <p>Background</p> <p>The increasing number of gene expression microarray studies represents an important resource in biomedical research. As a result, gene expression based diagnosis has entered clinical practice for patient stratification in breast cancer. However, the integration and combined analysis of microarray studies remains still a challenge. We assessed the potential benefit of data integration on the classification accuracy and systematically evaluated the generalization performance of selected methods on four breast cancer studies comprising almost 1000 independent samples. To this end, we introduced an evaluation framework which aims to establish good statistical practice and a graphical way to monitor differences. The classification goal was to correctly predict estrogen receptor status (negative/positive) and histological grade (low/high) of each tumor sample in an independent study which was not used for the training. For the classification we chose support vector machines (SVM), predictive analysis of microarrays (PAM), random forest (RF) and k-top scoring pairs (kTSP). Guided by considerations relevant for classification across studies we developed a generalization of kTSP which we evaluated in addition. Our derived version (DV) aims to improve the robustness of the intrinsic invariance of kTSP with respect to technologies and preprocessing.</p> <p>Results</p> <p>For each individual study the generalization error was benchmarked via complete cross-validation and was found to be similar for all classification methods. The misclassification rates were substantially higher in classification across studies, when each single study was used as an independent test set while all remaining studies were combined for the training of the classifier. However, with increasing number of independent microarray studies used in the training, the overall classification performance improved. DV performed better than the average and showed slightly less variance. In particular, the better predictive results of DV in across platform classification indicate higher robustness of the classifier when trained on single channel data and applied to gene expression ratios.</p> <p>Conclusions</p> <p>We present a systematic evaluation of strategies for the integration of independent microarray studies in a classification task. Our findings in across studies classification may guide further research aiming on the construction of more robust and reliable methods for stratification and diagnosis in clinical practice.</p