Effet de l'ET-1 sur le système MMP/TIMP dans les chondrocytes arthrosiques

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal

Endothelin-1 in osteoarthritic chondrocytes triggers nitric oxide production and upregulates collagenase production

The mechanism of endothelin-1 (ET-1)-induced nitric oxide (NO) production, MMP-1 production and MMP-13 production was investigated in human osteoarthritis chondrocytes. The cells were isolated from human articular cartilage obtained at surgery and were cultured in the absence or presence of ET-1 with or without inhibitors of protein kinase or LY83583 (an inhibitor of soluble guanylate cyclase and of cGMP). MMP-1, MMP-13 and NO levels were then measured by ELISA and Griess reaction, respectively. Additionally, inducible nitric oxide synthase (iNOS) and phosphorylated forms of p38 mitogen-activated protein kinase, p44/42, stress-activated protein kinase/Jun-N-terminal kinase and serine-threonine Akt kinase were determined by western blot. Results show that ET-1 greatly increased MMP-1 and MMP-13 production, iNOS expression and NO release. LY83583 decreased the production of both metalloproteases below basal levels, whereas the inhibitor of p38 kinase, SB202190, suppressed ET-1-stimulated production only. Similarly, the ET-1-induced NO production was partially suppressed by the p38 kinase inhibitor and was completely suppressed by the protein kinase A kinase inhibitor KT5720 and by LY83583, suggesting the involvement of these enzymes in relevant ET-1 signalling pathways. In human osteoarthritis chondrocytes, ET-1 controls the production of MMP-1 and MMP-13. ET-1 also induces NO release via iNOS induction. ET-1 and NO should thus become important target molecules for future therapies aimed at stopping cartilage destruction

The effectiveness of scoliosis screening programs: methods for systematic review and expert panel recommendations formulation

Background: Literature on scoliosis screening is vast, however because of the observational nature of available data and methodological flaws, data interpretation is often complex, leading to incomplete and sometimes, somewhat misleading conclusions. The need to propose a set of methods for critical appraisal of the literature about scoliosis screening, a comprehensive summary and rating of the available evidence appeared essential. METHODS: To address these gaps, the study aims were: i) To propose a framework for the assessment of published studies on scoliosis screening effectiveness; ii) To suggest specific questions to be answered on screening effectiveness instead of trying to reach a global position for or against the programs; iii) To contextualize the knowledge through expert panel consultation and meaningful recommendations. The general methodological approach proceeds through the following steps: Elaboration of the conceptual framework; Formulation of the review questions; Identification of the criteria for the review; Selection of the studies; Critical assessment of the studies; Results synthesis; Formulation and grading of recommendations in response to the questions. This plan follows at best GRADE Group (Grades of Recommendation, Assessment, Development and Evaluation) requirements for systematic reviews, assessing quality of evidence and grading the strength of recommendations. CONCLUSIONS: In this article, the methods developed in support of this work are presented since they may be of some interest for similar reviews in scoliosis and orthopaedic fields.Canadian Institutes of Health Research (CIHR) by three means: CIHR Research Operating Grants (2004–2007, 2008–2011); Canada Graduate Scholarships Doctoral Awards (MB) and CIHR MENTOR and AnEIS Strategic training programs doctoral awards (MB)

3D morphology prediction of progressive spinal deformities from probabilistic modeling of discriminant manifolds

We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding low-dimensional coordinates. A discriminant adjacency matrix is constructed to maximize the separation between progressive and non-progressive groups of patients diagnosed with scoliosis, while minimizing the distance in latent variables belonging to the same class. To predict the evolution of deformation, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. Rate of progression is modulated from the spine flexibility and curve magnitude of the 3D spine deformation. The method was tested on 745 reconstructions from 133 subjects using longitudinal 3D reconstructions of the spine, with results demonstrating the discriminatory framework can identify between progressive and non-progressive of scoliotic patients with a classification rate of 81% and prediction differences of 2.1$^{o}$ in main curve angulation, outperforming other manifold learning methods. Our method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in highly deformed spines compared to other learning methods