Adaptation et validation arabe de l’échelle de l’estime de Soi de Rosenberg chez des adolescents collégiens marocains

Vu l’intérêt que revêt l’estime de soi à l’adolescence et l’importance d’une haute estime de soi globale dans la promotion du bien être psychologique et d’une bonne santé mentale, nous avons effectué cette étude dans le but d’adapter et de valider une version arabe de l’échelle de l’estime de soi de Rosenberg de type Likert chez une population marocaine adolescente, De ce fait, nous avons réalisé une traduction de l'échelle par la méthode back-translation (Brislin, 1970), puis nous l’avons administrée auprès de 100 adolescents collégiens marocains (dont 63 filles), âgés de 13 à 18 ans. L’analyse de la composante principale (ACP) a permis la validation de notre échelle de mesure via l’extraction d’un facteur principal représentant et reflétant significativement le maximum d’informations contenues dans les 10 items. La version arabe de l'échelle a montré des niveaux satisfaisants de cohérence interne et de stabilité temporelle (test-retest) sur une période de deux semaines. Les résultats n'ont révélé aucun effet de l’âge sur l'estime de soi globale. De même, ils n’ont souligné aucune différence significative entre les sexes. Ces résultats soutiennent l'utilisation de la version arabe de RSES pour l'évaluation de l'estime globale de soi des adolescents dans l'enseignement secondaire marocain

Silk Fibroin as Adjuvant in the Fabrication of Mechanically Stable Fibrin Biocomposites

Fibrin is a very attractive material for the development of tissue-engineered scaffolds due to its exceptional bioactivity, versatility in the fabrication, affinity to cell mediators; and the possibility to isolate it from blood plasma, making it autologous. However, fibrin application is greatly limited due to its low mechanical properties, fast degradation, and strong contraction in the presence of cells. In this study, we present a new strategy to overcome these drawbacks by combining it with another natural polymer: silk fibroin. Specifically, we fabricated biocomposites of fibrin (5 mg/mL) and silk fibroin (0.1, 0.5 and 1% w/w) by using a dual injection system, followed by ethanol annealing. The shear elastic modulus increased from 23 ± 5 Pa from fibrin alone, to 67 ± 22 Pa for fibrin/silk fibroin 0.1%, 241 ± 67 Pa for fibrin/silk fibroin 0.5% and 456 ± 32 Pa for fibrin/silk fibroin 1%. After culturing for 27 days with strong contractile cells (primary human arterial smooth muscle cells), fibrin/silk fibroin 0.5% and fibrin/silk fibroin 1% featured minimal cell-mediated contraction (ca. 15 and 5% respectively) in contrast with the large surface loss of the pure fibrin scaffolds (ca. 95%). Additionally, the composites enabled the formation of a proper endothelial cell layer after culturing with human primary endothelial cells under standard culture conditions. Overall, the fibrin/silk fibroin composites, manufactured within this study by a simple and scalable biofabrication approach, offer a promising avenue to boost the applicability of fibrin in tissue engineering

Business plan d’une plateforme commerciale de déstockage de vêtements B2C

Mémoire de Master [120] en sciences de gestion (horaire décalé), Université catholique de Louvain, 201

Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait

Diagnosing Parkinson's disease is a complex task that requires the evaluation of several motor and non-motor symptoms. During diagnosis, gait abnormalities are among the important symptoms that physicians should consider. However, gait evaluation is challenging and relies on the expertise and subjectivity of clinicians. In this context, the use of an intelligent gait analysis algorithm may assist physicians in order to facilitate the diagnosis process. This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information. We used 1D convolutional neural network (1D-Convnet) to build a Deep Neural Network (DNN) classifier. The proposed model processes 18 1D-signals coming from foot sensors measuring the vertical ground reaction force (VGRF). The first part of the network consists of 18 parallel 1D-Convnet corresponding to system inputs. The second part is a fully connected network that connects the concatenated outputs of the 1D-Convnets to obtain a final classification. We tested our algorithm in Parkinson's detection and in the prediction of the severity of the disease with the Unified Parkinson's Disease Rating Scale (UPDRS). Our experiments demonstrate the high efficiency of the proposed method in the detection of Parkinson disease based on gait data. The proposed algorithm achieved an accuracy of 98.7 %. To our knowledge, this is the state-of-the-start performance in Parkinson's gait recognition. Furthermore, we achieved an accuracy of 85.3 % in Parkinson's severity prediction. To the best of our knowledge, this is the first algorithm to perform a severity prediction based on the UPDRS. Our results show that the model is able to learn intrinsic characteristics from gait data and to generalize to unseen subjects, which could be helpful in a clinical diagnosis.Comment: Source code available at https://github.com/imanneelmaachi/Parkinson-disease-detection-and-severity-prediction-from-gai