Tratamiento farmacológico y génico en las distrofias musculares de Duchenne y Becker / Pharmacologic and genetic treatment in the Duchenne and Becker muscular dystrophies

Introducción: Las distrofias musculares son las enfermedades degenerativas más comunes dentro de las enfermedades neuromusculares, cursan con debilidad muscular que progresa hasta la pérdida de la deambulación y en la segunda década de vida surgen complicaciones cardíacas, respiratorias y ortopédicas. Objetivo: Analizar el estado actual de los tratamientos génico y farmacológico en las distrofias musculares de Duchenne y Becker Métodos: Se realizó una búsqueda en los meses de enero, febrero y marzo de 2018 en las bases de datos Medline, Cinhal, Web Of Science y Scopus. Se obtuvieron 232 resultados y después de aplicar los criterios de inclusión y exclusión, se consiguieron para analizar 15 artículos válidos para la revisión. Resultados: Los artículos analizados investigan mayoritariamente el efecto de las terapias mencionadas a nivel de funcionalidad y de síntesis de la proteína distrofina durante períodos largos, en los que participan muestras de tamaño y edades variadas tanto como distrofia muscular de Duchenne y como distrofia muscular de Becker. Conclusiones: Existen más artículos enfocados en la distrofia muscular de Duchenne que en la distrofia muscular de Becker. Esto puede ser debido a que la primera es la más grave y de peor pronóstico. Sigue siendo necesario realizar más estudios para avanzar sobre el estado actual de estos tratamientos(AU)

Introduction: Muscular dystrophies are one of the most common degenerative pathologies within neuromuscular diseases. They present muscular weakness that develops until loss of wandering and in the second decade of life can appear cardiac, respiratory and orthopaedic complications. Objective: To know the current state of genetic and pharmacology treatments in the Duchenne and Becker muscular dystrophies. Methods: A search was made from January to March 2018 at Medline, Cinhal, Web Of Science and Scopus databases. 232 results were obtained, and applying the inclusion and exclusion criteria, 15 acceptable articles for reviewing were found. Results: Analyzed articles mostly investigate the effect of the mentioned therapies in the levels of functionality and dystrophin protein synthesis during long periods, in which samples of different sizes and ages are used. Conclusions: There are more articles focused on Duchenne Muscular Dystrophy than Becker Muscular Dystrophy. That can be due to the fact that the first is the most severe and with the worst prognosis. It is still necessary to carry out more scientific studies to move forward from the current stage of these treatments(AU)

Gene ordering in partitive clustering using microarray expressions.

A central step in the analysis of gene expression data is the identification of groups of genes that exhibit similar expression patterns. Clustering and ordering the genes using gene expression data into homogeneous groups was shown to be useful in functional annotation, tissue classification, regulatory motif identification, and other applications. Although there is a rich literature on gene ordering in hierarchical clustering framework for gene expression analysis, there is no work addressing and evaluating the importance of gene ordering in partitive clustering framework, to the best knowledge of the authors. Outside the framework of hierarchical clustering, different gene ordering algorithms are applied on the whole data set, and the domain of partitive clustering is still unexplored with gene ordering approaches. A new hybrid method is proposed for ordering genes in each of the clusters obtained from partitive clustering solution, using microarray gene expressions.Two existing algorithms for optimally ordering cities in travelling salesman problem (TSP), namely, FRAG_GALK and Concorde, are hybridized individually with self organizing MAP to show the importance of gene ordering in partitive clustering framework. We validated our hybrid approach using yeast and fibroblast data and showed that our approach improves the result quality of partitive clustering solution, by identifying subclusters within big clusters, grouping functionally correlated genes within clusters, minimization of summation of gene expression distances, and the maximization of biological gene ordering using MIPS categorization. Moreover, the new hybrid approach, finds comparable or sometimes superior biological gene order in less computation time than those obtained by optimal leaf ordering in hierarchical clustering solution.