iPSCs ameliorate hypoxia-induced autophagy and atrophy in C2C12 myotubes via the AMPK/ULK1 pathway

BACKGROUND: Duchenne muscular dystrophy (DMD) is an X-linked lethal genetic disorder for which there is no effective treatment. Previous studies have shown that stem cell transplantation into mdx mice can promote muscle regeneration and improve muscle function, however, the specific molecular mechanisms remain unclear. DMD suffers varying degrees of hypoxic damage during disease progression. This study aimed to investigate whether induced pluripotent stem cells (iPSCs) have protective effects against hypoxia-induced skeletal muscle injury. RESULTS: In this study, we co-cultured iPSCs with C2C12 myoblasts using a Transwell nested system and placed them in a DG250 anaerobic workstation for oxygen deprivation for 24 h. We found that iPSCs reduced the levels of lactate dehydrogenase and reactive oxygen species and downregulated the mRNA and protein levels of BAX/BCL2 and LC3II/ LC3I in hypoxia-induced C2C12 myoblasts. Meanwhile, iPSCs decreased the mRNA and protein levels of atrogin-1 and MuRF-1 and increased myotube width. Furthermore, iPSCs downregulated the phosphorylation of AMPKA and ULK1 in C2C12 myotubes exposed to hypoxic damage. CONCLUSIONS: Our study showed that iPSCs enhanced the resistance of C2C12 myoblasts to hypoxia and inhibited apoptosis and autophagy in the presence of oxidative stress. Further, iPSCs improved hypoxia-induced autophagy and atrophy of C2C12 myotubes through the AMPK/ULK1 pathway. This study may provide a new theoretical basis for the treatment of muscular dystrophy in stem cells.

The Feasibility of Using Classification and Identification Techniques to Auto-Assess the Quality of Health Information on the Web / 대한의료정보학회지

OBJECTIVE: An automatic detection tool was created for examining health-related webpage quality we went further by examining its feasibility and performance. METHODS: We developed an automatic detection system to auto-assess the authorship quality indicator of an health-related information webpage for governmental websites in Taiwan. The system was integrated with the Chinese word segmentation system developed by the Academia Sinica in Taiwan and the SVM(light), which serve as an SVM (Support Vector Machine) Classifiers and a method of information extraction and identification. The system was coded in Visual Basic 6.0, using SQL 2000. RESULTS: We developed the first Chinese automatic webpage classification and information identifier to evaluate the quality of web information. The sensitivity and specificity of the classifier on the training set of webpages were both as high as 100% and only one health webpage in the test set was misclassified, due to the fact that it contained both health and non-health information content. The sensitivity of our authorship identifier is 75.3%, with a specificity of 87.9%. CONCLUSION: The technical feasibility of auto-assessment for the quality of health information on the web is acceptable. Although it is not sufficient to assure the total quality of web contents, it is good enough to be used to support the entire quality assurance program.