Single-Channel Ecg-Based Sleep Stage Classification With End-To-End Trainable Deep Neural Networks.

The demand for automatic sleep stage classification using easily obtainable signals like electrocardiograms (ECGs) is rising due to the growing number of sleep disorder cases. Our study examined the potential of using single-channel ECG signals for user-friendly automatic sleep stage classification. Unlike previous studies that relied on manual features such as heart rate and variability, we propose using fully neural network-based features.

Characterization and expression analysis of mesenchymal stem cells from human bone marrow and adipose tissue.

Human mesenchymal stem cells (MSC), that have been reported to be present in bone marrow, adipose tissues, dermis, muscles and peripheral blood, have the potential to differentiate along different lineages including those forming bone, cartilage, fat, muscle and neuron. This differentiation potential makes MSC excellent candidates for cell-based tissue engineering. In this study, we have examined phenotypes and gene expression profile of the human adipose tissue-derived stromal cells (ATSC) in the undifferentiated states, and compared with that of bone marrow stromal cells (BMSC).