192 research outputs found

    Gremlin Enhances the Determined Path to Cardiomyogenesis

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    BACKGROUND: The critical event in heart formation is commitment of mesodermal cells to a cardiomyogenic fate, and cardiac fate determination is regulated by a series of cytokines. Bone morphogenetic proteins (BMPs) and fibroblast growth factors have been shown to be involved in this process, however additional factors needs to be identified for the fate determination, especially at the early stage of cardiomyogenic development. METHODOLOGY/PRINCIPAL FINDINGS: Global gene expression analysis using a series of human cells with a cardiomyogenic potential suggested Gremlin (Grem1) is a candidate gene responsible for in vitro cardiomyogenic differentiation. Grem1, a known BMP antagonist, enhanced DMSO-induced cardiomyogenesis of P19CL6 embryonal carcinoma cells (CL6 cells) 10-35 fold in an area of beating differentiated cardiomyocytes. The Grem1 action was most effective at the early differentiation stage when CL6 cells were destined to cardiomyogenesis, and was mediated through inhibition of BMP2. Furthermore, BMP2 inhibited Wnt/beta-catenin signaling that promoted CL6 cardiomyogenesis. CONCLUSIONS/SIGNIFICANCE: Grem1 enhances the determined path to cardiomyogenesis in a stage-specific manner, and inhibition of the BMP signaling pathway is involved in initial determination of Grem1-promoted cardiomyogenesis. Our results shed new light on renewal of the cardiovascular system using Grem1 in human

    Neuroprotective effect of 5-aminolevulinic acid against low inorganic phosphate in neuroblastoma SH-SY5Y cells

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    PiT-1 (encoded by SLC20A1) and PiT-2 (encoded by SLC20A2) are type-III sodium-dependent phosphate cotransporters (NaPiTs). Recently, SLC20A2 mutations have been found in patients with idiopathic basal ganglia calcification (IBGC), and were predicted to bring about an inability to transport Pi from the extracellular environment. Here we investigated the effect of low Pi loading on the human neuroblastoma SH-SY5Y and the human glioblastoma A172 cell lines. The results show a different sensitivity to low Pi loading and differential regulation of type-III NaPiTs in these cells. We also examined whether 5-aminolevulinic acid (5-ALA) inhibited low Pi loading-induced neurotoxicity in SH-SY5Y cells. Concomitant application of 5-ALA with low Pi loading markedly attenuated low Pi-induced cell death and mitochondrial dysfunction via the induction of HO-1 by p38 MAPK. The findings provide us with novel viewpoints to understand the pathophysiology of IBGC, and give a new insight into the clinical prevention and treatment of IBGC

    Neuromorphic computing using non-volatile memory

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    Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neural networks (DNNs), and ‘Memcomputing’. In SNNs, NVM synaptic connections are updated by a local learning rule such as spike-timing-dependent-plasticity, a computational approach directly inspired by biology. For DNNs, NVM arrays can represent matrices of synaptic weights, implementing the matrix–vector multiplication needed for algorithms such as backpropagation in an analog yet massively-parallel fashion. This approach could provide significant improvements in power and speed compared to GPU-based DNN training, for applications of commercial significance. We then survey recent research in which different types of NVM devices – including phase change memory, conductive-bridging RAM, filamentary and non-filamentary RRAM, and other NVMs – have been proposed, either as a synapse or as a neuron, for use within a neuromorphic computing application. The relevant virtues and limitations of these devices are assessed, in terms of properties such as conductance dynamic range, (non)linearity and (a)symmetry of conductance response, retention, endurance, required switching power, and device variability.11Yscopu
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