BackgroundChemoresistance remains a significant challenge in chronic myelogenous leukemia (CML) management, which is one of the most critical prognostic factors. Elucidation the molecular mechanisms underlying the resistance to chemoresistance may lead to better clinical outcomes.ResultsIn order to identify potential protein targets involved in the drug-resistant phenotype of leukemia, especially the chronic myelogenous leukemia (CML), we used a high-resolution “ultra-zoom” 2DE-based proteomics approach to characterize global protein expression patterns in doxorubicin-resistant myelogenous leukemia cells compared with parental control cells. Ultra-high resolution of 2DE was achieved by using a series of slightly overlapping narrow-range IPG strips during isoelectric focusing (IEF) separation. A total number of 44 proteins with altered abundances were detected and identified by MALDI-TOF or LC-MS/MS. Among these proteins, enolase, aldolase, HSP70 and sorcin were up-regulated in doxorubicin-resistant myelogenous leukemia cell line, whereas HSP27 was down-regulated. Some of the results have been validated by Western blotting. Both enolase and aldolase were first reported to be involved in chemoresistance, suggesting that process of glycolysis in doxorubicin-resistant myelogenous leukemia cells was accelerated to some extent to provide more energy to survive chemical stress. Possible roles of most of the identified proteins in development of chemoresistance in myelogenous leukemia cells were fully discussed. The results presented here could provide clues to further study for elucidating the mechanisms underlying drug resistance in leukemia.ConclusionsAs a whole, under the chemical stress, the doxorubicin-resistant myelogenous leukemia cells may employ various protective strategies to survive. These include: (i) pumping the cytotoxic drug out of the cells by P-glycoprotein, (ii) increased storage of fermentable fuel, (iii) sophisticated cellular protection by molecular chaperones, (iv) improved handling of intracellular calcium, (v) increased glucose utilization via increased rates of glycolysis. In the present study, proteomic analysis of leukemia cells and their drug resistant variants revealed multiple alterations in protein expression. Our results indicate that the development of drug resistance in doxorubicin-resistant myelogenous leukemia cells is a complex phenomenon undergoing several mechanisms.Electronic supplementary materialThe online version of this article (doi:10.1186/s12953-014-0057-y) contains supplementary material, which is available to authorized users.
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Australian population health survey (n=34,000) was used to identify the spatial distribution of health and wellbeing outcomes, including general health and social capital. For each outcome, the most and least desirable locations formed two domains. Streetscape design was sampled using around 80,000 Google Street View images per domain. Generative adversarial networks translated these images from one domain to the other, preserving the main structure of the input image, but transforming the 'style' from locations where self-reported health was bad to locations where it was good. These translations indicate that areas in Melbourne with good general health are characterised by sufficient green space and compactness of the urban environment, whilst streetscape imagery related to high social capital contained more and wider footpaths, fewer fences and more grass. Beyond identifying relationships, the method is a first step towards computer-generated design interventions that have the potential to improve population health and wellbeing.
Abstract. Indoor evacuation systems are needed for rescue and safety management. A particular challenge is real-time evacuation route planning for the trapped people. In this paper, an integrated model is proposed for indoor evacuation used on mobile phones. With the purpose of employing real-time sensor data as references for evacuation route calculation, this paper makes an attempt to convert sensor systems to sensor graphs and associate these sensor graphs with route graph. Based on the integration of sensing and routing, sensor tracking and risk aware evacuation routes are generated dynamically for evacuees. Experiments of the proposed model are illustrated in the paper. The benefit of the integrated model could extend to hastily and secure indoor evacuation and it potentially presents an approach to correlate environmental information to geospatial information for indoor application.
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