Human Oncostatin M

Background: Hepatic myofibroblasts (MFs) can originate from hepatic stellate cells, portal fibroblasts, or bone marrow-derived mesenchymal stem cells and can migrate towards the site of injury by aligning with nascent and established fibrotic septa in response to several mediators. Oncostatin M (OSM) is known to orchestrate hypoxia-modulated hepatic processes involving the hypoxia-inducible factor 1 (HIF-1). Methods. In vivo and in vitro experiments were performed to analyze the expression of OSM and OSM-receptor (OSMR) in three murine models of non-alcoholic-fatty liver disease (NAFLD) and -steatohepatitis (NASH) and in human NASH patients as well as the action of OSM on phenotypic responses of human MFs. Results: Hepatic OSM and OSMR levels were overexpressed in three murine NASH models and in NASH patients. OSM stimulates migration in human MFs by involving early intracellular ROS generation and activation of Ras/Erk, JNK1/2, PI3K/Akt as well as STAT1/STAT3 pathways and HIF-1 alpha. OSM-dependent migration relies on a biphasic mechanism requiring early intracellular generation of reactive oxygen species (ROS) and late HIF1-dependent expression and release of VEGF. Conclusion: OSM is overexpressed in experimental and human progressive NAFLD and can act as a profibrogenic factor by directly stimulating migration of hepatic MFs.

OpenStreetMap (OSM) is a free, open-access Volunteered geographic information (VGI) platform that has been widely used over the last decade as a source for Land Use Land Cover (LULC) mapping and visualization. However, it is known that the spatial coverage and accuracy of OSM data are not evenly distributed across all regions, with urban areas being likelier to have promising contributions (in both quantity and quality) than rural areas. The present study used OSM data history to generate LULC datasets with one-year timeframes as a way to support regional and rural multi-temporal LULC mapping. We evaluated the degree to which the different OSM datasets agreed with two existing reference datasets (CORINE Land Cover and the official Portuguese Land Cover Map). We also evaluated whether our OSM dataset was of sufficiently high quality (in terms of both completeness accuracy and thematic accuracy) to be used as a sampling data source for multi-temporal LULC maps. In addition, we used the near boundary tag accuracy criterion to assesses the fitness of the OSM data for producing training samples, with promising results. For each annual dataset, the completeness ratio of the coverage area for the selected study area was low. Nevertheless, we found high thematic accuracy values (ranged from 77.3% to 91.9%). Additionally, the training samples thematic accuracy improved as they moved away from the features' boundaries. Features with larger areas (>10 ha), e.g., Agriculture and Forest, had a steadily positive correlation between training samples accuracy and distance to feature boundaries.