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One of the major problems influencing the therapeutic efficacy of stem cell therapy is the poor cell survival following transplantation. This is partly attributed to insufficient resistance of transplanted stem cells to oxidative and inflammatory stresses at the injured sites. In the current study, we demonstrated the pivotal role of antioxidant levels in human umbilical cord mesenchymal stem cells (hUCMSCs) dynamic in vitro anti-stress abilities against lipopolysaccharide (LPS)/H2O2 intoxication and in vivo therapeutic efficacy in a murine acute liver failure model induced by D-galactosamine/LPS (Gal/LPS) by either reducing the antioxidant levels with diethyl maleate (DEM) or increasing antioxidant levels with edaravone. Both the anti- and pro-oxidant treatments dramatically influenced the survival, apoptosis, and reactive oxygen species (ROS) production of hUCMSCs through the MAPK-PKC-Nrf2 pathway in vitro. When compared with untreated and DEM-treated cells, edaravone-treated hUCMSCs rescued NOD/SCID mice from Gal/LPS-induced death, significantly improved hepatic functions and promoted host liver regeneration. These effects were probably from increased stem cell homing, promoted proliferation, decreased apoptosis and enhanced secretion of hepatocyte growth factor (HGF) under hepatic stress environment. In conclusion, elevating levels of antioxidants in hUCMSCs with edaravone can significantly influence their hepatic tissue repair capacity.

BackgroundDiabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency.ObjectiveThis study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs).MethodsThis study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014).ResultsOf the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days).ConclusionsThe online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.