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Abstract: Much of the excitement generated by induced pluripotent stem cell technology is concerned with the possibility of disease modeling as well as the potential for personalized cell therapy. However, to pursue this it is important to understand the ‘normal’ pluripotent state including its inherent variability. We have performed various molecular profiling assays for 21 hESC lines and 8 hiPSC lines to generate a comprehensive snapshot of the undifferentiated state of pluripotent stem cells. Analysis of the gene expression data revealed no iPSC-specific gene expression pattern in accordance with previous reports. We further compared cells, differentiated as embryoid bodies in 2 media proposed to initiate differentiation towards separate cell fates, as well as 20 adult tissues. From this analysis we have generated a gene list which defines pluripotency and establishes a baseline for the pluripotent state. Finally, we provide lists of genes enriched under both differentiation conditions which show the proposed bias toward independent cell fates. [Copyright &y& Elsevier]

Introduction: Precision medicine is an important milestone toward the attainment of personalized medicine. A learning health system (LHS) may facilitate the evidence collection and knowledge generation process for disease‐based research and for the diagnosis, classification, or treatment of each disease subtype to improve patient care. Methods: The LHS design and implementation used by Taichung Veterans General Hospital (TCVGH) in Taiwan for their newly funded precision medicine research, a dementia registry study, was modeled from an LHS developed at the National Institutes of Health in the United States. This Clinical Informatics and Management System (CIMS), including its subsystems, facilitates and enhances operations associated with the institutional review board, clinical research data collection and study management, the hospital biobank, and the participating health research centers to support their precision medicine research aimed at improving patient care. Results: The implementation of a shared‐design, full‐cycle LHS with an enhanced CIMS, combined with hospital‐based real‐world data marts, has made the TCVGH dementia registry study a reality. The research data, including clinical assessment and genomics analysis information collected in CIMS, combined with data marts, are the foundation of the TCVGH dementia registry for outcome analyses. These high‐quality datasets are useful for clinical validation, new hypotheses, and knowledge generation, leading to new clinical recommendations or guidelines for better patient treatment and care. The cyclic data flow supports the full‐cycle LHS for TCVGH's dementia research to improve the care of elderly patients. Conclusions: Knowledge generation requires high‐quality research and health care datasets. While the details of LHS implementation methods in the United States and Taiwan may differ slightly, the LHS concept design and basic system architecture, with improved CIMSs, were proven feasible. As a result, learning health processes in support of translational research and the potential for improvement in patient care were significantly facilitated. [ABSTRACT FROM AUTHOR]