A Review of Epistemology and Subject Areas in MIS Research

The purpose of this paper is to classify the most cited papers in Management Information Systems (MIS) by theoretical perspective and subject area. The determination of the underlying theoretical perspective of these papers facilitates and verifies the dominance of positivist perspectives. Our analysis indicates that 74% of the most cited articles are positivist and 26% are interpretivist. The presence of a significant percentage of interpretive work suggests that differing theoretical perspectives are being considered relevant to solving the problems identified in the current research streams. Our results also indicated User Satisfaction and Instrument Development and Group Support Systems as the most cited articles subject areas, 16% and 14% respectively. The significance of these subject areas promotes and supports that systems is the foundation of MIS

An open-source platform for pediatric cancer data exploration: a report from Data for the Common Good

Objective: The Pediatric Cancer Data Commons (PCDC)-a project of Data for the Common Good-houses clinical pediatric oncology data and utilizes the open-source Gen3 platform. To meet the needs of end users, the PCDC development team expanded the out-of-box functionality and developed additional custom features that should be useful to any group developing similar data commons. Materials and methods: Modifications of the PCDC data portal software were implemented to facilitate desired functionality. Results: Newly developed functionality includes updates to authorization methods, expansion of filtering capabilities, and addition of data analysis functions. Discussion: We describe the process by which custom functionalities were developed. Features are open source and available to be implemented and adapted to suit needs of data portals that utilize the Gen3 platform. Conclusion: Data portals are indispensable tools for facilitating data sharing. Open-source infrastructure facilitates a modular and collaborative approach for meeting needs of end users and stakeholders.</p

Single cell profiling at the maternal–fetal interface reveals a deficiency of PD-L1⁺ non-immune cells in human spontaneous preterm labor

The mechanisms that underlie the timing of labor in humans are largely unknown. In most pregnancies, labor is initiated at term (≥ 37 weeks gestation), but in a signifiicant number of women spontaneous labor occurs preterm and is associated with increased perinatal mortality and morbidity. The objective of this study was to characterize the cells at the maternal–fetal interface (MFI) in term and preterm pregnancies in both the laboring and non-laboring state in Black women, who have among the highest preterm birth rates in the U.S. Using mass cytometry to obtain high-dimensional single-cell resolution, we identified 31 cell populations at the MFI, including 25 immune cell types and six non-immune cell types. Among the immune cells, maternal PD1+ CD8 T cell subsets were less abundant in term laboring compared to term non-laboring women. Among the non-immune cells, PD-L1+ maternal (stromal) and fetal (extravillous trophoblast) cells were less abundant in preterm laboring compared to term laboring women. Consistent with these observations, the expression of CD274, the gene encoding PD-L1, was significantly depressed and less responsive to fetal signaling molecules in cultured mesenchymal stromal cells from the decidua of preterm compared to term women. Overall, these results suggest that the PD1/PD-L1 pathway at the MFI may perturb the delicate balance between immune tolerance and rejection and contribute to the onset of spontaneous preterm labor

Sociome Data Commons: A scalable and sustainable platform for investigating the full social context and determinants of health

Abstract Background/Objective: Non-clinical aspects of life, such as social, environmental, behavioral, psychological, and economic factors, what we call the sociome, play significant roles in shaping patient health and health outcomes. This paper introduces the Sociome Data Commons (SDC), a new research platform that enables large-scale data analysis for investigating such factors. Methods: This platform focuses on “hyper-local” data, i.e., at the neighborhood or point level, a geospatial scale of data not adequately considered in existing tools and projects. We enumerate key insights gained regarding data quality standards, data governance, and organizational structure for long-term project sustainability. A pilot use case investigating sociome factors associated with asthma exacerbations in children residing on the South Side of Chicago used machine learning and six SDC datasets. Results: The pilot use case reveals one dominant spatial cluster for asthma exacerbations and important roles of housing conditions and cost, proximity to Superfund pollution sites, urban flooding, violent crime, lack of insurance, and a poverty index. Conclusion: The SDC has been purposefully designed to support and encourage extension of the platform into new data sets as well as the continued development, refinement, and adoption of standards for dataset quality, dataset inclusion, metadata annotation, and data access/governance. The asthma pilot has served as the first driver use case and demonstrates promise for future investigation into the sociome and clinical outcomes. Additional projects will be selected, in part for their ability to exercise and grow the capacity of the SDC to meet its ambitious goals