Development of a Novel Renal Activity Index of Lupus Nephritis in Children and Young Adults

OBJECTIVE: Noninvasive estimation of the degree of inflammation seen on kidney biopsy with lupus nephritis (LN) remains difficult. The objective of this study was to develop a Renal Activity Index for Lupus (RAIL) that, based solely on laboratory measures, accurately reflects histologic LN activity. METHODS: We assayed traditional LN laboratory tests and 16 urine biomarkers (UBMs) in children (n = 47) at the time of kidney biopsy. Histologic LN activity was measured by the National Institutes of Health activity index (NIH-AI) and the tubulointerstitial activity index (TIAI). High LN-activity status (versus moderate/low) was defined as NIH-AI scores >10 (versus ≤10) or TIAI scores >5 (versus ≤5). RAIL algorithms that predicted LN-activity status for both NIH-AI and TIAI were derived by stepwise multivariate logistic regression, considering traditional biomarkers and UBMs as candidate components. The accuracy of the RAIL for discriminating by LN-activity status was determined. RESULTS: The differential excretion of 6 UBMs (neutrophil gelatinase-associated lipocalin, monocyte chemotactic protein 1, ceruloplasmin, adiponectin, hemopexin, and kidney injury molecule 1) standardized by urine creatinine was considered in the RAIL. These UBMs predicted LN-activity (NIH-AI) status with >92% accuracy and LN-activity (TIAI) status with >80% accuracy. RAIL accuracy was minimally influenced by concomitant LN damage. Accuracies between 71% and 85% were achieved without standardization of the UBMs. The strength of these UBMs to reflect LN-activity status was confirmed by principal component and linear discriminant analyses. CONCLUSION: The RAIL is a robust and highly accurate noninvasive measure of LN activity. The measurement properties of the RAIL, which reflect the degree of inflammatory changes as seen on kidney biopsy, will require independent validation

Bottom-Up Organizing with Tools from On High: Understanding the Data Practices of Labor Organizers

This paper provides insight into the use of data tools in the American labor movement by analyzing the practices of staff employed by unions to organize alongside union members. We interviewed 23 field-level staff organizers about how they use data tools to evaluate membership. We find that organizers work around and outside of these tools to develop access to data for union members and calibrate data representations to meet local needs. Organizers mediate between local and central versions of the data, and draw on their contextual knowledge to challenge campaign strategy. We argue that networked data tools can compound field organizers' lack of discretion, making it more difficult for unions to assess and act on the will of union membership. We show how the use of networked data tools can lead to less accurate data, and discuss how bottom-up approaches to data gathering can support more accurate membership assessments

Searching and Thinking About Searching JSTOR

Digital resources are helping to change the ways scholars and students work, but they must also be helping to shape the work that gets done. Taking JSTOR as an example, we might ask about the discursive power of the database. How is using an online resource for research acceding to unnoticed assumptions that underlie the construction of that resource?</jats:p