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Background: Systemic lupus erythematosus (SLE) is a prototypical autoimmune disease in which dysregulation of B cells has been recognized. Here, we searched for potential biomarkers of SLE using liquid chromatography-tandem mass spectrometry (LC-MS)., Methods: Lymph nodes from SLE patients and controls were analyzed by LC-MS. To validate the identified molecules, immunoblotting and immunohistochemistry were performed and B cells from SLE patients were analyzed by quantitative RT-PCR. B-cell subsets from NZB/W F1 mice, which exhibit autoimmune disease resembling human SLE, were analyzed by flow cytometry. Endoplasmic reticulum (ER) stress was induced by tunicamycin and the serum concentration of anti-dsDNA antibodies was determined by ELISA. TUNEL methods and immunoblotting were used to assess the effect of tunicamycin., Results: MZB1, which comprises part of a B-cell-specific ER chaperone complex and is a key player in antibody secretion, was one of the differentially expressed proteins identified by LC-MS and confirmed by immunoblotting. Immunohistochemically, larger numbers of MZB1 + cells were located mainly in interfollicular areas and scattered in germinal centers in specimens from SLE patients compared with those from controls. MZB1 colocalized with CD138 + plasma cells and IRTA1 + marginal zone B cells. MZB1 mRNA was increased by 2.1-fold in B cells of SLE patients with active disease (SLE Disease Activity Index 2000 ≥ 6) compared with controls. In aged NZB/W F1 mice, splenic marginal zone B cells and plasma cells showed elevated MZB1 levels. Tunicamycin induced apoptosis of MZB1 + cells in target organs, resulting in decreased serum anti-dsDNA antibody levels. Additionally, MZB1 + cells were increased in synovial tissue specimens from patients with rheumatoid arthritis., Conclusions: MZB1 may be a potential therapeutic target in excessive antibody-secreting cells in SLE.

Background: Mobile technology to support community health has surged in popularity, yet few studies have systematically examined usability of mobile platforms for this setting., Methods: We conducted a mixed-methods study of 14 community healthcare workers at a public healthcare clinic in São Paulo, Brazil. We held focus groups with community healthcare workers to elicit their ideas about a mobile health application and used this input to build a prototype app. A pre-use test survey was administered to all participants, who subsequently use-tested the app on three different devices (iPhone, iPad mini, iPad Air). Usability was assessed by objectively scored data entry errors and through a post-use focus group held to gather open-ended feedback on end-user satisfaction., Results: All of the participants were women, ranging from 18-64 years old. A large percentage (85.7%) of participants had at least a high school education. Internet (92.8%), computer (85.7%) and cell phone (71.4%) use rates were high. Data entry error rates were also high, particularly in free text fields, ranging from 92.3 to 100%. Error rates were comparable across device type. In a post-use focus group, participants reported that they found the app easy to use and felt that its design was consistent with their vision. The participants raised several concerns, including that they did not find filling out the forms in the app to be a useful task. They also were concerned about an app potentially creating more work for them and personal security issues related to carrying a mobile device in low-income areas., Conclusion: In a cohort of formally educated community healthcare workers with high levels of personal computer and cell phone use, we identified no technological barriers to adapting their existing work to a mobile device based system. Transferring current data entry work into a mobile platform, however, uncovered underlying dissatisfaction with some data entry tasks. This dissatisfaction may be a more significant barrier than the data entry errors our testing revealed. Our results highlight the fact that without a deep understanding of local process to optimize usability, technology-based solutions in health may fail. Developing such an understanding must be a central component in the design of any mHealth solution in global health.