Search

Objective RheumMadness is an online learning collaborative that seeks to actively engage the rheumatology community. The objective of this manuscript is to analyze the educational experience of RheumMadness over two years. Methods Direct measures of participant engagement were obtained using web‐based analytics. An electronic survey was created after the tournament to capture self‐reported engagement and educational experience using the Community of Inquiry framework. Data were analyzed according to the following objectives: (1) compare demographics, engagement, and educational experience of participants between 2021 and 2022; (2) describe the educational experience of those who created scouting reports; (3) explore the impact of RheumMadness on early learners (medical students and residents). Results Compared with 2021, the 2022 tournament had more participants who submitted a bracket, more early learners, and more scouting report creators. Self‐reported engagement and educational experience was high in both years of the tournament among all participants. Over 85% of scouting report creators reported that making a report was a fun and valuable learning experience. Early learners reported significantly higher levels of knowledge integration, sense of belonging in the rheumatology community, social connection, and overall learning experience compared with more advanced participants. Eighty‐five percent of early learners reported that RheumMadness increased their interest in rheumatology. Conclusion RheumMadness expanded from 2021 to 2022, engaging more participants in collaborative learning. Our results demonstrate that RheumMadness is particularly impactful among medical students and residents by helping them explore rheumatology topics and connect with the rheumatology community.

Objective Feedback from fellows‐in‐training (FITs) is important for faculty development and to enrich clinical teaching. We sought to evaluate the effectiveness of traditional online evaluations and a novel compiled verbal feedback mechanism. Methods An annual feedback system was implemented in our rheumatology division in which FITs provided verbal feedback on all faculty to a facilitator who compiled, deidentified, and shared the feedback with individual faculty members. FITs also completed standard online annual evaluations of faculty. FITs and faculty completed surveys assessing the perceived effectiveness and confidentiality of each feedback mechanism. Results Thirteen of 15 eligible faculty and all 4 eligible FITs completed both surveys. Responses by FITs and faculty regarding the quality of online evaluations were generally unfavorable or neutral. Faculty responses regarding compiled verbal feedback were more favorable in all questions and significantly more favorable with respect to the feedback's ability to explain strengths (54% favorable for online evaluations vs 100% for compiled verbal feedback), the feedback's specificity (0% vs 54%), and the feedback's actionable nature (15% vs 62%). All FITs’ responses regarding quality of compiled verbal feedback were favorable. FITs had concerns regarding confidentiality with both online evaluations (0% favorable) and compiled verbal feedback (25% favorable), though FITs had less concern for future faculty interactions with compiled verbal feedback (100% favorable) than with online evaluations (0% favorable). Conclusion Compiled verbal feedback by FITs produced more actionable and effective feedback for faculty, with less concerns regarding future faculty interactions compared with traditional online evaluations. Further study of this method across different programs and institutions is warranted.

Autonomous vehicles are highly complex systems, required to function reliably in a wide variety of situations. Manually crafting software controllers for these vehicles is difficult, but there has been some success in using deep neural networks generated using machine-learning. However, deep neural networks are opaque to human engineers, rendering their correctness very difficult to prove manually; and existing automated techniques, which were not designed to operate on neural networks, fail to scale to large systems. This paper focuses on proving the adversarial robustness of deep neural networks, i.e. proving that small perturbations to a correctly-classified input to the network cannot cause it to be misclassified. We describe some of our recent and ongoing work on verifying the adversarial robustness of networks, and discuss some of the open questions we have encountered and how they might be addressed.

During nephrogenesis, stem/progenitor cells differentiate and give rise to early nephron structures that segment to proximal and distal nephron cell types. Previously, we prospectively isolated progenitors from human fetal kidney (hFK) utilizing a combination of surface markers. However, upon culture nephron progenitors differentiated and could not be robustly maintained in vitro. Here, by culturing hFK in a modified medium used for in vitro growth of mouse nephron progenitors, and by dissection of NCAM+/CD133− progenitor cells according to EpCAM expression (NCAM+/CD133−/EpCAM−, NCAM+/CD133−/EpCAMdim, NCAM+/CD133−/EpCAMbright), we show at single-cell resolution a preservation of uninduced and induced cap mesenchyme as well as a transitioning mesenchymal-epithelial state. Concomitantly, differentiating and differentiated epithelial lineages are also maintained. In vitro expansion of discrete stages of early human nephrogenesis in nephron stem cell cultures may be used for drug screening on a full repertoire of developing kidney cells and for prospective isolation of mesenchymal or epithelial renal lineages for regenerative medicine.

Patients with systemic lupus erythematosus (SLE) presenting with chest pain pose a unique diagnostic challenge, with causes ranging from cardiopulmonary disease to esophageal disorders and musculoskeletal chest wall pain. The most common biomarkers for myocardial injury are cardiac troponin T and I (cTnT and cTnI) due to their high sensitivity for the early detection of myocardial infarction. In the idiopathic inflammatory myopathies, cTnT is commonly elevated, and this reflects skeletal muscle breakdown rather than myocardial damage. Similar observations have not been reported in SLE myositis to date. We present two cases of patients with SLE and associated myositis who presented with chest pain and elevated cTnT. Both patients had a normal cTnI, transthoracic echocardiogram, and cardiac magnetic resonance imaging, likely indicating noncardiac chest pain. Clinicians should be aware that the specificity of cTnT might be lower in SLE myositis and that cTnI elevation may be more specific in detecting myocardial insult.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

This extended abstract reports on current progress of SMTCoq, a communication tool between the Coq proof assistant and external SAT and SMT solvers. Based on a checker for generic first-order certificates implemented and proved correct in Coq, SMTCoq offers facilities both to check external SAT and SMT answers and to improve Coq's automation using such solvers, in a safe way. Currently supporting the SAT solver zChaff, and the SMT solver veriT for the combination of the theories of congruence closure and linear integer arithmetic, SMTCoq is meant to be extendable with a reasonable amount of effort: we present work in progress to support the SMT solver CVC4 and the theory of bit vectors.