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ObjectivesHypocomplementaemia is common in patients with IgG4-related disease (IgG4-RD). We aimed to determine the IgG4-RD features associated with hypocomplementaemia and investigate mechanisms of complement activation in this disease.MethodsWe performed a single-centre cross-sectional study of 279 patients who fulfilled the IgG4-RD classification criteria, using unadjusted and multivariable-adjusted logistic regression to identify factors associated with hypocomplementaemia.ResultsHypocomplementaemia was observed in 90 (32%) patients. In the unadjusted model, the number of organs involved (OR 1.42, 95% CI 1.23 to 1.63) and involvement of the lymph nodes (OR 3.87, 95% CI 2.19 to 6.86), lungs (OR 3.81, 95% CI 2.10 to 6.89), pancreas (OR 1.66, 95% CI 1.001 to 2.76), liver (OR 2.73, 95% CI 1.17 to 6.36) and kidneys (OR 2.48, 95% CI 1.47 to 4.18) were each associated with hypocomplementaemia. After adjusting for age, sex and number of organs involved, only lymph node (OR 2.59, 95% CI 1.36 to 4.91) and lung (OR 2.56, 95% CI 1.35 to 4.89) involvement remained associated with hypocomplementaemia while the association with renal involvement was attenuated (OR 1.6, 95% CI 0.92 to 2.98). Fibrotic disease manifestations (OR 0.43, 95% CI 0.21 to 0.87) and lacrimal gland involvement (OR 0.53, 95% CI 0.28 to 0.999) were inversely associated with hypocomplementaemia in the adjusted analysis. Hypocomplementaemia was associated with higher concentrations of all IgG subclasses and IgE (all p<0.05). After adjusting for serum IgG1 and IgG3, only IgG1 but not IgG4 remained strongly associated with hypocomplementaemia.ConclusionsHypocomplementaemia in IgG4-RD is not unique to patients with renal involvement and may reflect the extent of disease. IgG1 independently correlates with hypocomplementaemia in IgG4-RD, but IgG4 does not. Complement activation is likely involved in IgG4-RD pathophysiology.

Abstract IgG4-related disease (IgG4-RD) is an increasingly recognized cause of fibroinflammatory lesions in patients of diverse racial and ethnic backgrounds and is associated with an increased risk of death. The aetiology of IgG4-RD is incompletely understood, but evidence to date suggests that B and T cells are important players in pathogenesis, both of which are key targets of ongoing drug development programmes. The diagnosis of IgG4-RD requires clinicopathological correlation because there is no highly specific or sensitive test. Glucocorticoids are highly effective, but their use is limited by toxicity, highlighting the need for studies investigating the efficacy of glucocorticoid-sparing agents. B cell-targeted therapies, particularly rituximab, have demonstrated benefit, but no randomized clinical trials have evaluated their efficacy. If untreated or under-treated, IgG4-RD can cause irreversible organ damage, hence close monitoring and consideration for long-term immunosuppression is warranted in certain cases. Lay Summary IgG4-related disease (IgG4-RD) can affect nearly any organ and cause masses or inflammatory lesions. Common sites of disease include the salivary glands, lacrimal glands, orbit, pancreas, biliary tract, lungs and retroperitoneum. IgG4-RD has been increasingly recognized over the last 20 years and described in patients of diverse racial and ethnic backgrounds. It is associated with an increased risk of death. The cause of IgG4-RD is unknown, but several aspects of the immune system, including B cells and T cells, are thought to contribute to the disease and might be important targets for new treatments. To diagnose IgG4-RD, one must consider the history, physical examination, laboratory results, imaging and pathology findings. Elevated IgG4 levels in the blood and significant numbers of IgG4+ plasma cells in the tissue are not specific to the diagnosis of IgG4-RD but can support a diagnosis in the right clinical setting. IgG4-RD can lead to organ damage if it is not treated, but there are effective treatments, including steroids. Given that steroids can cause significant side effects, a number of trials are evaluating the potential role of steroid-sparing drugs. The optimal approach to treatment is still being defined, and ongoing clinical trials will help to address this knowledge gap.

In-vivo single cell clonal analysis in the adult mouse kidney has previously shown lineage-restricted clonal proliferation within varying nephron segments as a mechanism responsible for cell replacement and local regeneration. To analyze ex-vivo clonal growth, we now preformed limiting dilution to generate genuine clonal cultures from one single human renal epithelial cell, which can give rise to up to 3.4 * 10 6 cells, and analyzed their characteristics using transcriptomics. A comparison between clonal cultures revealed restriction to either proximal or distal kidney sub-lineages with distinct cellular and molecular characteristics; rapidly amplifying de-differentiated clones and a stably proliferating cuboidal epithelial-appearing clones, respectively. Furthermore, each showed distinct molecular features including cell-cycle, epithelial-mesenchymal transition, oxidative phosphorylation, BMP signaling pathway and cell surface markers. In addition, analysis of clonal versus bulk cultures show early clones to be more quiescent, with elevated expression of renal developmental genes and overall reduction in renal identity markers, but with an overlapping expression of nephron segment identifiers and multiple identity. Thus, ex-vivo clonal growth mimics the in-vivo situation displaying lineage-restricted precursor characteristics of mature renal cells. These data suggest that for reconstruction of varying renal lineages with human adult kidney based organoid technology and kidney regeneration ex-vivo, use of multiple heterogeneous precursors is warranted.

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.

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.

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

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.

As neural networks are increasingly being integrated into mission-critical systems, it is becoming crucial to ensure that they meet various safety and liveness requirements. Toward, that end, numerous complete and sound verification techniques have been proposed in recent years, but these often suffer from severe scalability issues. One recently proposed approach for improving the scalability of verification techniques is to enhance them with abstraction/refinement capabilities: instead of verifying a complex and large network, abstraction allows the verifier to construct and then verify a much smaller network, and the correctness of the smaller network immediately implies the correctness of the original, larger network. One shortcoming of this scheme is that whenever the smaller network cannot be verified, the verifier must perform a refinement step, in which the size of the network being verified is increased. The verifier then starts verifying the new network from scratch—effectively “forgetting” its earlier work, in which the smaller network was verified. Here, we present an enhancement to abstraction-based neural network verification, which uses residual reasoning : a process where information acquired when verifying an abstract network is utilized in order to facilitate the verification of refined networks. At its core, the method enables the verifier to retain information about parts of the search space in which it was determined that the refined network behaves correctly, allowing the verifier to focus on areas of the search space where bugs might yet be discovered. For evaluation, we implemented our approach as an extension to the Marabou verifier and obtained highly promising results.

Deep neural networks (DNNs) play a crucial role in the field of machine learning, demonstrating state-of-the-art performance across various application domains. However, despite their success, DNN-based models may occasionally exhibit challenges with generalization, i.e., may fail to handle inputs that were not encountered during training. This limitation is a significant challenge when it comes to deploying deep learning for safety-critical tasks, as well as in real-world settings characterized by substantial variability. We introduce a novel approach for harnessing DNN verification technology to identify DNN-driven decision rules that exhibit robust generalization to previously unencountered input domains. Our method assesses generalization within an input domain by measuring the level of agreement between independently trained deep neural networks for inputs in this domain. We also efficiently realize our approach by using off-the-shelf DNN verification engines, and extensively evaluate it on both supervised and unsupervised DNN benchmarks, including a deep reinforcement learning (DRL) system for Internet congestion control—demonstrating the applicability of our approach for real-world settings. Moreover, our research introduces a fresh objective for formal verification, offering the prospect of mitigating the challenges linked to deploying DNN-driven systems in real-world scenarios.

Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist to find failures in networks (e.g., adversarial examples), but these cannot guarantee the absence of failures. Verification algorithms address this need and provide formal guarantees about a neural network by answering “yes or no” questions. For example, they can answer whether a violation exists within certain bounds. However, individual “yes or no\" questions cannot answer qualitative questions such as “what is the largest error within these bounds”; the answers to these lie in the domain of optimization. Therefore, we propose strategies to extend existing verifiers to perform optimization and find: (i) the most extreme failure in a given input region and (ii) the minimum input perturbation required to cause a failure. A naive approach using a bisection search with an off-the-shelf verifier results in many expensive and overlapping calls to the verifier. Instead, we propose an approach that tightly integrates the optimization process into the verification procedure, achieving better runtime performance than the naive approach. We evaluate our approach implemented as an extension of Marabou, a state-of-the-art neural network verifier, and compare its performance with the bisection approach and MIPVerify, an optimization-based verifier. We observe complementary performance between our extension of Marabou and MIPVerify.