We show that Scurfy mice show a predominant AC-5 ANA pattern typical for mixed connective tissue disease as in scleroderma. The autoimmune inflammation in scurfy skin mainly consists of CD4 T cells with Th2 differentiation and alternatively-activated (M2) macrophages as it is found in scleroderma with advanced fibrosis.
Due to a missense mutation in the Foxp3 gene, scurfy mice are deficient in functional regulatory T cells (Treg). The consequent loss of peripheral tolerance manifests itself by fatal autoimmune mediated multi-organ disease. Previous studies have outlined the systemic inflammatory disease and demonstrated production of anti-nuclear antibodies (ANA) in scurfy mice. However, specific autoantibody targets remained to be defined. ANA are immunological markers for several connective tissue diseases (CTD) and target a large number of intracellular molecules. Therefore, we examined scurfy sera for the presence of different ANA specificities and further assessed the organ involvement in these animals. Indirect immunofluorescence was used as a screen for ANA in the sera of scurfy mice and dilutions of 1/100 were considered positive. Addressable laser bead immunoassays (ALBIA) were used to detect specific autoantibody targets. Subsequent histological tissue evaluation was verified by hematoxylin and eosin (H&E) staining. In our study, we observed that nearly all scurfy mice produced ANA. The most prevalent pattern in scurfy sera was nuclear coarse speckled, also known as the AC-5 pattern according to the International Consensus on ANA Patterns. U1-ribonucleoprotein (U1RNP) was found to be the most common target antigen recognized by autoantibodies in scurfy mice. Additionally, scurfy mice exhibited a mild myositis with histological characteristics similar to polymyositis/dermatomyositis. Myopathy-specific autoantibody profile revealed significantly increased levels of anti-SMN (survival of motor neuron) as well as anti-Gemin3 antibodies in scurfy sera. Overall, we demonstrate that the impaired peripheral tolerance in the absence of regulatory T cells in scurfy mice is associated with features of mixed connective tissue disease (MCTD). This includes, along with our previous findings, very high titers of anti-U1RNP antibodies and an inflammatory myopathy.
IMPORTANCE Ecthyma contagiosum, or orf, is a viral zoonotic infection caused by Poxviridae. Although human orf infection is considered to follow a self-limited course, various immunological reactions may be triggered, including immunobullous diseases. In the majority of the latter cases, the antigenic target remained enigmatic.OBJECTIVE To characterize the predominant autoantigen in orf-induced immunobullous disease and further describe this clinical entity. DESIGN, SETTING, AND PARTICIPANTSThis multicenter case series sought to provide detailed clinical, histopathological and immunological characteristics of a patient with orf-induced pemphigoid. Based on this index patient, serological analyses were conducted of 4 additional patients with previously reported orf-induced immunobullous disease. Immunoblotting with extracellular matrix and a recently established indirect immunofluorescence assay for detection of serum anti-laminin 332 IgG were performed.EXPOSURES The disease course and clinical characteristics of orf-induced immunobullous disease were observed. MAIN OUTCOMES AND MEASURESOrf-induced immunobullous disease is primarily characterized by anti-laminin 332 autoantibodies, predominant skin involvement, and a self-limiting course. The study provides further details on epidemiological, clinical, immunopathological, diagnostic, and therapeutic aspects of orf-induced immunobullous disease. RESULTSIn all 5 patients, IgG1 and/or IgG3 autoantibodies against laminin 332 were identified. The α3, β3, and γ2 chains were recognized in 2, 4, and 1 patient(s), respectively. CONCLUSIONS AND RELEVANCEIn this case series, laminin 332, a well-known target antigen in mucous membrane pemphigoid, was a major autoantigen in orf-induced immunobullous disease, even though predominant mucosal lesions were lacking in this autoimmune blistering disease. Orf-induced anti-laminin 332 pemphigoid is proposed as distinct clinical entity.
In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy, by learning an adaptive node searching order. In contrast, our imitation learning policy is focused solely on learning which of a node’s children to select. We present an offline method to learn such a policy in two settings: one that comprises a heuristic by committing to pruning of nodes; one that is exact and backtracks from a leaf to guarantee finding the optimal integer solution. The former setting corresponds to a child selector during plunging, while the latter is akin to a diving heuristic. We apply the policy within the popular open-source solver SCIP, in both heuristic and exact settings. Empirical results on five MIP datasets indicate that our node selection policy leads to solutions significantly more quickly than the state-of-the-art precedent in the literature. While we do not beat the highly-optimised SCIP state-of-practice baseline node selector in terms of solving time on exact solutions, our heuristic policies have a consistently better optimality gap than all baselines, if the accuracy of the predictive model is sufficient. Further, the results also indicate that, when a time limit is applied, our heuristic method finds better solutions than all baselines in the majority of problems tested. We explain the results by showing that the learned policies have imitated the SCIP baseline, but without the latter’s early plunge abort. Our recommendation is that, despite the clear improvements over the literature, this kind of MIP child selector is better seen in a broader approach to using learning in MIP branch-and-bound tree decisions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.