NF-κB signaling through its -dependent canonical and-dependent noncanonical pathways plays distinctive roles in a diverse range of immune processes. Recently, mutations in these 2 genes have been associated with common variable immunodeficiency (CVID). While studying patients with genetically uncharacterized primary immunodeficiencies, we detected 2 novel nonsense gain-of-function (GOF) mutations (E418X and R635X) in 3 patients from 2 families, and a novel missense change (S866R) in another patient. Their immunophenotype was assessed by flow cytometry and protein expression; activation of canonical and noncanonical pathways was examined in peripheral blood mononuclear cells and transfected HEK293T cells through immunoblotting, immunohistochemistry, luciferase activity, real-time polymerase chain reaction, and multiplex assays. The S866R change disrupted a C-terminal NF-κΒ2 critical site affecting protein phosphorylation and nuclear translocation, resulting in CVID with adrenocorticotropic hormone deficiency, growth hormone deficiency, and mild ectodermal dysplasia as previously described. In contrast, the nonsense mutations E418X and R635X observed in 3 patients led to constitutive nuclear localization and activation of both canonical and noncanonical NF-κΒ pathways, resulting in a combined immunodeficiency (CID) without endocrine or ectodermal manifestations. These changes were also found in 2 asymptomatic relatives. Thus, these novel GOF mutations produce a nonfully penetrant CID phenotype through a different pathophysiologic mechanism than previously described for mutations in .

The intracellular chaperone heat-shock protein 70 (Hsp70) can be secreted from cells, but its extracellular role is unclear, as the protein has been reported to both activate and suppress the innate immune response. Potential immunomodulatory receptors on myelomonocytic lineage cells that bind extracellular Hsp70 are not well defined. Siglecs are Ig-superfamily lectins on mammalian leukocytes that recognize sialic acid-bearing glycans and thereby modulate immune responses. Siglec-5 and Siglec-14, expressed on monocytes and neutrophils, share identical ligand-binding domains but have opposing signaling functions. Based on phylogenetic analyses of these receptors, we predicted that endogenous sialic acid-independent ligands should exist. An unbiased screen revealed Hsp70 as a ligand for Siglec-5 and Siglec-14. Hsp70 stimulation through Siglec-5 delivers an anti-inflammatory signal, while stimulation through Siglec-14 is pro-inflammatory. The functional consequences of this interaction are also addressed in relation to a SIGLEC14 polymorphism found in humans. Our results demonstrate that an endogenous non-sialic acid-bearing molecule can be either a danger-associated or self-associated signal through paired Siglecs, and may explain seemingly contradictory prior reports on extracellular Hsp70 action.

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Background: Deep learning (DL) models typically interpret images without prior knowledge of anatomic significance. However, pathophysiology is highly classified by human definitions according to underlying affected anatomy. Therefore, we examine the impact of introducing explicit knowledge of anatomy through cardiac contours on cardiac magnetic resonance images (CMR) to DL models. The DL models were then trained to differentiate between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) using late gadolinium enhanced (LGE) CMR. Method: We evaluated 301 CMR studies; ICM (n=176) and NICM (n=125). Manual contouring of LGE images was performed using CVI42 (Circle, Ontario). We compared a radiomic and end-to-end DL approach to identify cardiomyopathy (CM) etiology from short axis LGE images. Patients are randomly assigned to training (70%) and testing (30%). Model performance was assessed with area under curve (AUC). Result: Table 1 shows the results of radiomic and deep learning approaches to differentiate NICM and ICM. Segmented/manually contoured images with removal of non-cardiac structures greatly improved classification accuracy across all deep learning models. The average improvement in AUC was 0.163 when using segmented images compared to the full images. Furthermore, the deep learning models outperformed the radiomics models. The best radiomic model and deep learning model achieved AUCs of 0.914 and 0.947, respectively. Both radiomic based models achieved AUCs above 0.874 while all 2D deep learning models with segmented images achieved AUCs above 0.875. The two 3D deep learning models which utilize 3D convolutions provided lower AUCs ranging between 0.743 and 0.900. Conclusion: Manual segmentation of LGE images improved the ability to train DL models with fairly small volumes of labeled data, resulting in higher classification accuracy. Table1 AUCs of various models.