Spatial Statistics of Visual Keypoints for Texture Recognition

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Ancient Origin of the New Developmental Superfamily DANGER

Developmental proteins play a pivotal role in the origin of animal complexity and diversity. We report here the identification of a highly divergent developmental protein superfamily (DANGER), which originated before the emergence of animals (∼850 million years ago) and experienced major expansion-contraction events during metazoan evolution. Sequence analysis demonstrates that DANGER proteins diverged via multiple mechanisms, including amino acid substitution, intron gain and/or loss, and recombination. Divergence for DANGER proteins is substantially greater than for the prototypic member of the superfamily (Mab-21 family) and other developmental protein families (e.g., WNT proteins). DANGER proteins are widely expressed and display species-dependent tissue expression patterns, with many members having roles in development. DANGER1A, which regulates the inositol trisphosphate receptor, promotes the differentiation and outgrowth of neuronal processes. Regulation of development may be a universal function of DANGER family members. This family provides a model system to investigate how rapid protein divergence contributes to morphological complexity

Expression of the Inherently Autoreactive Idiotope 9G4 on Autoantibodies to Citrullinated Peptides and on Rheumatoid Factors in Patients with Early and Established Rheumatoid Arthritis.

The pre-symptomatic stage of Rheumatoid arthritis (RA) is associated with pro-inflammatory cytokines and autoantibodies. High levels and epitope spread by Rheumatoid factors (RhF) and autoantibodies to citrullinated proteins signify progression towards disease expression. In established RA, the persistence of high autoantibody levels reflects production by both long-lived plasma cells and short-lived plasmablasts. Neither the relative contributions to pathogenesis by autoantibodies from either source, nor the factors responsible for deciding the fate of autoantigen specific 'parent' B-cells, is understood. Phenotypic markers identifying subsets of autoreactive B-cells are therefore of interest in understanding the origin and perpetuation of the autoimmune response in RA. One such phenotypic marker is the rat monoclonal antibody, 9G4, which recognises an idiotope on immunoglobuins derived from the inherently autoreactive VH-gene, VH4-34. We therefore investigated whether the 9G4 idiotope was expressed on autoantibodies in patients with RA

Cold Agglutinin Disease: Improved Understanding of Pathogenesis Helps Define Targets for Therapy

The last 2 decades have seen great progress in understanding the pathogenesis of cold agglutinin disease (CAD) and development of effective therapies. Cold agglutinins can cause hemolytic anemia as well as peripheral circulatory symptoms such as acrocyanosis. We distinguish CAD, a well-defined clinicopathologic entity, from secondary cold agglutinin syndrome. This review addresses the histopathologic, immune phenotypic, and molecular features that allow CAD to be classified as a distinct clonal lymphoproliferative disorder of the bone marrow, recently recognized in the WHO classification. We discuss recent data on the possible overlap or distinction between CAD and Waldenström’s macroglobulinemia. Two major steps in the pathogenesis of CAD are identified: clonal B-cell lymphoproliferation (leading to monoclonal IgM production) and complement-mediated hemolysis. Each of these steps constitutes a target for treatment. Established as well as novel and experimental therapies are reviewed

Cold Agglutinin Disease: Improved Understanding of Pathogenesis Helps Define Targets for Therapy

The last 2 decades have seen great progress in understanding the pathogenesis of cold agglutinin disease (CAD) and development of effective therapies. Cold agglutinins can cause hemolytic anemia as well as peripheral circulatory symptoms such as acrocyanosis. We distinguish CAD, a well-defined clinicopathologic entity, from secondary cold agglutinin syndrome. This review addresses the histopathologic, immune phenotypic, and molecular features that allow CAD to be classified as a distinct clonal lymphoproliferative disorder of the bone marrow, recently recognized in the WHO classification. We discuss recent data on the possible overlap or distinction between CAD and Waldenström’s macroglobulinemia. Two major steps in the pathogenesis of CAD are identified: clonal B-cell lymphoproliferation (leading to monoclonal IgM production) and complement-mediated hemolysis. Each of these steps constitutes a target for treatment. Established as well as novel and experimental therapies are reviewed

Deep learning neural network -guided detection of asbestos bodies in bronchoalveolar lavage samples

Introduction: Asbestos is a global occupational health hazard and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich proteins and mucopolysaccharides, after which they are called asbestos bodies and can be detected in light microscopy. Bronchoalveolar lavage, a cytological sample from the lower airways, is one of the methods for diagnosing lung asbestosis and related morbidity. Search for asbestos bodies in these samples is generally laborious and time-consuming. We describe a novel diagnostic method, which implements deep-learning neural network technology for the detection of asbestos bodies in bronchoalveolar lavage samples.Methods: Bronchoalveolar lavage samples with suspicion of asbestos exposure were scanned as whole slide images and uploaded to a cloud-based virtual microscopy platform with a neural network training interface. The images were used for training and testing a neural network model capable of recognizing asbestos bodies. To prioritize the model's sensitivity, we allowed it to also make false-positive suggestions. To test the model, we compared its performance to standard light microscopy diagnostic data as well as the ground truth number of asbestos bodies, which we established by a thorough manual search of the whole slide images.Results: We were able to reach overall sensitivity of 93.4 % (95% CI 90.3 - 95.7 %) in the detection of asbestos bodies in comparison to their ground truth number. Compared to standard light microscopy diagnostic data, our model showed equal to or higher sensitivity in most cases.Conclusion: Our results indicate that deep learning neural network technology offers promising diagnostic tools for routine assessment of bronchoalveolar lavage samples. However, at this stage, a human expert is required to confirm the findings.Peer reviewe