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Journal articleMcAdoo S, Farrah TE, Prendecki M, et al., 2021,
Journal articleFarrah TE, Prendecki M, Hunter RW, et al., 2021,
Journal articlePokidysheva EN, Seeger H, Pedchenko V, et al., 2021,
Collagen IVα345 dysfunction in glomerular basement membrane diseases. I. Discovery of a COL4A3 variant in familial Goodpasture's and Alport diseases, Journal of Biological Chemistry, Vol: 296, ISSN: 0021-9258
Diseases of the glomerular basement membrane (GBM), such as Goodpasture's disease (GP) and Alport syndrome (AS), are a major cause of chronic kidney failure and an unmet medical need. Collagen IVα345 is an important architectural element of the GBM that was discovered in previous research on GP and AS. How this collagen enables GBM to function as a permselective filter and how structural defects cause renal failure remain an enigma. We found a distinctive genetic variant of collagen IVα345 in both a familial GP case and four AS kindreds that provided insights into these mechanisms. The variant is an 8-residue appendage at the C-terminus of the α3 subunit of the α345 hexamer. A knock-in mouse harboring the variant displayed GBM abnormalities and proteinuria. This pathology phenocopied AS, which pinpointed the α345 hexamer as a focal point in GBM function and dysfunction. Crystallography and assembly studies revealed underlying hexamer mechanisms, as described in Companion Papers II and III. Bioactive sites on the hexamer surface were identified where pathogenic pathways of GP and AS converge, and, potentially, that of diabetic nephropathy (DN). We conclude that the hexamer functions include signaling and organizing macromolecular complexes, which enable GBM assembly and function. Therapeutic modulation or replacement of α345 hexamer could therefore be a potential treatment for GBM diseases, and this knock-in mouse model is suitable for developing gene therapies.
Journal articleMcAdoo SP, Dhaun N, 2021,
Journal articleAhn SS, Yoon T, Park Y-B, et al., 2021,
Serum chitinase-3-like 1 protein is a useful biomarker to assess disease activity in ANCA-associated vasculitis: an observational study, Arthritis Research and Therapy, Vol: 23, Pages: 1-12, ISSN: 1478-6354
BackgroundTo investigate whether serum chitinase-3-like 1 protein (YKL-40) is associated with disease activity in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV).MethodsELISA was performed in serum samples from AAV patients who were enrolled in our prospective observational cohort to estimate levels of YKL-40. Birmingham vasculitis activity score (BVAS) (version 3), five factor score (FFS), and short form-36 (SF-36), as well as clinical and laboratory data were collected. Kidney expression of YKL-40 was assessed by immunohistochemical staining using renal biopsy tissues from ANCA-associated glomerulonephritis patients (AAGN). Severe AAV and FFS were defined as BVAS ≥ 12 and FFS ≥ 2, and the correlations between laboratory variables, BVAS, FFS, and SF-36 score were assessed using linear regression analysis. The optimal cut-off of serum YKL-40 for severe AAV and high FFS was calculated using the receiver operator characteristic curve analysis.ResultsOf the included 60 patients, 32 (53.3%), 17 (28.3%), and 11 (18.3%) were classified as microscopic polyangiitis, granulomatosis with polyangiitis, and eosinophilic granulomatosis with polyangiitis. The median BVAS and FFS were 7.0 and 1.0, whereas the mean SF-36 physical and mental component scores were 50.5 and 58.3. Serum YKL-40 level was higher in patients with severe AAV and high FFS compared to those without (p = 0.007 and p < 0.001); multivariable linear regression analysis revealed that serum YKL-40 was independently associated with BVAS, FFS, and SF-36 scores. On kidney tissues obtained from AAGN patients, strong cytoplasmic staining of YKL-40 was found in cells present in inflammatory lesions. In addition, AAV patients had higher levels of serum YKL-40 compared to those with systemic lupus erythematosus, rheumatoid arthritis, osteoarthritis, and healthy control. The proportion of patients having severe AAV and high FFS was significantly hig
Journal articleWang C, Yang G, Papanastasiou G, et al., 2021,
Cycle-consistent generative adversarial network (CycleGAN) has been widely used for cross-domain medical image synthesis tasks particularly due to its ability to deal with unpaired data. However, most CycleGAN-based synthesis methods cannot achieve good alignment between the synthesized images and data from the source domain, even with additional image alignment losses. This is because the CycleGAN generator network can encode the relative deformations and noises associated to different domains. This can be detrimental for the downstream applications that rely on the synthesized images, such as generating pseudo-CT for PET-MR attenuation correction. In this paper, we present a deformation invariant cycle-consistency model that can filter out these domain-specific deformation. The deformation is globally parameterized by thin-plate-spline (TPS), and locally learned by modified deformable convolutional layers. Robustness to domain-specific deformations has been evaluated through experiments on multi-sequence brain MR data and multi-modality abdominal CT and MR data. Experiment results demonstrated that our method can achieve better alignment between the source and target data while maintaining superior image quality of signal compared to several state-of-the-art CycleGAN-based methods.
Journal articleRoberts M, Driggs D, Thorpe M, et al., 2021,
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans, Nature Machine Intelligence, Vol: 3, Pages: 199-217
Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.
Journal articleCheung CK, McAdoo SP, 2021,
Journal articlePrendecki M, McAdoo SP, 2021,
Anti-neutrophil cytoplasm antibody (ANCA)- associated vasculitis (AAV) is a rare systemic auto-immune disease characterised by necrotizing inflammation of predominantly small blood vessels and the presence of circulating ANCA directed against myeloperoxidase (MPO) or proteinase-3 (PR3). Current treatment strategies for severe disease, supported by the findings of several well-coordinated randomised control trials, aim to induce remission with high-dose glucocorticoids and either rituximab or cyclophosphamide, followed by relapse prevention with a period of sustained low-dose treatment. This approach has dramatically improved outcomes in AAV, however a significant proportion of patients experience serious treatment-related side effects or suffer relapse. Recent advances in our understanding of disease pathogenesis has led to the identification of novel therapeutic targets which may address these problems, including those directed at the aberrant adaptive autoimmune response (B and T cell directed treatments) and those targeting innate immune elements (complement, monocytes, neutrophils). It is anticipated that these novel treatments, used alone or in combination, will lead to more effective and less-toxic treatment regimens for patients with AAV in the future.
Conference paperWu Y, Hatipoglu S, Alonso-Álvarez D, et al., 2021,
Four-dimensional (4D) left ventricular myocardial velocity mapping (MVM) is a cardiac magnetic resonance (CMR) technique that allows assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 4D MVM also acquires three velocity-encoded phase datasets which are used to generate velocity maps. These can be used to facilitate and improve myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel automated framework that improves the standard U-Net based methods on these CMR multi-channel data (magnitude and phase) by cross-channel fusion with attention module and shape information based post-processing to achieve accurate delineation of both epicardium and endocardium contours. To evaluate the results, we employ the widely used Dice scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows enhanced performance compared to standard UNet based networks trained with single-channel data. Based on the results, our method provides compelling evidence for the design and application for the multi-channel image analysis of the 4D MVM CMR data.
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