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Conference paperPrendecki M, Bhatt T, Dudhiya F, et al., 2018,
SPLEEN TYROSINE KINASE EXPRESSION IN HUMAN NEUTROPHILS IN AAV
, 55th Congress of the European-Renal-Association (ERA) and European-Dialysis-and-Transplantation-Association (EDTA), Publisher: OXFORD UNIV PRESS, ISSN: 0931-0509 -
Journal articleAlrashed F, Calay D, Lang M, et al., 2018,
Celecoxib exerts protective effects in the vascular endothelium via COX-2-independent activation of AMPK-CREB-Nrf2 signalling
, Scientific Reports, Vol: 8, ISSN: 2045-2322Although concern remains about the athero-thrombotic risk posed by cyclo-oxygenase (COX)-2-selective inhibitors, recent data implicates rofecoxib, while celecoxib appears equivalent to NSAIDs naproxen and ibuprofen. We investigated the hypothesis that celecoxib activates AMP kinase (AMPK) signalling to enhance vascular endothelial protection. In human arterial and venous endothelial cells (EC), and in contrast to ibuprofen and naproxen, celecoxib induced the protective protein heme oxygenase-1 (HO-1). Celecoxib derivative 2,5-dimethyl-celecoxib (DMC) which lacks COX-2 inhibition also upregulated HO-1, implicating a COX-2-independent mechanism. Celecoxib activated AMPKα(Thr172) and CREB-1(Ser133) phosphorylation leading to Nrf2 nuclear translocation. Importantly, these responses were not reproduced by ibuprofen or naproxen, while AMPKα silencing abrogated celecoxib-mediated CREB and Nrf2 activation. Moreover, celecoxib induced H-ferritin via the same pathway, and increased HO-1 and H-ferritin in the aortic endothelium of mice fed celecoxib (1000 ppm) or control chow. Functionally, celecoxib inhibited TNF-α-induced NF-κB p65(Ser536) phosphorylation by activating AMPK. This attenuated VCAM-1 upregulation via induction of HO-1, a response reproduced by DMC but not ibuprofen or naproxen. Similarly, celecoxib prevented IL-1β-mediated induction of IL-6. Celecoxib enhances vascular protection via AMPK-CREB-Nrf2 signalling, a mechanism which may mitigate cardiovascular risk in patients prescribed celecoxib. Understanding NSAID heterogeneity and COX-2-independent signalling will ultimately lead to safer anti-inflammatory drugs.
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Journal articleYang G, Zhuang X, Khan H, et al., 2018,
Fully automatic segmentation and objective assessment of atrial scars for long-standing persistent atrial fibrillation patients using late gadolinium-enhanced MRI.
, Med Phys, Vol: 45, Pages: 1562-1576PURPOSE: Atrial fibrillation (AF) is the most common heart rhythm disorder and causes considerable morbidity and mortality, resulting in a large public health burden that is increasing as the population ages. It is associated with atrial fibrosis, the amount and distribution of which can be used to stratify patients and to guide subsequent electrophysiology ablation treatment. Atrial fibrosis may be assessed noninvasively using late gadolinium-enhanced (LGE) magnetic resonance imaging (MRI) where scar tissue is visualized as a region of signal enhancement. However, manual segmentation of the heart chambers and of the atrial scar tissue is time consuming and subject to interoperator variability, particularly as image quality in AF is often poor. In this study, we propose a novel fully automatic pipeline to achieve accurate and objective segmentation of the heart (from MRI Roadmap data) and of scar tissue within the heart (from LGE MRI data) acquired in patients with AF. METHODS: Our fully automatic pipeline uniquely combines: (a) a multiatlas-based whole heart segmentation (MA-WHS) to determine the cardiac anatomy from an MRI Roadmap acquisition which is then mapped to LGE MRI, and (b) a super-pixel and supervised learning based approach to delineate the distribution and extent of atrial scarring in LGE MRI. We compared the accuracy of the automatic analysis to manual ground truth segmentations in 37 patients with persistent long-standing AF. RESULTS: Both our MA-WHS and atrial scarring segmentations showed accurate delineations of cardiac anatomy (mean Dice = 89%) and atrial scarring (mean Dice = 79%), respectively, compared to the established ground truth from manual segmentation. In addition, compared to the ground truth, we obtained 88% segmentation accuracy, with 90% sensitivity and 79% specificity. Receiver operating characteristic analysis achieved an average area under the curve of 0.91. CONCLUSION: Compared with previously studied methods with manual interve
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Journal articlePrendecki M, Martin L, Tanna A, et al., 2018,
Increased prevalence of thyroid disease in patients with antineutrophil cytoplasmic antibodies-associated vasculitis
, Journal of Rheumatology, Vol: 45, ISSN: 0315-162XOBJECTIVE: Antineutrophil cytoplasmic antibodies (ANCA)-associated vasculitis (AAV) has been linked with thyroid disease as a result of antithyroid medications. We assessed the prevalence of thyroid disease in our patients with AAV. METHODS: Clinical records of 279 patients with AAV diagnosed between 1991 and 2014 were analyzed. RESULTS: Thyroid disease was identified in 21.5% of patients, but only 2 had previously received propylthiouracil. There was a greater proportion of female patients, patients with antimyeloperoxidase antibodies, and patients with renal disease in the group with thyroid disease. CONCLUSION: Our data show a higher prevalence of thyroid disease in patients with AAV than the general population. This was not attributable to antithyroid drugs.
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Conference papervan Daalen EE, Jennette JC, McAdoo SP, et al., 2018,
Predicting Outcome in Patients with Anti-GBM Glomerulonephritis
, 18th International Vasculitis and ANCA Workshop, Publisher: AMER SOC NEPHROLOGY, Pages: 63-72, ISSN: 1555-9041- Author Web Link
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Journal articleMcAdoo SP, Medjeral-Thomas N, Gopaluni S, et al.,
Long-term Follow-up of a Combined Rituximab and Cyclophosphamide Regimen in Renal ANCA-associated Vasculitis
, Nephrology Dialysis Transplantation, ISSN: 0931-0509 -
Journal articleYu S, Dong H, Yang G, et al.,
Deep De-Aliasing for Fast Compressive Sensing MRI
Fast Magnetic Resonance Imaging (MRI) is highly in demand for many clinicalapplications in order to reduce the scanning cost and improve the patientexperience. This can also potentially increase the image quality by reducingthe motion artefacts and contrast washout. However, once an image field of viewand the desired resolution are chosen, the minimum scanning time is normallydetermined by the requirement of acquiring sufficient raw data to meet theNyquist-Shannon sampling criteria. Compressive Sensing (CS) theory has beenperfectly matched to the MRI scanning sequence design with much less requiredraw data for the image reconstruction. Inspired by recent advances in deeplearning for solving various inverse problems, we propose a conditionalGenerative Adversarial Networks-based deep learning framework for de-aliasingand reconstructing MRI images from highly undersampled data with great promiseto accelerate the data acquisition process. By coupling an innovative contentloss with the adversarial loss our de-aliasing results are more realistic.Furthermore, we propose a refinement learning procedure for training thegenerator network, which can stabilise the training with fast convergence andless parameter tuning. We demonstrate that the proposed framework outperformsstate-of-the-art CS-MRI methods, in terms of reconstruction error andperceptual image quality. In addition, our method can reconstruct each image in0.22ms--0.37ms, which is promising for real-time applications.
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Journal articleHao J, Wang C, Zhang H, et al.,
Annealing Genetic GAN for Minority Oversampling
The key to overcome class imbalance problems is to capture the distributionof minority class accurately. Generative Adversarial Networks (GANs) have shownsome potentials to tackle class imbalance problems due to their capability ofreproducing data distributions given ample training data samples. However, thescarce samples of one or more classes still pose a great challenge for GANs tolearn accurate distributions for the minority classes. In this work, we proposean Annealing Genetic GAN (AGGAN) method, which aims to reproduce thedistributions closest to the ones of the minority classes using only limiteddata samples. Our AGGAN renovates the training of GANs as an evolutionaryprocess that incorporates the mechanism of simulated annealing. In particular,the generator uses different training strategies to generate multiple offspringand retain the best. Then, we use the Metropolis criterion in the simulatedannealing to decide whether we should update the best offspring for thegenerator. As the Metropolis criterion allows a certain chance to accept theworse solutions, it enables our AGGAN steering away from the local optimum.According to both theoretical analysis and experimental studies on multipleimbalanced image datasets, we prove that the proposed training strategy canenable our AGGAN to reproduce the distributions of minority classes from scarcesamples and provide an effective and robust solution for the class imbalanceproblem.
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