Arthropathy of genetic hemochromatosis: a major and distinctive manifestation of the disease

Genetic hemochromatosis is not a rare disease and represents a frequently underestimated cause of arthropathy. Joint involvement is one of the most frequent manifestations of the disease and presents typical clinical and radiological features that strongly suggest the diagnosis. Joint complaints are often the first clinical manifestation of GH. Their identification may be crucial to establish the diagnosis in the pre-cirrhotic phase and to institute appropriate therapy to prevent organ damage and associated mortality. Recent identification of the genetic defect responsible for the disease is leading to new insights into the pathogenesis of GH and the associated arthropathy

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. METHODS: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). RESULTS: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. CONCLUSION: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

Zero-shot super-resolution with a physically-motivated downsampling kernel for endomicroscopy

Super-resolution (SR) methods have seen significant advances thanks to the development of convolutional neural networks (CNNs). CNNs have been successfully employed to improve the quality of endomicroscopy imaging. Yet, the inherent limitation of research on SR in endomicroscopy remains the lack of ground truth high-resolution (HR) images, commonly used for both supervised training and reference-based image quality assessment (IQA). Therefore, alternative methods, such as unsupervised SR are being explored. To address the need for non-reference image quality improvement, we designed a novel zero-shot super-resolution (ZSSR) approach that relies only on the endomicroscopy data to be processed in a self-supervised manner without the need for ground-truth HR images. We tailored the proposed pipeline to the idiosyncrasies of endomicroscopy by introducing both: a physically-motivated Voronoi downscaling kernel accounting for the endomicroscope’s irregular fibre-based sampling pattern, and realistic noise patterns. We also took advantage of video sequences to exploit a sequence of images for self-supervised zero-shot image quality improvement. We run ablation studies to assess our contribution in regards to the downscaling kernel and noise simulation. We validate our methodology on both synthetic and original data. Synthetic experiments were assessed with reference-based IQA, while our results for original images were evaluated in a user study conducted with both expert and non-expert observers. The results demonstrated superior performance in image quality of ZSSR reconstructions in comparison to the baseline method. The ZSSR is also competitive when compared to supervised single-image SR, especially being the preferred reconstruction technique by experts

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

BACKGROUND: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. RESULTS: We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. CONCLUSION: Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site

Learning from irregularly sampled data for endomicroscopy super-resolution: a comparative study of sparse and dense approaches

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) enables performing an optical biopsy via a probe. pCLE probes consist of multiple optical fibres arranged in a bundle, which taken together generate signals in an irregularly sampled pattern. Current pCLE reconstruction is based on interpolating irregular signals onto an over-sampled Cartesian grid, using a naive linear interpolation. It was shown that convolutional neural networks (CNNs) could improve pCLE image quality. Yet classical CNNs may be suboptimal in regard to irregular data. METHODS: We compare pCLE reconstruction and super-resolution (SR) methods taking irregularly sampled or reconstructed pCLE images as input. We also propose to embed a Nadaraya-Watson (NW) kernel regression into the CNN framework as a novel trainable CNN layer. We design deep learning architectures allowing for reconstructing high-quality pCLE images directly from the irregularly sampled input data. We created synthetic sparse pCLE images to evaluate our methodology. RESULTS: The results were validated through an image quality assessment based on a combination of the following metrics: peak signal-to-noise ratio and the structural similarity index. Our analysis indicates that both dense and sparse CNNs outperform the reconstruction method currently used in the clinic. CONCLUSION: The main contributions of our study are a comparison of sparse and dense approach in pCLE image reconstruction. We also implement trainable generalised NW kernel regression as a novel sparse approach. We also generated synthetic data for training pCLE SR

Exuberant calcinosis and acroosteolysis. A diagnostic challenge

A case of exuberant acroosteolysis and subcutaneous tissue calcinosis in the absence of skin involvement is presented. Different hypotheses are discussed following the clinical unfolding of the case in practice

A case of infliximab-induced lupus in a patient with ankylosing spondylitis: is it safe switch to another anti-TNF-α agent?

Anti-TNF-α therapies are the latest class of medications found to be associated with drug-induced lupus, a distinctive entity known as anti-TNF-α-induced lupus (ATIL) (Williams et al., Rheumatology (Oxford) 48:716-20, 2009; De Rycke et al., Lupus 14:931-7, 2005; De Bandt et al., Clin Rheumatol 22:56-61, 2003). With the widespread use of these agents, it is likely that the incidence of ATIL will increase. The onset of ATIL in patients with rheumatoid arthritis and Crohn's disease has been described, but the literature regarding the occurrence of this entity in patients with ankylosing spondylitis (AS) is scarce (De Bandt et al., Clin Rheumatol 22:56-61, 2003; Ramos-Casals et al., Autoimmun Rev 9:188-93, 2010; Perez-Garcia et al., Rheumatology 45:114-116, 2006). To our knowledge, few reports of switching anti-TNF-α therapy after ATIL in AS have been reported (Akgül et al., Rheumatol Int, 2012). Therefore, it is not clear whether the development of ATIL should prohibit switch to another therapy, since patients may respond to another anti-TNF-α agent (Akgül et al., Rheumatol Int, 2012; Bodur et al., Rheumatol Int 29:451-454, 2009; Mounach et al., Clin Exp Rheumatol 26:1116-8, 2008; Williams and Cohen, Int J Dermatol 50:619-625, 2011; Ye et al., J Rheumatol 38:1216, 2011; Wetter and Davis, Mayo Clin Proc 84:979-984, 2009; Cush, Clin Exp Rheumatol 22:S141-147, 2004; Kocharla and Mongey, Lupus 18:169-7, 2009). A lack of published experience of successful anti-TNF-α switching is a cause of concern for rheumatologists faced with this challenging clinical scenario. We report the case of a 69-year-old woman with AS who developed infliximab-induced lupus, which did not recur despite the subsequent institution of etanercept. The authors review and discuss ATIL and the possible implications for subsequent treatment with alternative anti-TNF-α agents