A Case of Drug-Induced Hepatitis due to Lenalidomide

Lenalidomide is a recent thalidomide analog used for the treatment of refractory multiple myeloma. The main toxicity of this drug consists in severe neutropenia and thrombocytopenia. Lenalidomide-associated liver injury is rare, manifesting itself as elevated liver enzymes and hyperbilirubinemia reversible upon weeks after drug withdrawal. We report here in detail the clinical course as well as the biological and histological alterations of an acute lenalidomide-induced liver injury. Findings on liver biopsy allowed us to discriminate acute inflammatory changes due to the drug and minor associated lesions of graft-versus-host disease in this patient with recurrent myeloma after allogeneic bone marrow transplantation

MiniBooNE and LSND data: non-standard neutrino interactions in a (3+1) scheme versus (3+2) oscillations

The recently observed event excess in MiniBooNE anti-neutrino data is in agreement with the LSND evidence for electron anti-neutrino appearance. We propose an explanation of these data in terms of a (3+1) scheme with a sterile neutrino including non-standard neutrino interactions (NSI) at neutrino production and detection. The interference between oscillations and NSI provides a source for CP violation which we use to reconcile different results from neutrino and anti-neutrino data. Our best fit results imply NSI at the level of a few percent relative to the standard weak interaction, in agreement with current bounds. We compare the quality of the NSI fit to the one obtained within the (3+1) and (3+2) pure oscillation frameworks. We also briefly comment on using NSI (in an effective two-flavour framework) to address a possible difference in neutrino and anti-neutrino results from the MINOS experiment.Comment: 28 pages, 9 figures, discussion improved, new appendix added, conclusions unchange

Graphical Approach to Model Reduction for Nonlinear Biochemical Networks

Model reduction is a central challenge to the development and analysis of multiscale physiology models. Advances in model reduction are needed not only for computational feasibility but also for obtaining conceptual insights from complex systems. Here, we introduce an intuitive graphical approach to model reduction based on phase plane analysis. Timescale separation is identified by the degree of hysteresis observed in phase-loops, which guides a “concentration-clamp” procedure for estimating explicit algebraic relationships between species equilibrating on fast timescales. The primary advantages of this approach over Jacobian-based timescale decomposition are that: 1) it incorporates nonlinear system dynamics, and 2) it can be easily visualized, even directly from experimental data. We tested this graphical model reduction approach using a 25-variable model of cardiac β1-adrenergic signaling, obtaining 6- and 4-variable reduced models that retain good predictive capabilities even in response to new perturbations. These 6 signaling species appear to be optimal “kinetic biomarkers” of the overall β1-adrenergic pathway. The 6-variable reduced model is well suited for integration into multiscale models of heart function, and more generally, this graphical model reduction approach is readily applicable to a variety of other complex biological systems

Individual Facial Coloration in Male Eulemur fulvus rufus: A Condition-dependent Ornament?

Researchers studying individual variation in conspicuous skin coloration in primates have suggested that color indicates male quality. Although primate fur color can also be flamboyant, the potential condition dependence and thus signaling function of fur remains poorly studied. We studied sources of variation in sexually dichromatic facial hair coloration in red-fronted lemurs (Eulemur fulvus rufus). We collected data on 13 adult males in Kirindy Forest, Madagascar, during two study periods in 2006 and 2007, to determine whether variation in facial hair coloration correlates with male age, rank, androgen status, and reproductive success. We quantified facial hair coloration via standardized digital photographs of each male, assessed androgen status using fecal hormone measurements, and obtained data on reproductive success through genetic paternity analyses. Male facial hair coloration showed high individual variation, and baseline coloration was related to individual androgen status but not to any other parameter tested. Color did not reflect rapid androgen changes during the mating season. However, pronounced long-term changes in androgen levels between years were accompanied by changes in facial hair coloration. Our data suggest that facial hair coloration in red-fronted lemur males is under proximate control of androgens and may provide some information about male quality, but it does not correlate with dominance rank or male reproductive success

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions

QCD and strongly coupled gauge theories : challenges and perspectives

We highlight the progress, current status, and open challenges of QCD-driven physics, in theory and in experiment. We discuss how the strong interaction is intimately connected to a broad sweep of physical problems, in settings ranging from astrophysics and cosmology to strongly coupled, complex systems in particle and condensed-matter physics, as well as to searches for physics beyond the Standard Model. We also discuss how success in describing the strong interaction impacts other fields, and, in turn, how such subjects can impact studies of the strong interaction. In the course of the work we offer a perspective on the many research streams which flow into and out of QCD, as well as a vision for future developments.Peer reviewe