Liver-specific insulin receptor isoform A expression enhances hepatic glucose uptake and ameliorates liver steatosis in a mouse model of diet-induced obesity

Among the main complications associated with obesity are insulin resistance and altered glucose and lipid metabolism within the liver. It has previously been described that insulin receptor isoform A (IRA) favors glucose uptake and glycogen storage in hepatocytes compared with isoform B (IRB), improving glucose homeostasis in mice lacking liver insulin receptor. Thus, we hypothesized that IRA could also improve glucose and lipid metabolism in a mouse model of high-fatdiet-induced obesity. We addressed the role of insulin receptor isoforms in glucose and lipid metabolism in vivo. We expressed IRA or IRB specifically in the liver by using adeno-associated viruses (AAVs) in a mouse model of diet-induced insulin resistance and obesity. IRA, but not IRB, expression induced increased glucose uptake in the liver and muscle, improving insulin tolerance. Regarding lipid metabolism, we found that AAV-mediated IRA expression also ameliorated hepatic steatosis by decreasing the expression of Fasn, Pgc1a, Acaca and Dgat2 and increasing Scd-1 expression. Taken together, our results further unravel the role of insulin receptor isoforms in hepatic glucose and lipid metabolism in an insulin-resistant scenario. Our data strongly suggest that IRA is more efficient than IRB at favoring hepatic glucose uptake, improving insulin tolerance and ameliorating hepatic steatosis. Therefore, we conclude that a gene therapy approach for hepatic IRA expression could be a safe and promising tool for the regulation of hepatic glucose consumption and lipid metabolism, two key processes in the development of non-alcoholic fatty liver disease associated with obesity

Additional value of screening for minor genes and copy number variants in hypertrophic cardiomyopathy

Introduction: Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited heart disease. Next-generation sequencing (NGS) is the preferred genetic test, but the diagnostic value of screening for minor and candidate genes, and the role of copy number variants (CNVs) deserves further evaluation. Methods: Three hundred and eighty-seven consecutive unrelated patients with HCM were screened for genetic variants in the 5 most frequent genes (MYBPC3, MYH7, TNNT2, TNNI3 and TPM1) using Sanger sequencing (N = 84) or NGS (N = 303). In the NGS cohort we analyzed 20 additional minor or candidate genes, and applied a proprietary bioinformatics algorithm for detecting CNVs. Additionally, the rate and classification of TTN variants in HCM were compared with 427 patients without structural heart disease. Results: The percentage of patients with pathogenic/likely pathogenic (P/LP) variants in the main genes was 33.3%, without significant differences between the Sanger sequencing and NGS cohorts. The screening for 20 additional genes revealed LP variants in ACTC1, MYL2, MYL3, TNNC1, GLA and PRKAG2 in 12 patients. This approach resulted in more inconclusive tests (36.0% vs. 9.6%, p<0.001), mostly due to variants of unknown significance (VUS) in TTN. The detection rate of rare variants in TTN was not significantly different to that found in the group of patients without structural heart disease. In the NGS cohort, 4 patients (1.3%) had pathogenic CNVs: 2 deletions in MYBPC3 and 2 deletions involving the complete coding region of PLN. Conclusions: A small percentage of HCM cases without point mutations in the 5 main genes are explained by P/LP variants in minor or candidate genes and CNVs. Screening for variants in TTN in HCM patients drastically increases the number of inconclusive tests, and shows a rate of VUS that is similar to patients without structural heart disease, suggesting that this gene should not be analyzed for clinical purposes in HCM

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Serous cystic neoplasm of the pancreas: A multinational study of 2622 patients under the auspices of the International Association of Pancreatology and European Pancreatic Club (European Study Group on Cystic Tumors of the Pancreas)

OBJECTIVES: Serous cystic neoplasm (SCN) is a cystic neoplasm of the pancreas whose natural history is poorly known. The purpose of the study was to attempt to describe the natural history of SCN, including the specific mortality. DESIGN: Retrospective multinational study including SCN diagnosed between 1990 and 2014. RESULTS: 2622 patients were included. Seventy-four per cent were women, and median age at diagnosis was 58\u2005years (16-99). Patients presented with non-specific abdominal pain (27%), pancreaticobiliary symptoms (9%), diabetes mellitus (5%), other symptoms (4%) and/or were asymptomatic (61%). Fifty-two per cent of patients were operated on during the first year after diagnosis (median size: 40\u2005mm (2-200)), 9% had resection beyond 1\u2005year of follow-up (3\u2005years (1-20), size at diagnosis: 25\u2005mm (4-140)) and 39% had no surgery (3.6\u2005years (1-23), 25.5\u2005mm (1-200)). Surgical indications were (not exclusive) uncertain diagnosis (60%), symptoms (23%), size increase (12%), large size (6%) and adjacent organ compression (5%). In patients followed beyond 1\u2005year (n=1271), size increased in 37% (growth rate: 4\u2005mm/year), was stable in 57% and decreased in 6%. Three serous cystadenocarcinomas were recorded. Postoperative mortality was 0.6% (n=10), and SCN's related mortality was 0.1% (n=1). CONCLUSIONS: After a 3-year follow-up, clinical relevant symptoms occurred in a very small proportion of patients and size slowly increased in less than half. Surgical treatment should be proposed only for diagnosis remaining uncertain after complete workup, significant and related symptoms or exceptionally when exists concern with malignancy. This study supports an initial conservative management in the majority of patients with SCN

Design and construction of the MicroBooNE Cosmic Ray Tagger system

The MicroBooNE detector utilizes a liquid argon time projection chamber (LArTPC) with an 85 t active mass to study neutrino interactions along the Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground level, the detector records many cosmic muon tracks in each beam-related detector trigger that can be misidentified as signals of interest. To reduce these cosmogenic backgrounds, we have designed and constructed a TPC-external Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for High Energy Physics (LHEP), Albert Einstein center for fundamental physics, University of Bern. The system utilizes plastic scintillation modules to provide precise time and position information for TPC-traversing particles. Successful matching of TPC tracks and CRT data will allow us to reduce cosmogenic background and better characterize the light collection system and LArTPC data using cosmic muons. In this paper we describe the design and installation of the MicroBooNE CRT system and provide an overview of a series of tests done to verify the proper operation of the system and its components during installation, commissioning, and physics data-taking

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal