Darunavir/cobicistat/emtricitabine/tenofovir alafenamide versus dolutegravir /abacavir/lamivudine in antiretroviral-naïve adults (SYMTRI): a multicenter randomized open-label study (PReEC/RIS-57)

D/C/F/TAF is the reference for combination therapy based on protease inhibitors but has not been compared with regimens containing integrase inhibitors as initial ART. We could not demonstrate D/C/F/TAF noninferiority relative to DTG/ABC/3TC, although both regimens were similarly well tolerated. Background Darunavir/cobicistat/emtricitabine/tenofovir alafenamide (D/C/F/TAF) is the reference for combination therapy based on protease inhibitors due to its efficacy, tolerability, and convenience. Head-to-head randomized comparisons between D/C/F/TAF and combination therapy based on integrase inhibitors in antiretroviral-naive patients are lacking. Methods Adult (>18 years old) human immunodeficiency virus-infected antiretroviral-naive patients (HLA-B*5701 negative and hepatitis B virus negative), with viral load (VL) >= 500 c/mL, were centrally randomized to initiate D/C/F/TAF or dolutegravir/abacavir/lamivudine (DTG/3TC/ABC) after stratifying by VL and CD4 count. Clinical and analytical assessments were performed at weeks 0, 4, 12, 24, and 48. The primary endpoint was VL 100 000 copies/mL, and 13% had <200 CD4 cells/mu L. Median weight was 73 kg and median body mass index was 24 kg/m(2). At 48 weeks, 79% (D/C/F/TAF) versus 82% (DTG/3TC/ABC) had VL <50 c/mL (difference, -2.4%; 95% confidence interval [CI], -11.3 to 6.6). Eight percent versus four percent experienced virologic failure but no resistance-associated mutations emerged. Four percent versus six percent had drug discontinuation due to adverse events. In the per-protocol analysis, 94% versus 96% of patients had VL <50 c/mL (difference, -2%; 95% CI, -8.1 to 3.5). There were no differences in CD4 cell count or weight changes. Conclusions We could not demonstrate the noninferiority of D/C/F/TAF relative to DTG/ABC/3TC as initial antiretroviral therapy, although both regimens were similarly well tolerated

Effect of Alirocumab on Lipoprotein(a) and Cardiovascular Risk After Acute Coronary Syndrome

Lipoprotein(a) concentration is associated with cardiovascular events. Alirocumab, a proprotein convertase subtilisin/kexin type 9 inhibitor, lowers lipoprotein(a) and low-density lipoprotein cholesterol (LDL-C). A pre-specified analysis of the placebo-controlled ODYSSEY Outcomes trial in patients with recent acute coronary syndrome (ACS) determined whether alirocumab-induced changes in lipoprotein(a) and LDL-C independently predicted major adverse cardiovascular events (MACE). One to 12 months after ACS, 18,924 patients on high-intensity statin therapy were randomized to alirocumab or placebo and followed for 2.8 years (median). Lipoprotein(a) was measured at randomization and 4 and 12 months thereafter. The primary MACE outcome was coronary heart disease death, nonfatal myocardial infarction, ischemic stroke, or hospitalization for unstable angina. Baseline lipoprotein(a) levels (median: 21.2 mg/dl; interquartile range [IQR]: 6.7 to 59.6 mg/dl) and LDL-C [corrected for cholesterol content in lipoprotein(a)] predicted MACE. Alirocumab reduced lipoprotein(a) by 5.0 mg/dl (IQR: 0 to 13.5 mg/dl), corrected LDL-C by 51.1 mg/dl (IQR: 33.7 to 67.2 mg/dl), and reduced the risk of MACE (hazard ratio [HR]: 0.85; 95% confidence interval [CI]: 0.78 to 0.93). Alirocumab-induced reductions of lipoprotein(a) and corrected LDL-C independently predicted lower risk of MACE, after adjustment for baseline concentrations of both lipoproteins and demographic and clinical characteristics. A 1-mg/dl reduction in lipoprotein(a) with alirocumab was associated with a HR of 0.994 (95% CI: 0.990 to 0.999; p = 0.0081). Baseline lipoprotein(a) and corrected LDL-C levels and their reductions by alirocumab predicted the risk of MACE after recent ACS. Lipoprotein(a) lowering by alirocumab is an independent contributor to MACE reduction, which suggests that lipoprotein(a) should be an independent treatment target after ACS. (ODYSSEY Outcomes: Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab; NCT01663402

Design, upgrade and characterization of the silicon photomultiplier front-end for the AMIGA detector at the Pierre Auger Observatory

AMIGA (Auger Muons and Infill for the Ground Array) is an upgrade of the Pierre Auger Observatory to complement the study of ultra-high-energy cosmic rays (UHECR) by measuring the muon content of extensive air showers (EAS). It consists of an array of 61 water Cherenkov detectors on a denser spacing in combination with underground scintillation detectors used for muon density measurement. Each detector is composed of three scintillation modules, with 10 m$^2$ detection area per module, buried at 2.3 m depth, resulting in a total detection area of 30 m$^2$. Silicon photomultiplier sensors (SiPM) measure the amount of scintillation light generated by charged particles traversing the modules. In this paper, the design of the front-end electronics to process the signals of those SiPMs and test results from the laboratory and from the Pierre Auger Observatory are described. Compared to our previous prototype, the new electronics shows a higher performance, higher efficiency and lower power consumption, and it has a new acquisition system with increased dynamic range that allows measurements closer to the shower core. The new acquisition system is based on the measurement of the total charge signal that the muonic component of the cosmic ray shower generates in the detector.Comment: 40 pages, 33 figure

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

Measurement of the cosmic-ray energy spectrum above 2.5 x 10(18) eV using the Pierre Auger Observatory

We report a measurement of the energy spectrum of cosmic rays for energies above 2.5×10$^{18}$ eV based on 215,030 events recorded with zenith angles below 60°. A key feature of the work is that the estimates of the energies are independent of assumptions about the unknown hadronic physics or of the primary mass composition. The measurement is the most precise made hitherto with the accumulated exposure being so large that the measurements of the flux are dominated by systematic uncertainties except at energies above 5×10$^{19}$ eV. The principal conclusions are (1) The flattening of the spectrum near 5×10$^{18}$ eV, the so-called “ankle,” is confirmed. (2) The steepening of the spectrum at around 5×10$^{Z19}$ eV is confirmed. (3) A new feature has been identified in the spectrum: in the region above the ankle the spectral index γ of the particle flux (∝E$^{−γ }$) changes from 2.51±0.03 (stat)±0.05 (syst) to 3.05±0.05 (stat)±0.10 (syst) before changing sharply to 5.1±0.3 (stat)±0.1 (syst) above 5×10$^{19}$ eV. (4) No evidence for any dependence of the spectrum on declination has been found other than a mild excess from the Southern Hemisphere that is consistent with the anisotropy observed above 8×10$^{18}$ eV

Features of the energy spectrum of cosmic rays above 2.5×1018 eV using the pierre auger observatory

We report a measurement of the energy spectrum of cosmic rays above 2.5×10$^{18}$ eV based on 215 030 events. New results are presented: at about 1.3×10$^{19}$ eV, the spectral index changes from 2.51±0.03(stat)±0.05(syst) to 3.05±0.05(stat)±0.10(syst), evolving to 5.1±0.3(stat)±0.1(syst) beyond 5×10$^{19}$ eV, while no significant dependence of spectral features on the declination is seen in the accessible range. These features of the spectrum can be reproduced in models with energy-dependent mass composition. The energy density in cosmic rays above 5×10$^{18}$ eV is [5.66±0.03(stat)±1.40(syst)]×10$^{53}$ erg Mpc$^{-3}$

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

The Pierre Auger Observatory, at present the largest cosmic-ray observatory ever built, is instrumented with a ground array of 1600 water-Cherenkov detectors, known as the Surface Detector (SD). The SD samples the secondary particle content (mostly photons, electrons, positrons and muons) of extensive air showers initiated by cosmic rays with energies ranging from $10^{17}~$eV up to more than $10^{20}~$eV. Measuring the independent contribution of the muon component to the total registered signal is crucial to enhance the capability of the Observatory to estimate the mass of the cosmic rays on an event-by-event basis. However, with the current design of the SD, it is difficult to straightforwardly separate the contributions of muons to the SD time traces from those of photons, electrons and positrons. In this paper, we present a method aimed at extracting the muon component of the time traces registered with each individual detector of the SD using Recurrent Neural Networks. We derive the performances of the method by training the neural network on simulations, in which the muon and the electromagnetic components of the traces are known. We conclude this work showing the performance of this method on experimental data of the Pierre Auger Observatory. We find that our predictions agree with the parameterizations obtained by the AGASA collaboration to describe the lateral distributions of the electromagnetic and muonic components of extensive air showers.Comment: 23 pages, 15 figures. Version accepted for publication in JINS