THE EFFECTS OF THERAPEUTIC HYPOTHERMIA ON CYTOCHROME P450-MEDIATED METABOLISM: STUDIES IN TRANSLATIONAL RESEARCH

Therapeutic hypothermia decreases neurological damage in patients experiencing out-of-hospital cardiac arrest (CA). In addition to hypothermia, critically ill patients are treated with an extensive pharmacotherapeutic regimen. The majority of these medications are hepatically eliminated via the cytochrome P450 (CYP450) system. Changes in drug clearance could limit the putative benefit of hypothermic therapy. With the increased use of therapeutic hypothermia and the fact that critically ill patients receive multiple medications, it is crucial to understand the effects of hypothermia on the disposition and metabolism of drugs used in this population. Thus, it was the overall aim of this research to investigate the effects of therapeutic hypothermia on CYP450-mediated metabolism in an animal model of CA, in human liver microsomes, and in normal healthy subjects. Specifically, hypothermia produced a ~2 fold decrease in the systemic clearance (CLS) of intravenous chlorzoxazone, a specific CYP2E1 probe substrate, in a CA rat model when compared to CA rats treated under normothermic temperatures. The mechanism behind this decrease in CLS was a hypothermia-mediated decrease in the affinity of CYP2E1 for chlorzoxazone. We extended the experimental period to investigate the effects of hypothermia after re-warming, on CYP2E1 and CYP3A2 activity and expression. Our results indicate that rats with CA treated under normothermic temperatures demonstrated a significant decrease in the activities of CYP2E1 and CYP3A2, 24 hrs after injury compared to control. Furthermore, CA significantly decreased the expression of CYP3A2, but not the expression of CYP2E1. CA also produced a ~ 10-fold increase in plasma concentrations of interleukin-6 (IL-6) compared to Control. The CA-mediated reduction in CYP3A2 and CYP2E1 activity, mRNA, and the increase in IL-6 plasma concentrations was attenuated by hypothermia. We also investigated the effects of mild and moderate hypothermia on CYP2E1 and CYP3A4 enzyme kinetics in human liver microsomes. Both mild and moderate hypothermia significantly decreased the Vmax of CYP2E1 and CYP3A4. However, hypothermia increased the Km of CYP2E1 but not CYP3A4. These data demonstrate that mild and moderate hypothermia may produce isoform specific alterations of human CYP450-mediated metabolism. Lastly, in a pilot analysis, we showed that mild hypothermia may potentially alter the ClS and the volume of distribution (Vss) of midazolam in mildly hypothermic normal healthy volunteers.Collectively, this work provides evidence that therapeutic hypothermia alters CYP450-mediated metabolism both during cooling and after re-warming. Based on the magnitude of these changes it is clear that intensivists should be cognizant of these alterations and monitor drug levels and outcomes in their patients when possible. In addition to increased clinical attention, future research efforts are essential to delineate precise dosing guidelines and mechanisms of the effects of hypothermia on drug disposition, metabolism, and response

Hedgehog-driven myogenic tumors recapitulate skeletal muscle cellular heterogeneity

Hedgehog (Hh) pathway activation in R26-SmoM2;CAGGS-CreER mice, which carry a tamoxifen-inducible activated Smoothened allele (SmoM2), results in numerous microscopic tumor foci in mouse skeletal muscle. These tumors exhibit a highly differentiated myogenic phenotype and resemble human fetal rhabdomyomas. This study sought to apply previously established strategies to isolate lineally distinct populations of normal mouse myofiber-associated cells in order to examine cellular heterogeneity in SmoM2 tumors. We demonstrate that established SmoM2 tumors are composed of cells expressing myogenic, adipocytic and hematopoietic lineage markers and differentiation capacity. SmoM2 tumors thus recapitulate the phenotypic and functional hetereogeneity observed in normal mouse skeletal muscle. SmoM2 tumors also contain an expanded population of PAX7+ and MyoD+ satellite-like cells with extremely low clonogenic activity. Selective activation of Hh signaling in freshly isolated muscle satellite cells enhanced terminal myogenic differentiation without stimulating proliferation. Our findings support the conclusion that SmoM2 tumors represent an aberrant skeletal muscle state and demonstrate that, similar to normal muscle, myogenic tumors contain functionally distinct cell subsets, including cells lacking myogenic differentiation potential

Deep Underground Neutrino Experiment (DUNE) Near Detector Conceptual Design Report

International audienceThe Deep Underground Neutrino Experiment (DUNE) is an international, world-class experiment aimed at exploring fundamental questions about the universe that are at the forefront of astrophysics and particle physics research. DUNE will study questions pertaining to the preponderance of matter over antimatter in the early universe, the dynamics of supernovae, the subtleties of neutrino interaction physics, and a number of beyond the Standard Model topics accessible in a powerful neutrino beam. A critical component of the DUNE physics program involves the study of changes in a powerful beam of neutrinos, i.e., neutrino oscillations, as the neutrinos propagate a long distance. The experiment consists of a near detector, sited close to the source of the beam, and a far detector, sited along the beam at a large distance. This document, the DUNE Near Detector Conceptual Design Report (CDR), describes the design of the DUNE near detector and the science program that drives the design and technology choices. The goals and requirements underlying the design, along with projected performance are given. It serves as a starting point for a more detailed design that will be described in future documents

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

International audienceLiquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype

Deep Underground Neutrino Experiment (DUNE), Far Detector Technical Design Report, Volume I Introduction to DUNE

International audienceThe preponderance of matter over antimatter in the early universe, the dynamics of the supernovae that produced the heavy elements necessary for life, and whether protons eventually decay—these mysteries at the forefront of particle physics and astrophysics are key to understanding the early evolution of our universe, its current state, and its eventual fate. The Deep Underground Neutrino Experiment (DUNE) is an international world-class experiment dedicated to addressing these questions as it searches for leptonic charge-parity symmetry violation, stands ready to capture supernova neutrino bursts, and seeks to observe nucleon decay as a signature of a grand unified theory underlying the standard model. The DUNE far detector technical design report (TDR) describes the DUNE physics program and the technical designs of the single- and dual-phase DUNE liquid argon TPC far detector modules. This TDR is intended to justify the technical choices for the far detector that flow down from the high-level physics goals through requirements at all levels of the Project. Volume I contains an executive summary that introduces the DUNE science program, the far detector and the strategy for its modular designs, and the organization and management of the Project. The remainder of Volume I provides more detail on the science program that drives the choice of detector technologies and on the technologies themselves. It also introduces the designs for the DUNE near detector and the DUNE computing model, for which DUNE is planning design reports. Volume II of this TDR describes DUNE's physics program in detail. Volume III describes the technical coordination required for the far detector design, construction, installation, and integration, and its organizational structure. Volume IV describes the single-phase far detector technology. A planned Volume V will describe the dual-phase technology

Highly-parallelized simulation of a pixelated LArTPC on a GPU

The rapid development of general-purpose computing on graphics processing units (GPGPU) is allowing the implementation of highly-parallelized Monte Carlo simulation chains for particle physics experiments. This technique is particularly suitable for the simulation of a pixelated charge readout for time projection chambers, given the large number of channels that this technology employs. Here we present the first implementation of a full microphysical simulator of a liquid argon time projection chamber (LArTPC) equipped with light readout and pixelated charge readout, developed for the DUNE Near Detector. The software is implemented with an end-to-end set of GPU-optimized algorithms. The algorithms have been written in Python and translated into CUDA kernels using Numba, a just-in-time compiler for a subset of Python and NumPy instructions. The GPU implementation achieves a speed up of four orders of magnitude compared with the equivalent CPU version. The simulation of the current induced on $10^3$ pixels takes around 1 ms on the GPU, compared with approximately 10 s on the CPU. The results of the simulation are compared against data from a pixel-readout LArTPC prototype