Should you treat prediabetes? It's complicated

Should you treat prediabetes? It's complicated. While treatment with metformin or lifestyle modification reduces risk for T2D in patients with prediabetes, neither intervention ultimately offers a mortality benefit.Daniel Holligan, MD; Roxanne Radi, MD, MPH, FAAFP; Corey Lyon, DO, FAAFP (Department of Family Medicine, University of Colorado)Includes bibliographical reference

Does prophylactic azithromycin reduce the number of COPD exacerbations or hospitalizations?

Q Does prophylactic azithromycin reduce the number of COPD exacerbations or hospitalizations? EVIDENCE-BASED ANSWER: Yes for exacerbations, no for hospitalizations. Prophylactic azithromycin reduces the number of exacerbations by about 25%. It also extends the time between exacerbations by approximately 90 days for patients with moderate-to-severe chronic obstructive pulmonary disease (COPD). Azithromycin benefits patients who are >65 years, patients with Global Initiative for Obstructive Lung Disease (GOLD) stage II or III COPD, former smokers, and patients using long-term oxygen; it doesn't benefit patients ≤65 years, patients with GOLD stage IV COPD, current smokers, or patients not using oxygen (strength of recommendation [SOR]: B, randomized controlled trials [RCTs]). Prophylactic azithromycin doesn't reduce hospitalizations overall (SOR: B, single small RCT).Corey Lyon, DO; Roxanne Radi, MD; Thomas Staff, MD, MPH, The University of Colorado, Family Medicine Residency, Program, Denver; Joan Nashelsky, MLS, Family Physicians Inquiries, Network, Iowa City

Does hormone replacement therapy prevent cognitive decline in postmenopausal women?

Q: Evidence-based answer: NO. Hormone replacement therapy (HRT) does not prevent cognitive decline in postmenopausal women—and in fact, it may slightly increase risk (strength of recommendation, A; systematic review, meta-analysis of randomized controlled trials [RCTs], and individual RCT).Madeline Gates, MD; Melissa Beagle, MD, MPH; Lauren Bull, MD; Roxanne Radi, MD, MPH; Corey Lyon, DO (University of Colorado, Family Medicine Residency), Kristen DeSanto, MSLS, MS, RD (University of Colorado, Health Sciences Library), Rick Guthmann, MD, MPH (Advocate Health Care Illinois Masonic Medical Center Program)Includes bibliographical reference

Is low-dose naltrexone effective in chronic pain management?

Q: Is low-dose naltrexone effective in chronic pain management? Evidence-based answer: YES. Low-dose naltrexone is as effective as amitriptyline in the treatment of painful diabetic neuropathy and has a superior safety profile (strength of recommendation [SOR], B; single randomized controlled trial [RCT]). Low-dose naltrexone significantly reduced pain by 32% in inflammatory conditions and 44% in neuropathic conditions (SOR, B; single retrospective cohort study). Doses as low as 5.4 mg were found to reduce pain in 95% of patients with fibromyalgia (SOR, B; single prospective doseresponse study).Roxanne Radi, MD, MPH; Harriet Huang, MD; Jason Rivera, MD; Corey Lyon, DO (University of Colorado Family Medicine Residency), Kristen DeSanto, MSLS, MS, RD (University of Colorado Health Sciences Library). Deputy Editor: Rick Guthmann, MD, MPH (Advocate Health Care Illinois Masonic Medical Center Program)Includes bibliographical reference

DUNE Offline Computing Conceptual Design Report

International audienceThis document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

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

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

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment

DUNE Offline Computing Conceptual Design Report

This document describes Offline Software and Computing for the Deep Underground Neutrino Experiment (DUNE) experiment, in particular, the conceptual design of the offline computing needed to accomplish its physics goals. Our emphasis in this document is the development of the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes. In this effort, we concentrate on developing the tools and systems thatfacilitate the development and deployment of advanced algorithms. Rather than prescribing particular algorithms, our goal is to provide resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves and to provide computing that achieves the physics goals of the DUNE experiment