Inappropriate antibiotic use in the COVID-19 era: Factors associated with inappropriate prescribing and secondary complications. Analysis of the registry SEMI-COVID

Background: Most patients with COVID-19 receive antibiotics despite the fact that bacterial co-infections are rare. This can lead to increased complications, including antibacterial resistance. We aim to analyze risk factors for inappropriate antibiotic prescription in these patients and describe possible complications arising from their use. Methods: The SEMI-COVID-19 Registry is a multicenter, retrospective patient cohort. Patients with antibiotic were divided into two groups according to appropriate or inappropriate prescription, depending on whether the patient fulfill any criteria for its use. Comparison was made by means of multilevel logistic regression analysis. Possible complications of antibiotic use were also identified. Results: Out of 13,932 patients, 3047 (21.6%) were prescribed no antibiotics, 6116 (43.9%) were appropriately prescribed antibiotics, and 4769 (34.2%) were inappropriately prescribed antibiotics. The following were independent factors of inappropriate prescription: February-March 2020 admission (OR 1.54, 95%CI 1.18-2.00), age (OR 0.98, 95%CI 0.97-0.99), absence of comorbidity (OR 1.43, 95%CI 1.05-1.94), dry cough (OR 2.51, 95%CI 1.94-3.26), fever (OR 1.33, 95%CI 1.13-1.56), dyspnea (OR 1.31, 95%CI 1.04-1.69), flu-like symptoms (OR 2.70, 95%CI 1.75-4.17), and elevated C-reactive protein levels (OR 1.01 for each mg/L increase, 95% CI 1.00-1.01). Adverse drug reactions were more frequent in patients who received ANTIBIOTIC (4.9% vs 2.7%, p < .001). Conclusion: The inappropriate use of antibiotics was very frequent in COVID-19 patients and entailed an increased risk of adverse reactions. It is crucial to define criteria for their use in these patients. Knowledge of the factors associated with inappropriate prescribing can be helpful

La retórica y el debate como herramientas de aprendizaje

Convocatoria proyectos de innovación de Extremadura 2019/2020Se describe un proyecto llevado a cabo en el IES San Fernando (Badajoz) surgido del Departamento de Filosofía que tiene como objetivo principal mejorar las habilidades comunicativas del alumnado. Se pretendía que supieran hablar, transmitir ideas y opiniones, saber escuchar, compartir posturas, rebatir y estar preparados para cambiar de opinión. Otros objetivos del trabajo fueron: transmitir una visión de sociedad que se basa en el diálogo constructivo a través de una interacción respetuosa, rigurosamente fundamentada, reflexiva e inclusiva; lograr en los alumnos el desarrollo del pensamiento crítico y conseguir que comprendan la información que reciben, las inferencias y que sean capaces de evaluarla y generar nuevas propuestasExtremaduraES

La expresión oral y escrita, objetivo fundamental de calidad de la enseñanza

Convocatoria de Proyectos de innovación de Extremadura 2017/2018Se describe un proyecto llevado a cabo en el IES Miguel Durán (Azuaga, Badajoz) desarrollado dentro del Sistema de Gestión de Calidad del centro, que tenía como objetivo principal que los alumnos adquirieran las competencias necesarias para una correcta expresión oral y escrita (mejora de la competencia informacional y las competencias comunicativa e idiomática). Para conseguir el objetivo se organizaron distintas actividades promovidas por el Grupo de Trabajo de la Biblioteca del centro: fomento de la lectura, feria del libro, Día de la poesía, diversas actividades de lectura en la biblioteca, club de lectura con libros digitales, etc., así como la realización de otras actividades y la elaboración de materiales por parte de los distintos departamentos del instituto como: exposiciones de los alumnos ante el aula, lectura en distintos soportes, realización de trabajos de investigación, actividades de enriquecimiento del vocabulario, etc.ExtremaduraES

Promoción turística sostenible de la reserva de la biosfera Tajo-Tejo Internacional

Convocatoria proyectos de innovación de Extremadura 2020/2021Se describe un proyecto llevado acabo por varios centros educativos ubicados en la zona de la Reserva de la Biosfera Tajo-Tejo Internacional (RBTTI) que pretendía contribuir a la transformación sostenible del entorno mediante su conocimiento y promoción, implementando las competencias digital, social y ciudadana y la cultura emprendedora mediante metodologías activas como el aprendizaje servicio. Entre los objetivos principales del proyecto destacan: dar a conocer las implicaciones de la RBTTI; diseñar una campaña de promoción de la RBTTI mediante trípticos y vídeos promocionales; conocer la Reserva a través de las principales vías pecuarias y caminos que comunican los pueblos; descubrir los principales elementos socioculturales, históricos y tradicionales de la Reserva; valorar la importancia del territorio para conservar la biodiversidad: paisajes, ecosistemas, fauna y flora representativa; relacionar la trashumancia y las vías pecuarias como rasgos identificativos de la Reserva, vinculándolo con la historia y rasgos culturales de los pueblos y valorar el emprendimiento y la iniciativa personal, el asosiacionismo y creación de redes de cooperación en y entre pueblos como motor de desarrolloExtremaduraES

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

Reconstruction of interactions in the ProtoDUNE-SP detector with Pandora

International audienceThe Pandora Software Development Kit and algorithm libraries provide pattern-recognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNE-SP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNE-SP, located at CERN, is exposed to a charged-particle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNE-SP. In complex events with numerous cosmic-ray and beam background particles, the simulated reconstruction and identification efficiency for triggered test-beam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/$c$ charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1$\pm0.6$% and 84.1$\pm0.6$%, respectively. The efficiencies measured for test-beam data are shown to be within 5% of those predicted by the simulation

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

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