Valoración funcional postquirúrgica en pacientes con diagnóstico de hallux valgus tratados con técnica mínimamente invasiva en el Servicio de Traumatología Hospital Luis Vernaza, durante el año 2017

Introducción: El hallux valgus se debe a una deformaciónevolutiva del primer segmento metatarso-digital delpie, viéndose afectados por tanto el primer metatarsianojunto con sus dos sesamoideos, lo que produce un cuadromás complejo que una simple deformidad estética.Por lo tanto, el presente estudio tuvo por objetivo valorarla evolución de los pacientes con el diagnóstico de halluxvalgus tratados con técnica mínimamente invasiva.Materiales y métodos: Se realizó un estudio prospectivode cohorte en una serie de 21 pacientes con diagnósticode hallux valgus moderado y severo que acudieron al HospitalLuis Vernaza en un período de 6 meses comprendidodesde enero hasta julio del 2017. Se utilizó la escala devaloración funcional de la American Orthopeadic Foot andAnkle Society Score (AOFAS).Resultados: Del total de 21 pacientes el 31,1% (n=8)fueron hombres y 61,9% (n=13) mujeres con una edadpromedio de 47,2 años. El 71,43% tuvo una valoraciónglobal AOFAS “buena”; el dolor fue clasificado como leveen el 61,90%. Las mujeres tuvieron una mayor frecuenciade dolor leve (69,23%) que los hombres (50,0%). Los pacientescon una buena evolución presentaron un 80% dedolor leve, mientras que los pacientes con una mala evolucióntuvieron dolor moderado en el 100% de los casos.Conclusiones: La cirugía mínimamente invasiva es eficazpara el tratamiento de hallux valgus, dejando de ladolas complicaciones que pueden existir no por el tipo deintervención, sino por la destreza quirúrgica de quien laejecuta. Se produce mejoría del dolor después de la intervenciónquirúrgica, con una acentuada mejoría en lasmujeres, así como se define que el mayor porcentaje depacientes que son intervenidas son del sexo femenino,concordando con la fisiopatología de la enfermedad en surelación con el uso del calzado

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

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6 $\times$ 6 $\times$ 6 m$^3$ liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019–2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties.DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6x6x6m3 liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light. The scintillation light signal in these detectors can provide the trigger for non-beam events. In addition, it adds precise timing capabilities and improves the calorimetry measurements. In ProtoDUNE-DP, scintillation and electroluminescence light produced by cosmic muons in the LArTPC is collected by photomultiplier tubes placed up to 7 m away from the ionizing track. In this paper, the ProtoDUNE-DP photon detection system performance is evaluated with a particular focus on the different wavelength shifters, such as PEN and TPB, and the use of Xe-doped LAr, considering its future use in giant LArTPCs. The scintillation light production and propagation processes are analyzed and a comparison of simulation to data is performed, improving understanding of the liquid argon properties

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