The impact of direct acting antivirals on hepatitis C virus disease burden and associated costs in four european countries

Background and Aims We assessed the clinical and economic impact of direct-acting antiviral (DAA) therapy for hepatitis C virus (HCV) in England, Italy, Romania and Spain.Methods An HCV progression Markov model was developed considering DAA eligibility and population data during the years 2015-2019. The period of time to recover the investment in DAAs was calculated as the cost saved by avoiding estimated clinical events for 1000 standardized treated patients. A delayed treatment scenario because of coronavirus disease (COVID-19) was also developed.Results The estimated number of avoided hepatocellular carcinoma, decompensated cirrhosis and liver transplantations over a 20-year time horizon was: 1,057 in England; 1,221 in Italy; 1,211 in Romania; and 1,103 in Spain for patients treated during 2015-2016 and 640 in England; 626 in Italy; 739 in Romania; and 643 in Spain for patients treated during 2017-2019. The cost-savings ranged from euro 45 to euro 275 million. The investment needed to expand access to DAAs in 2015-2019 is estimated to be recovered in 6.5 years in England; 5.4 years in Italy; 6.7 years in Romania; and 4.5 years in Spain. A delay in treatment because of COVID-19 will increase liver mortality in all countries.Conclusion Direct-acting antivirals have significant clinical benefits and can bring substantial cost-savings over the next 20 years, reaching a Break-even point in a short period of time. When pursuing an exit strategy from strict lockdown measures for COVID-19, providing DAAs should remain high on the list of priorities in order to maintain HCV elimination efforts

“Estudios de algunos fármacos coleréticos del mercado terapéutico español”

It is studied the choleretic activity of different natural (vegetal or animal origin) and semisynthetical drugs so that the pharmacological profile can be fixed.Se estudia la actividad colerética de diferentes fármacos de origen natural (vegetal o animal) y semisintéticos, con el fin de determinar el perfil farmacológico de los mismos

Obtaining agricultural land cover in Sentinel-2 satellite images with drone image injection using Random Forest in Google Earth Engine

[ES] Para obtener información precisa sobre los cambios de la cubierta terrestre en el sector agrícola, proponemos un método de clasificación supervisada que integra las imágenes del satélite Sentinel-2 con las imágenes obtenidas de los Sistemas de Aeronaves Pilotadas a Distancia (RPAS, por sus siglas en inglés). La metodología se aplicó en la plataforma de Google Earth Engine. Inicialmente, la colección de imágenes de Sentinel-2 se integró en una sola imagen mediante un proceso de reducción de mediana. Posteriormente, se aplicó el método de fusión de imágenes de pansharpening con filtro de paso alto (HPF, por sus siglas en inglés) a las bandas espectrales térmicas para obtener una resolución espacial final de 10 m. Para realizar la integración de las dos fuentes de imágenes, la imagen del RPAS se normalizó utilizando un filtro de textura gaussiano de 5×5 y el píxel se re-muestreó a cinco veces su tamaño original. Este procedimiento se realizó de forma iterativa hasta alcanzar la resolución espacial de la imagen del Sentinel-2. Además, se añadieron a la clasificación los siguientes datos: los índices espectrales, calculados a partir de las bandas de Sentinel-2 y RPAS (por ejemplo, NDVI, NDWI, SIPI, GARI), la información altimétrica y las pendientes de la zona derivadas del MED SRTM. La clasificación supervisada se realizó utilizando la técnica de Random Forest (Machine Learning). La referencia de la semilla de la cubierta terrestre para realizar la clasificación fue capturada manualmente por un experto temático, luego, esta referencia fue distribuida en un 70% para el entrenamiento del algoritmo de Random Forest y en un 30% para validar la clasificación. Los resultados muestran que la incorporación de la imagen RPAS mejora los indicadores de precisión temática en un promedio del 3% en comparación con una clasificación realizada exclusivamente con imágenes de Sentinel-2.[EN] To obtain accurate information on land cover changes in the agricultural sector, we propose a supervised classification method that integrates Sentinel-2 satellite imagery with images surveyed from Remote Piloted Aircraft Systems (RPAS). The methodology was implemented on the Google Earth Engine platform. Initially, the Sentinel-2 imagery collection was integrated into a single image through a median reduction process. Subsequently, the high-pass filter (HPF) pansharpening image fusion method was applied to the thermal spectral bands to obtain a final spatial resolution of 10 m. To perform the integration of the two image sources, the RPAS image was normalized by using a 5X5 gaussian texture filter and the pixel was resampled to five times its original size. This procedure was performed iteratively until reaching the spatial resolution of the Sentinel-2 imagery. Besides, the following inputs were added to the classification: the spectral indices calculated from the Sentinel-2 and RPAS bands (e.g. NDVI, NDWI, SIPI, GARI); altimetric information and slopes of the zone derived from the SRTM DEM. The supervised classification was done by using the Random Forest technique (Machine Learning). The land cover seed reference to perform the classification was manually captured by a thematic expert, then, this reference was distributed in 70% for the training of the Random Forest algorithm and in 30% to validate the classification. The results show that the incorporation of the RPAS image improves thematic accuracy indicators by an average of 3% compared to a classification made exclusively with Sentinel-2 imagery.Departamento Administrativo Nacional de EstadísticaRamírez, M.; Martínez, L.; Montilla, M.; Sarmiento, O.; Lasso, J.; Díaz, S. (2020). Obtención de coberturas del suelo agropecuarias en imágenes satelitales Sentinel-2 con la inyección de imágenes de dron usando Random Forest en Google Earth Engine. Revista de Teledetección. 0(56):49-68. https://doi.org/10.4995/raet.2020.14102OJS496805

P82 238. ¿Es segura la reintervención dejando los injertos arteriales permeables sin clampar?

ObjetivoEl objetivo de no dañar la arteria mamaria evitando su disección y clampaje supone un reto para la protección miocárdica debido al lavado de la cardioplejía.Presentamos nuestra experiencia en reintervenciones sin disecar ni clampar los injertos arteriales.Material y métodosDesde septiembre de 2000 hasta febrero de 2010 se realizaron 29 reintervenciones en 28 pacientes, (89,7% varones), edad media 73,17±7,38años. Las causas de reoperación fueron: progresión de valvulopatía 17 pacientes (58,6%), endocarditis 10 (34,4%), disfunción protésica no estructural 2 (6,9%). La mediana del EuroS-CORE logístico fue 14,84 (4-77,25). La cirugía fue urgente en 7 pacientes. Se realizó sustitución valvular aórtica aislada en 18 y se asoció revascularización en 4, sustitución/plastia mitral en 4, sustitución de aorta en 2; sustitución mitral aislada en 1.La protección miocárdica se realizó con cardioplejía hemática con esmolol, K+ y Mg+, administrándola siempre que no dificultaba el trabajo quirúrgico (intervalos nunca > 20min). Temperatura sistémica media 32,26±3,23 °C.ResultadosLa mediana del tiempo de circulación extracorpórea (CEC) fue 153 (91-494) y de clampaje 103 (71-430)min. Presentaron infarto postoperatorio 1 paciente (3,4%) y síndrome de bajo gasto postoperatorio 2 pacientes (6,5%). La mediana de troponina I postoperatoria fue 7,03ng/ml (1,84-109,5). La mortalidad hospitalaria fue 3 pacientes (10,3%) (cirugía urgente por endocarditis). Las causas de mortalidad fueron: daño neurológico irreversible (1 paciente), sepsis y bajo gasto (2 pacientes). La mediana de estancia hospitalaria fue 7 (1-33) días.ConclusiónEn nuestra experiencia, sin clampar los injertos arteriales y con la estrategia descrita, la protección miocárdica parece adecuada y la mortalidad hospitalaria aceptable

Evaluation of classification algorithms in the Google Earth Engine platform for the identification and change detection of rural and periurban buildings from very high-resolution images

[EN] Building change detection based on remote sensing imagery is a key task for land management and planning e.g., detection of illegal settlements, updating land records and disaster response. Under the post- classification comparison approach, this research aimed to evaluate the feasibility of several classification algorithms to identify and capture buildings and their change between two time steps using very-high resolution images (<1 m/pixel) across rural areas and urban/rural perimeter boundaries. Through an App implemented on the Google Earth Engine (GEE) platform, we selected two study areas in Colombia with different images and input data. In total, eight traditional classification algorithms, three unsupervised (K-means, X-Means y Cascade K-Means) and five supervised (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy and Minimum distance) available at GEE were trained. Additionally, a deep neural network named Feature Pyramid Networks (FPN) was added and trained using a pre-trained model, EfficientNetB3 model. Three evaluation zones per study area were proposed to quantify the performance of the algorithms through the Intersection over Union (IoU) metric. This metric, with a range between 0 and 1, represents the degree of overlapping between two regions, where the higher agreement the higher IoU values. The results indicate that the models configured with the FPN network have the best performance followed by the traditional supervised algorithms. The performance differences were specific to the study area. For the rural area, the best FPN configuration obtained an IoU averaged for both time steps of 0.4, being this four times higher than the best supervised model, Support Vector Machines using a linear kernel with an average IoU of 0.1. Regarding the setting of urban/rural perimeter boundaries, this difference was less marked, having an average IoU of 0.53 in comparison to 0.38 obtained by the best supervised classification model, in this case Random Forest. The results are relevant for institutions tracking the dynamics of building areas from cloud computing platfo future assessments of classifiers in likewise platforms in other contexts.[ES] La detección de cambios de áreas construidas basada en datos de teledetección es una importante herramienta para el ordenamiento y la administración del territorio p.e.: la identificación de construcciones ilegales, la actualización de registros catastrales y la atención de desastres. Bajo el enfoque de comparación post-clasificación, la presente investigación tuvo como objetivo evaluar la funcionalidad de varios algoritmos de clasificación para identificar y capturar las construcciones y su cambio entre dos fechas de análisis usando imágenes de alta resolución (<1 m/píxel) en ámbitos rurales y límites del perímetro urbano municipal. La anterior evaluación fue llevada a cabo a través de una aplicación desarrollada mediante la plataforma Google Earth Engine (GEE), donde se alojaron y analizaron diferentes imágenes y datos de entrada sobre dos áreas de estudio en Colombia. En total, ocho algoritmos de clasificación tradicional, tres no supervisados (K-means, X-Means y Cascade K-Means) y cinco supervisados (Random Forest, Support Vector Machine, Naive Bayes, GMO maximum Entropy y Minimum distance) fueron entrenados empleando GEE. Adicionalmente, se entrenó una red neuronal profunda denominada Feature Pyramid Networks (FPN) sobre la cual se aplicó la estrategia de modelos preentrenados, usando pesos del modelo EfficientNetB3. Por cada una de las dos áreas de estudio, tres zonas de evaluación fueron propuestas para cuantificar la funcionalidad de los algoritmos mediante la métrica Intersection over Union (IoU). Esta métrica representa la evaluación de la superposición de dos regiones y tiene un rango de valores de 0 a 1, donde a mayor coincidencia de las imágenes mayor es el valor de IoU. Los resultados indican que los modelos configurados con la red FPN tienen la mejor funcionalidad, seguido de los algoritmos tradicionales supervisados. Las diferencias de la funcionalidad fueron específicas por área de estudio. Para el ámbito rural, la mejor configuración de FPN obtuvo un IoU promedio entre ambas fechas de 0,4, es decir, cuatro veces el mejor modelo supervisado, correspondiente al Support Vector Machine de kernel Lineal con un IoU de 0,1. Respecto al área de límites del perímetro urbano municipal, esta diferencia fue menos marcada, con un IoU promedio de 0,53 en comparación con el 0,38 derivado del mejor modelo de clasificación supervisada, que en este caso fue Random Forest. Los resultados de esta investigación son relevantes para entidades responsables del seguimiento de las dinámicas de las áreas construidas a partir de plataformas de procesamiento en la nube como GEE, estableciendo una línea base para futuros estudios evaluando la funcionalidad de los clasificadores disponibles en otros contextos.Los autores agradecen a las Subdirecciones de Catastro, y Geografía y Cartografía del IGAC. Esta investigación hace parte de la licencia del programa GEO-GEE administrada por la Subdirección de Geografía y Cartografía. Se agradece igualmente al equipo de EODataScience por su soporte constante en los desarrollos técnicos de esta investigación.Coca-Castro, A.; Zaraza-Aguilera, MA.; Benavides-Miranda, YT.; Montilla-Montilla, YM.; Posada-Fandiño, HB.; Avendaño-Gomez, AL.; Hernández-Hamon, HA.... (2021). Evaluación de algoritmos de clasificación en la plataforma Google Earth Engine para la identificación y detección de cambios de construcciones rurales y periurbanas a partir de imágenes de alta resolución. Revista de Teledetección. 0(58):71-88. http://hdl.handle.net/10251/169765OJS718805

In-flight Diagnostics in LISA Pathfinder

LISA PathFinder (LPF) will be flown with the objective to test in space key technologies for LISA. However its sensitivity goals are, for good reason, one order of magnitude less than those which LISA will have to meet, both in drag-free and optical metrology requirements, and in the observation frequency band. While the expected success of LPF will of course be of itself a major step forward to LISA, one might not forget that a further improvement by an order of magnitude in performance will still be needed. Clues for the last leap are to be derived from proper disentanglement of the various sources of noise which contribute to the total noise, as measured in flight during the PathFinder mission. This paper describes the principles, workings and requirements of one of the key tools to serve the above objective: the diagnostics subsystem. This consists in sets of temperature, magnetic field, and particle counter sensors, together with generators of controlled thermal and magnetic perturbations. At least during the commissioning phase, the latter will be used to identify feed-through coefficients between diagnostics sensor readings and associated actual noise contributions. A brief progress report of the current state of development of the diagnostics subsystem will be given as well.Peer Reviewe

In-flight Diagnostics in LISA Pathfinder

LISA PathFinder (LPF) will be flown with the objective to test in space key technologies for LISA. However its sensitivity goals are, for good reason, one order of magnitude less than those which LISA will have to meet, both in drag-free and optical metrology requirements, and in the observation frequency band. While the expected success of LPF will of course be of itself a major step forward to LISA, one might not forget that a further improvement by an order of magnitude in performance will still be needed. Clues for the last leap are to be derived from proper disentanglement of the various sources of noise which contribute to the total noise, as measured in flight during the PathFinder mission. This paper describes the principles, workings and requirements of one of the key tools to serve the above objective: the diagnostics subsystem. This consists in sets of temperature, magnetic field, and particle counter sensors, together with generators of controlled thermal and magnetic perturbations. At least during the commissioning phase, the latter will be used to identify feed-through coefficients between diagnostics sensor readings and associated actual noise contributions. A brief progress report of the current state of development of the diagnostics subsystem will be given as well.Peer Reviewe

The chemical and electrochemical oxidative polymerization of 2-amino-4-tert-butylphenol

[EN] Poly(2-amino-4-tert-butylphenol), poly(2A-4TBP), was synthesized from monomer aqueous solution using either electrochemical or chemical oxidation procedures. Several spectroscopic characterization techniques were employed to gain information on the chemical structure and redox behavior of the obtained materials. It was found that the chemical polymerization product could be described as an oligomer mixture containing up to 16 monomer units. In parallel to other polymers derived from o-aminophenol, phenoxazine rings constitute also the basic structure of poly(2A-4TBP). In addition, the occurrence of N-N couplings, which are favored by the presence of the voluminous tert-butyl substituent, seems also relevant. No significant structural differences were found between the chemically or electrochemically synthesized materials. © 2016 Published by Elsevier Ltd.Financial support from the Spanish Ministerio de Economía y Competitividad and FEDER funds (MAT2013-42007-P) and from the Generalitat Valenciana (PROMETEO2013/038) is gratefully acknowledged. M. Abidi thanks the Ministry of Higher Education and Scientific Research of Tunisia for funding her stay at the University of Alicante.Abidi, M.; López-Bernabeu, S.; Huerta, F.; Montilla-Jiménez, F.; Besbes-Hentati, S.; Morallón, E. (2016). The chemical and electrochemical oxidative polymerization of 2-amino-4-tert-butylphenol. Electrochimica Acta. 212:958-965. https://doi.org/10.1016/j.electacta.2016.07.060S95896521

Fish Oil Blunts Lung Function Decrements Induced by Acute Exposure to Ozone in Young Healthy Adults: A Randomized Trial

Background: Over one-third of the U.S. population is exposed to unsafe levels of ozone (O3). Dietary supplementation with fish oil (FO) or olive oil (OO) has shown protection against other air pollutants. This study evaluates potential cardiopulmonary benefits of FO or OO supplementation against acute O3 exposure in young healthy adults. Methods: Forty-three participants (26 ± 4 years old; 47% female) were randomized to receive 3 g/day of FO, 3 g/ day OO, or no supplementation (CTL) for 4 weeks prior to undergoing 2-hour exposures to filtered air and 300 ppb O3 with intermittent exercise on two consecutive days. Outcome measurements included spirometry, sputum neutrophil percentage, blood markers of inflammation, tissue injury and coagulation, vascular function, and heart rate variability. The effects of dietary supplementation and O3 on these outcomes were evaluated with linear mixed-effect models. Results: Compared with filtered air, O3 exposure decreased FVC, FEV1, and FEV1/FVC immediately post exposure regardless of supplementation status. Relative to that in the CTL group, the lung function response to O3 exposure in the FO group was blunted, as evidenced by O3-induced decreases in FEV1 (Normalized CTL − 0.40 ± 0.34 L, Normalized FO − 0.21 ± 0.27 L) and FEV1/FVC (Normalized CTL − 4.67 ± 5.0 %, Normalized FO − 1.4 ± 3.18 %) values that were on average 48% and 70% smaller, respectively. Inflammatory responses measured in the sputum immediately post O3 exposure were not different among the three supplementation groups. Systolic blood pressure elevations 20-h post O3 exposure were blunted by OO supplementation. Conclusion: FO supplementation appears to offer protective effects against lung function decrements caused by acute O3 exposure in healthy adults