Análisis de proteínas receptoras HER2 en imágenes de histología de cáncer de mama

En aquest estudi es presenta una solució basada en Deep Learning per identificar i classificar les cèl·lules d'imatges de càncer de mama tenyides amb HER2, una tinció que afecta la membrana cel·lular. Amb aquest propòsit, s'ha seguit un enfocament de segmentació semàntica, entrenant una xarxa U-Net amb un dataset de 105 imatges amb tinció HER2. Posteriorment, s'ha aplicat un post-processament amb algorismes morfològics de segmentació per tal de quantificar les cèl·lules i calcular el score associat al HER2 que s'assigna a cada pacient. S'han obtingut resultats satisfactoris, aconseguint amb el millor model un F1-score de 0.744 per a la detecció de cèl·lules i una precisió del 90% a la quantificació del score HER2, ambdós en el conjunt de validació. Per tant, els resultats demostren que és un bon mètode per a l'anàlisi d'aquest tipus de biomarcadors i que pot proporcionar suport als patòlegs.This study presents a Deep Learning-based solution to identify and classify cells in breast cancer images with HER2 staining, a staining that affects the cell membrane. For this purpose, a semantic segmentation approach has been followed, training a U-Net on a dataset with 105 HER2-stained images. Subsequently, a post-processing with morphological segmentation algorithms has been applied in order to quantify the cells and calculate the HER2-associated score assigned to each patient. Satisfactory results have been obtained, achieving with the best model an F1-score of 0.744 for cell detection and a 90% accuracy in the quantification of the HER2 score, both in the validation set. Therefore, the results demonstrate that it is a good method for the analysis of this type of biomarker and that it can provide support to pathologists.En este estudio se presenta una solución basada en Deep Learning para identificar y clasificar las células de imágenes de cáncer de mama teñidas con HER2, tinción que afecta la membrana celular. Para ello se ha seguido un enfoque de segmentación semántica, entrenando una U-Net en un dataset con 105 imágenes con tinción HER2. Posteriormente se ha aplicado un postprocesado con algoritmos morfológicos de segmentación para poder cuantificar las células y calcular el score asociado al HER2 que se asigna a cada paciente. Se han obtenido resultados satisfactorios, consiguiendo con el mejor modelo un F1-score del 0.744 para la detección de células y un 90% de accuracy en la cuantificación del score HER2, ambos en el conjunto de validación. Por lo tanto, los resultados demuestran que es un buen método para el análisis de este tipo de biomarcador y que puede dar soporte a los patólogos

Agronomic and Environmental Assessment of a Polyculture Rooftop Soilless Urban Home Garden in a Mediterranean City

Unidad de excelencia María de Maeztu MdM-2015-0552Altres ajuts: Generalitat de Catalunya grant (FI-DGR 2016) and the Universitat Autònoma de Barcelona scholarship (PIF-UAB 2017)Urban planning has been focusing its attention on urban rooftop agriculture as an innovative way to produce local and reliable food in unused spaces in cities. However, there is a lack of quantitative data on soilless urban home gardens and their contribution to self-sufficiency. The aim of the present study is to provide quantitative agronomic and environmental data on an actual soilless urban garden to estimate its degree of self-sufficiency and sustainability. For this purpose, an 18 m2 soilless polyculture rooftop urban home garden in the city center of Barcelona was analyzed. From 2015 to 2017, 22 different crops were grown to feed 2 people in an open-air soilless system, and a life cycle assessment was performed. A total productivity of 10.6 kg/m2/year was achieved, meaning that 5.3 m2 would be needed to fulfill the yearly vegetable requirements of an average citizen (in terms of weight). Considering the vegetable market basket of Catalonia, an 8.2 m2 soilless garden would be needed to cover 62% of the market basket for one person. The top 5 most productive crops were tomato, chard, lettuce, pepper and eggplant, accounting for 85.5% of the total production. The water consumption was 3.7 L/m2/day, and 3.3 kg/year/m2 of waste was generated. A high degree of self-sufficiency was achieved, although adjustments could be made to adapt the production to the market basket. The environmental assessment showed that the fertilizers and their associated leachates accounted for the highest environmental impacts in all the studied impact categories. Overall, 0.6 kg CO2 eq. was generated per kg of vegetables produced. The quantitative data provided by the present study offer a reference from which urban planners and researchers can project future implementations of rooftop urban agriculture (UA) on a large scale

Agronomic and Environmental Assessment of a Polyculture Rooftop Soilless Urban Home Garden in a Mediterranean City

Urban planning has been focusing its attention on urban rooftop agriculture as an innovative way to produce local and reliable food in unused spaces in cities. However, there is a lack of quantitative data on soilless urban home gardens and their contribution to self-sufficiency. The aim of the present study is to provide quantitative agronomic and environmental data on an actual soilless urban garden to estimate its degree of self-sufficiency and sustainability. For this purpose, an 18 m2 soilless polyculture rooftop urban home garden in the city center of Barcelona was analyzed. From 2015 to 2017, 22 different crops were grown to feed 2 people in an open-air soilless system, and a life cycle assessment was performed. A total productivity of 10.6 kg/m2/year was achieved, meaning that 5.3 m2 would be needed to fulfill the yearly vegetable requirements of an average citizen (in terms of weight). Considering the vegetable market basket of Catalonia, an 8.2 m2 soilless garden would be needed to cover 62% of the market basket for one person. The top 5 most productive crops were tomato, chard, lettuce, pepper and eggplant, accounting for 85.5% of the total production. The water consumption was 3.7 L/m2/day, and 3.3 kg/year/m2 of waste was generated. A high degree of self-sufficiency was achieved, although adjustments could be made to adapt the production to the market basket. The environmental assessment showed that the fertilizers and their associated leachates accounted for the highest environmental impacts in all the studied impact categories. Overall, 0.6 kg CO2 eq. was generated per kg of vegetables produced. The quantitative data provided by the present study offer a reference from which urban planners and researchers can project future implementations of rooftop urban agriculture (UA) on a large scale

Symptom-based predictive model of COVID-19 disease in children

Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is neither always accessible nor easy to perform in children. We aimed to propose a machine learning model to assess the need for a SARS-CoV-2 test in children (<16 years old), depending on their clinical symptoms. Methods: Epidemiological and clinical data were obtained from the REDCap® registry. Overall, 4434 SARS-CoV-2 tests were performed in symptomatic children between 1 November 2020 and 31 March 2021, 784 were positive (17.68%). We pre-processed the data to be suitable for a machine learning (ML) algorithm, balancing the positive-negative rate and preparing subsets of data by ageThis research has received external funding from the Fundació la Marató tv3 after being awarded in the COVID-19 research call with the expedient number 202134-30-31.Objectius de Desenvolupament Sostenible::3 - Salut i BenestarPostprint (published version