Adenocarcinoma in Caroli's Disease Treated by Liver Transplantation

Caroli's disease is characterized by congenital cystic dilatation of the intrahepatic bile ducts. In 7% of casea a malignant tumor develops complicating the course of the disease

The social support in kinship foster care: a way to enhance resilience.

This paper analyses how social support enhances family resilience in kinship foster families by involving the families in an educational group programme. Sixty-two kinship foster families from Spain participated in the research. The data were collected before the programme (interviews) and after the programme (interviews and focus groups), and it was analysed by content analysis with the program Atlas.ti. The results show that the factors that contribute most to the development of family resilience are (i) feeling able to look for solutions when faced with problems; (ii) an increase of their network of formal support; (iii) being able to offer support to other foster families; and (iv) feeling that the support they give to parents' foster children is socially recognized

Clasificación de medidas de glucemia en función de ingestas en diabetes gestacional

Este trabajo presenta un clasificador de medidas de glucemia en función de las ingestas asociadas para pacientes con diabetes gestacional. Se presentan los resultados obtenidos al comparar la relevancia de diferentes atributos así como del uso de dos de los algoritmos más populares en el mundo del aprendizaje automático: las redes neuronales y los árboles de decisión. El estudio se ha realizado con los datos de 53 pacientes pertenecientes al Hospital de Sabadell y al Hospital Mutua de Terrassa obteniendo un 91,72% de precisión en el caso de la red neuronal, y un 95.92% con el árbol de decisión. La clasificación automática de medidas de glucemia permitirá a los especialistas pautar un tratamiento más acertado en base a la información obtenida directamente del glucómetro de las pacientes, contribuyendo así al desarrollo de los sistemas automáticos de ayuda a la decisión para diabetes gestacional

Automatic blood glucose classification for gestational diabetes with feature selection: decision trees vs neural networks

Automatic blood glucose classification may help specialists to provide a better interpretation of blood glucose data, downloaded directly from patients glucose meter and will contribute in the development of decision support systems for gestational diabetes. This paper presents an automatic blood glucose classifier for gestational diabetes that compares 6 different feature selection methods for two machine learning algorithms: neural networks and decision trees. Three searching algorithms, Greedy, Best First and Genetic, were combined with two different evaluators, CSF and Wrapper, for the feature selection. The study has been made with 6080 blood glucose measurements from 25 patients. Decision trees with a feature set selected with the Wrapper evaluator and the Best first search algorithm obtained the best accuracy: 95.92%

Depicting the battle between nectarine and Monilinia laxa: the fruit developmental stage dictates the effectiveness of the host defenses and the pathogen’s infection strategies

Infections by the fungus Monilinia laxa, the main cause of brown rot in Europe, result in considerable losses of stone fruit. Herein, we present a comprehensive transcriptomic approach to unravel strategies deployed by nectarine fruit and M. laxa during their interaction. We used M. laxa-inoculated immature and mature fruit, which was resistant and susceptible to brown rot, respectively, to perform a dual RNA-Seq analysis. In immature fruit, host responses, pathogen biomass, and pathogen transcriptional activity peaked at 14–24 h post inoculation (hpi), at which point M. laxa appeared to switch its transcriptional response to either quiescence or death. Mature fruit experienced an exponential increase in host and pathogen activity beginning at 6 hpi. Functional analyses in both host and pathogen highlighted differences in stage-dependent strategies. For example, in immature fruit, M. laxa unsuccessfully employed carbohydrate-active enzymes (CAZymes) for penetration, which the fruit was able to combat with tightly regulated hormone responses and an oxidative burst that challenged the pathogen’s survival at later time points. In contrast, in mature fruit, M. laxa was more dependent on proteolytic effectors than CAZymes, and was able to invest in filamentous growth early during the interaction. Hormone analyses of mature fruit infected with M. laxa indicated that, while jasmonic acid activity was likely useful for defense, high ethylene activity may have promoted susceptibility through the induction of ripening processes. Lastly, we identified M. laxa genes that were highly induced in both quiescent and active infections and may serve as targets for control of brown rot.info:eu-repo/semantics/publishedVersio

Simulador de dispositivos SLM basados en cristal líquido

En este trabajo, se presenta un simulador de modulación de fase espacial para su uso en comunicaciones ópticas de espacio libre. El simulador está basado en dispositivos TNLCD (Twisted Nematic Liquid Crystal Display). En primer lugar, se examinan las características y propiedades de los cristales líquidos como medios uniáxicos birrefrigentes. Posteriormente, mediante un modelo analítico simple, se caracterizan los diferentes elementos ópticos de un modulador espacial de luz (SLM) en términos de sus correspondientes matrices de Jones. Las expresiones deducidas en esta caracterización se utilizan en la herramienta de simulación para obtener la diferencia de fase espacial y la transmitancia producida por el SLM. Finalmente, el funcionamiento del simulador se ha verificado comparando resultados teóricos y de simulación para diferentes excitaciones de pruebaUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Trust and contextual engagement with the PEPPER system: The qualitative findings of a clinical feasibility study

Background and aims. PEPPER (Patient Empowerment through Predictive PERsonalised decision support) is an EU-funded research project which aims to improve self-management of type 1 diabetes (T1D). The system comprises an AI insulin bolus recommender, coupled with a safety system. The aim of the qualitative arm of this clinical feasibility study was to examine the context of participants’ interaction with the PEPPER system and identify incidents where bolus recommendations were trusted and accepted. Methods. This was a multicentre (UK and Spain) non-randomised open-labelled 6-week pilot study. Thirteen adults with T1D participated in weekly telephone interviews to explore the context of their interactions and responses to PEPPER. Data was thematically analysed through conceptual frameworks for engagement with healthcare digital behaviour change interventions. Results. Participants reported their key interactions as responding to PEPPER bolus recommendations, inputting carbohydrate values, interpreting continuous glucose monitoring (CGM) values through visualization of personal data and dealing with safety alarms. Two themes were associated with trust and engagement with the system; ‘feeling monitored’ and ‘feeling in control’. The incidents where participants trusted PEPPER also enhanced personal expertise of T1D through insights provided by the safety system such as low glucose basal insulin for pump users. Benefits were balanced against technical challenges of the system, which were used to improve the PEPPER application and enhance user experience. Conclusion. Some participants suggested that even access to PEPPER for a temporary period could positively influence self-management strategies. Contextual interviewing is a valuable tool in mobile application development for diabetes decision support systems