Efecto de las variantes genéticas en apoa5 en la activación de la lipoproteina lipasa y su asociación al síndrome de quilomicronemia familiar

La lipoproteína Lipasa (LPL) tiene entre sus activadores a la APO-AV, aunque existe controversia sobre si esta resulta esencial en la activación de la enzima. Nuestro objetivo fue estudiar el efecto de determinadas variantes genéticas en APOA5 presentes en 4 pacientes con historia de Hipertrigliceridemia Grave (HTG) sobre la actividad de la Lipoproteína Lipasa post-heparina in vitro y su asociación con las manifestaciones clínicas del Síndrome de Quilomicronemia Familiar (SQF). Material y métodos: Para estudiar la capacidad de activación del suero cada paciente sobre la actividad Lipoproteína Lipasa, se añadieron cantidades crecientes del suero pre-heparina de cada paciente, como fuente de APO-AV, a la mezcla de reacción (10, 20 y 40 µL; pre-calentados a 56 °C durante 60 minutos con PMSF al 0.1% (m/v)). En cada ensayo enzimático a punto final se empleó LPL de un plasma post-heparina (100 U/Kg) procedente de un individuo sano. Por otro lado, se estableció el perfil apolipoproteico mediante turbidimetría, ELISA y ultracentrifugación secuencial, se estableció la presencia de HPLI mediante el cálculo del cociente de triglicéridos en quilomicrones entre triglicéridos en VLDL y se recogieron datos clínicos y antropométricos. Resultados: PACIENTE RESUMEN DE VARIANTES GENÉTICAS EN APOA5 1 Hom c.758T>C 2 het c.758T>C & c.326_327insC 3 Het c.990_993delAACA & c.289C>T 4 het c.289C>T & c.50-2ª>G Los pacientes 1, 2 y 3, presentaron HPLI, y hospitalizaciones por episodios de pancreatitis. Además, los sueros pre-heparina de estos pacientes no activaron significativamente la actividad LPL (p<0.05). En cambio, el paciente 4 no presentó HPLI, no tuvo episodios de pancreatitis y su plasma pre-heparina sí activó significativamente la actividad LPL (p<0.05). Conclusiones: En pacientes con historia clínica de HTG, determinadas variantes genéticas en APOA5 no activan a la LPL, asociándose además a la presencia de HPLI y a una clínica compatible con SQF.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

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 (&lt;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 (&lt;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

Lipoprotein lipase activity and mass, apolipoprotein C-II mass and polymorphisms of apolipoproteins E and A5 in subjects with prior acute hypertriglyceridaemic pancreatitis

Journal Article; Research Support, Non-U.S. Gov't;BACKGROUND Severe hypertriglyceridaemia due to chylomicronemia may trigger an acute pancreatitis. However, the basic underlying mechanism is usually not well understood. We decided to analyze some proteins involved in the catabolism of triglyceride-rich lipoproteins in patients with severe hypertriglyceridaemia. METHODS Twenty-four survivors of acute hypertriglyceridaemic pancreatitis (cases) and 31 patients with severe hypertriglyceridaemia (controls) were included. Clinical and anthropometrical data, chylomicronaemia, lipoprotein profile, postheparin lipoprotein lipase mass and activity, hepatic lipase activity, apolipoprotein C II and CIII mass, apo E and A5 polymorphisms were assessed. RESULTS Only five cases were found to have LPL mass and activity deficiency, all of them thin and having the first episode in childhood. No cases had apolipoprotein CII deficiency. No significant differences were found between the non-deficient LPL cases and the controls in terms of obesity, diabetes, alcohol consumption, drug therapy, gender distribution, evidence of fasting chylomicronaemia, lipid levels, LPL activity and mass, hepatic lipase activity, CII and CIII mass or apo E polymorphisms. However, the SNP S19W of apo A5 tended to be more prevalent in cases than controls (40% vs. 23%, NS). CONCLUSION Primary defects in LPL and C-II are rare in survivors of acute hypertriglyceridaemic pancreatitis; lipase activity measurements should be restricted to those having their first episode during childhood.Part of the studies were financed by grants from the Swedish Research Council and from the King Gustaf V and Queen Victoria Research Fund and by grants from Grupos de Investigacion y Desarrollo Tecnologico de la Junta de Andalucia (Grupo consolidado CTS- 159).Ye

Blood pressure-lowering effects of nifedipine/candesartan combinations in high-risk individuals: Subgroup analysis of the DISTINCT randomised trial

The DISTINCT study (reDefining Intervention with Studies Testing Innovative Nifedipine GITS - Candesartan Therapy) investigated the efficacy and safety of nifedipine GITS/candesartan cilexetil combinations vs respective monotherapies and placebo in patients with hypertension. This descriptive sub-analysis examined blood pressure (BP)-lowering effects in high-risk participants, including those with renal impairment (estimated glomerular filtration rate<90 ml min-1, n=422), type 2 diabetes mellitus (n=202), hypercholesterolaemia (n=206) and cardiovascular (CV) risk factors (n=971), as well as the impact of gender, age and body mass index (BMI). Participants with grade I/II hypertension were randomised to treatment with nifedipine GITS (N) 20, 30, 60 mg and/or candesartan cilexetil (C) 4, 8, 16, 32 mg or placebo for 8 weeks. Mean systolic BP and diastolic BP reductions after treatment in high-risk participants were greater, overall, with N/C combinations vs respective monotherapies or placebo, with indicators of a dose-response effect. Highest rates of BP control (ESH/ESC 2013 guideline criteria) were also achieved with highest doses of N/C combinations in each high-risk subgroup. The benefits of combination therapy vs monotherapy were additionally observed in patient subgroups categorised by gender, age or BMI. All high-risk participants reported fewer vasodilatory adverse events in the pooled N/C combination therapy than the N monotherapy group. In conclusion, consistent with the DISTINCT main study outcomes, high-risk participants showed greater reductions in BP and higher control rates with N/C combinations compared with respective monotherapies and lesser vasodilatory side-effects compared with N monotherapy

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio