Efecto del tipo y cantidad de alúmina como dopante sobre la densificación y las propiedades eléctricas de electrodos cerámicos de óxido de zinc

[EN] Aluminum-doped zinc oxide (AZO) electrodes can be a good alternative to replace the expensive electrodes (Ti, ITO, FTO, etc.), which are used in the electrooxidation process to remove refractory and emergent contaminants from industrial wastewaters. AZO electrodes have been prepared by the traditional ceramic method using ZnO as the main raw material and different precursors of Al2O3 as dopant sources. Densification, microstructure and electric resistivity of AZO electrodes are a function of precursor's nature and sintering thermal treatment. The higher the number of precursor's particles and the smaller their size, the sintering temperature needed to attain high densifications and low resistivities shifted to higher values. Micrometric and colloidal alumina were the precursors which allowed to equilibrate an affordable sintering temperature interval (1200-1300 degrees C) with acceptable densification and resistivity values (around 95% and 5 x 10(-3) Omega cm, respectively). However, colloidal alumina made it possible to obtain slightly lower values of resistivity at the cost of having a narrower working interval.[ES] En este trabajo de investigación se presentan electrodos cerámicos de óxido de zinc dopado con aluminio (AZO) como alternativa a los actuales electrodos de titanio (ITO, FTO. . .) utilizados en el proceso de electrooxidación de aguas residuales para la eliminación de contaminantes refractarios y emergentes. Estos electrodos AZO han sido preparados mediante el método tradicional cerámico, utilizando ZnO como materia prima principal y diferentes precursores de Al2O3 como dopantes. La densificación, la microestructura y la resistividad eléctrica de estos electrodos son propiedades que están directamente relacionadas con la naturaleza del precursor y con eltratamiento térmico utilizado para su sinterización. Cuanto mayor es el número de partículas del precursor y menor es su tamano, ¿ la temperatura de sinterización necesaria para lograr altas densificaciones y bajas resistividades cambia a valores más altos. Fueron la alúmina micrométrica y la coloidal los dopantes que ofrecieron un buen equilibrio entre temperatura de sinterización (1.200¿1.300 ¿C) y densificación-resistividad (95% y 5·10¿3 cm, respectivamente). Concretamente en el caso de la alúmina coloidal, se pudieron optimizar estos resultados estrechando el intervalo de temperatura de trabajo.The authors thanks to"Ministerio de Economia y Competitividad" and "Fondo Europeo de Desarrollo Regional" the support to this research [Plan Nacional de I+D, project Ref. CTQ2015-65202-C2-2-R (MINECO/FEDER)].Sánchez-Rivera, M.; Orts, M.; Pérez-Herranz, V.; Mestre, S. (2021). Effect of type and amount of alumina as dopant over the densification and the electrical properties of zinc oxide ceramic electrodes. Boletín de la Sociedad Española de Cerámica y Vidrio. 60(1):53-61. https://doi.org/10.1016/j.bsecv.2020.01.003536160

CuO improved (Sn,Sb)O2 ceramic anodes for electrochemical advanced oxidation processes

[EN] Antimony¿doped tin oxide electrodes with CuO as sintering aid are presented as an economical alternative to metal¿based electrodes, intended for the electrooxidation process of emerging and recalcitrant organic contaminants in wastewaters. The CuO proportion has been optimized to obtain densified electrodes with a mild thermal cycle (Tmax = 1200°C). One of the manufactured electrodes (97.8 mol.% of SnO2, 1.0 mol.% of Sb2O3, and 1.2 mol.% of CuO) was selected for electrochemical characterization from a physical and morphological analysis. The electrochemical behavior of the selected electrode showed that the addition of CuO as sintering aid widens the electrochemical window and increases the electrode ¿inactivity¿, with respect to an (Sn, Sb)O2 electrode synthesized in the same conditions. In return, the (Sn,Sb,Cu)O2 electrode presents a significantly lower electrochemical rugosity factor. Moreover, the addition of CuO does not change the oxygen evolution reaction mechanism, but it modifies the kinetic parameters, leading to a larger accumulation of hydroxyl radicals. Consequently, the addition of CuO as sintering aid significantly improves the electrochemical properties of the electrode as an electrochemical advanced oxidation process anode with respect to the (Sn,Sb)O2 electrode, at the expense of worsening its electrochemical roughness factor. The results of the electrochemical characterization were confirmed by Norfloxacin degradation tests.The authors are very grateful to the Ministerio de Economia y Competitividad (Projects: CTQ2015-65202-C2-1-R and CTQ2015-65202-C2-2-R) and to the European Regional Development Fund (FEDER), for their economic support.Sánchez-Rivera, M.; Giner-Sanz, JJ.; Pérez-Herranz, V.; Mestre, S. (2019). CuO improved (Sn,Sb)O2 ceramic anodes for electrochemical advanced oxidation processes. 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The exposure of the hybrid detector of the Pierre Auger Observatory

The Pierre Auger Observatory is a detector for ultra-high energy cosmic rays. It consists of a surface array to measure secondary particles at ground level and a fluorescence detector to measure the development of air showers in the atmosphere above the array. The "hybrid" detection mode combines the information from the two subsystems. We describe the determination of the hybrid exposure for events observed by the fluorescence telescopes in coincidence with at least one water-Cherenkov detector of the surface array. A detailed knowledge of the time dependence of the detection operations is crucial for an accurate evaluation of the exposure. We discuss the relevance of monitoring data collected during operations, such as the status of the fluorescence detector, background light and atmospheric conditions, that are used in both simulation and reconstruction.Comment: Paper accepted by Astroparticle Physic

Deep learning-based lesion subtyping and prediction of clinical outcomes in COVID-19 pneumonia using chest CT

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists' severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists' interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists' severity score

Association of a single nucleotide polymorphism combination pattern of the Klotho gene with non-cardiovascular death in patients with chronic kidney disease

Chronic kidney disease (CKD) is associated with an elevated risk of all-cause mortality, with cardiovascular death being extensively investigated. However, non-cardiovascular mortality represents the biggest percentage, showing an evident increase in recent years. Klotho is a gene highly expressed in the kidney, with a clear influence on lifespan. Low levels of Klotho have been linked to CKD progression and adverse outcomes. Single nucleotide polymorphisms (SNPs) of the Klotho gene have been associated with several diseases, but studies investigating the association of Klotho SNPs with noncardiovascular death in CKD populations are lacking. The main aim of this study was to assess whether 11 Klotho SNPs were associated with non-cardiovascular death in a subpopulation of the National Observatory of Atherosclerosis in Nephrology (NEFRONA) study (n ¼ 2185 CKD patients). After 48 months of follow-up, 62 cardiovascular deaths and 108 non-cardiovascular deaths were recorded. We identified a high non-cardiovascular death risk combination of SNPs corresponding to individuals carrying the most frequent allele (G) at rs562020, the rare allele (C) at rs2283368 and homozygotes for the rare allele (G) at rs2320762 (rs562020 GG/AG þ rs2283368 CC/CT þ rs2320762 GG). Among the patients with the three SNPs genotyped (n ¼ 1016), 75 (7.4%) showed this combination. Furthermore, 95 (9.3%) patients showed a low-risk combination carrying all the opposite genotypes (rs562020 AA þ rs2283368 TT þ rs2320762 GT/TT). All the other combinations [n ¼ 846 (83.3%)] were considered as normal risk. Using competing risk regression analysis, we confirmed that the proposed combinations are independently associated with a higher fhazard ratio [HR] 3.28 [confidence interval (CI) 1.51-7.12]g and lower [HR 6 × 10- (95% CI 3.3 × 10--1.1 × 10-)] risk of suffering a non-cardiovascular death in the CKD population of the NEFRONA cohort compared with patients with the normal-risk combination. Determination of three SNPs of the Klotho gene could help in the prediction of non-cardiovascular death in CKD

Association of candidate gene polymorphisms with chronic kidney disease : Results of a case-control analysis in the NEFRONA cohort

Chronic kidney disease (CKD) is a major risk factor for end-stage renal disease, cardiovascular disease and premature death. Despite classical clinical risk factors for CKD and some genetic risk factors have been identified, the residual risk observed in prediction models is still high. Therefore, new risk factors need to be identified in order to better predict the risk of CKD in the population. Here, we analyzed the genetic association of 79 SNPs of proteins associated with mineral metabolism disturbances with CKD in a cohort that includes 2,445 CKD cases and 559 controls. Genotyping was performed with matrix assisted laser desorption ionization-time of flight mass spectrometry. We used logistic regression models considering different genetic inheritance models to assess the association of the SNPs with the prevalence of CKD, adjusting for known risk factors. Eight SNPs (rs1126616, rs35068180, rs2238135, rs1800247, rs385564, rs4236, rs2248359, and rs1564858) were associated with CKD even after adjusting by sex, age and race. A model containing five of these SNPs (rs1126616, rs35068180, rs1800247, rs4236, and rs2248359), diabetes and hypertension showed better performance than models considering only clinical risk factors, significantly increasing the area under the curve of the model without polymorphisms. Furthermore, one of the SNPs (the rs2248359) showed an interaction with hypertension, being the risk genotype affecting only hypertensive patients. We conclude that 5 SNPs related to proteins implicated in mineral metabolism disturbances (Osteopontin, osteocalcin, matrix gla protein, matrix metalloprotease 3 and 24 hydroxylase) are associated to an increased risk of suffering CKD

Cut-offs and response criteria for the Hospital Universitario la Princesa Index (HUPI) and their comparison to widely-used indices of disease activity in rheumatoid arthritis

Objective To estimate cut-off points and to establish response criteria for the Hospital Universitario La Princesa Index (HUPI) in patients with chronic polyarthritis. Methods Two cohorts, one of early arthritis (Princesa Early Arthritis Register Longitudinal PEARL] study) and other of long-term rheumatoid arthritis (Estudio de la Morbilidad y Expresión Clínica de la Artritis Reumatoide EMECAR]) including altogether 1200 patients were used to determine cut-off values for remission, and for low, moderate and high activity through receiver operating curve (ROC) analysis. The areas under ROC (AUC) were compared to those of validated indexes (SDAI, CDAI, DAS28). ROC analysis was also applied to establish minimal and relevant clinical improvement for HUPI. Results The best cut-off points for HUPI are 2, 5 and 9, classifying RA activity as remission if =2, low disease activity if >2 and =5), moderate if >5 and <9 and high if =9. HUPI''s AUC to discriminate between low-moderate activity was 0.909 and between moderate-high activity 0.887. DAS28''s AUCs were 0.887 and 0.846, respectively; both indices had higher accuracy than SDAI (AUCs: 0.832 and 0.756) and CDAI (AUCs: 0.789 and 0.728). HUPI discriminates remission better than DAS28-ESR in early arthritis, but similarly to SDAI. The HUPI cut-off for minimal clinical improvement was established at 2 and for relevant clinical improvement at 4. Response criteria were established based on these cut-off values. Conclusions The cut-offs proposed for HUPI perform adequately in patients with either early or long term arthritis

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch