Nefroprevención en el paciente muy anciano

Nephroprevention consists of a set of measures to attempt to prevent or slow kidney damage. Primary nephroprevention is the term used when such measures seek to reduce the risk of  installing an acute renal failure; and secondary prevention or nephroprotection is used when attempting to slow the progression of chronic renal failure. Regarding nephroprotection, the measures implemented for this purpose in young and very elderly (age>75 years) patients are often similar, based on the modulation of the diet, blood pressure levels, hemoglobin and glycosylated hemoglobin, and the type and dose of medication delivered. However, given that those objectives can induce complications in the very elderly, less strict targets must be sought,while respecting certain well-defined limits.La nefroprevención es un conjunto de medidas destinadas a intentar prevenir o ralentizar el daño renal, soliéndose emplear el término nefroprevención primaria cuando dichas medidas buscan reducir el riesgo de instalación de una insuficiencia renal aguda; y el de prevención secundaria o nefroprotección, cuando pretenden enlentecer la progresión de una insuficiencia renal crónica. Con respecto a la nefroprotección, las medidas implementadas para tal fin, en pacientes jóvenes y muy ancianos (edad >75 años), suelen ser similares, basadas en la modulación de la dieta, cifras de tensión arterial, valores de hemoglobina y hemoglobina glicosilada, así como en el tipo y dosis de medicación suministrada. Sin embargo, dado que dichos objetivos pueden inducir complicaciones en los muy ancianos, deben muchas veces buscarse objetivos más laxos, aunque respetando ciertos límites bien definidos

Gapped dilatons in scale invariant superfluids

We study a paradigmatic model in field theory where a global U(1) and scale symmetries are jointly and spontaneously broken. At zero density the model has a noncompact flat direction, which at finite density needs to be slightly lifted. The resulting low-energy spectrum is composed by a standard gapless U(1) Nambu-Goldstone mode and a light dilaton whose gap is determined by the chemical potential and corrected by the couplings. Even though U(1) and scale symmetries commute, there is a mixing between the U(1) Nambu-Goldstone and the dilaton that is crucial to recover the expected dynamics of a conformal fluid and leads to a phonon propagating at the speed of sound. The results rely solely on an accurate study of the Ward-Takahashi identities and are checked against standard fluctuation computations. We extend our results to a boosted superfluid, and comment the relevance of our findings to condensed matter applicationsR. A. and D. N. acknowledge support by IISNBelgium (convention 4.4503.15) and by the F. R. S.-FNRS under the “Excellence of Science" EOS be.h Project No. 30820817. R. A. is supported as a Research Director of the F. R. S.-FNRS (Belgium). C. H. has been partially supported by the Spanish Ministerio de Ciencia, Innovacion y Universidades Grant No. PGC2018-096894-B-100 and by the Principado de Asturias through Grant No. GRUPIN-IDI/2018/000174S

Biosemiotic medicine: From an effect-based medicine to a process-based medicine

La medicina contemporánea se caracteriza por una creciente subespecialización, así como por la adquisición de un mayor conocimiento respecto de la interacción entre las distintas estructuras del organismo (biosemiótica) tanto en estado de salud como de enfermedad. Se propone, en este artículo, una nueva conceptualización del organismo basada en la perspectiva de considerarlo conformado por un espacio biológico (células, tejidos y órganos) y un espacio biosemiótico (intercambio de señales entre ellos). Su desarrollo daría lugar a una nueva subespecialidad dedicada al estudio e interferencia de la biosemiótica de la enfermedad (medicina biosemiótica), lo que propiciaría el desarrollo de una medicina de procesos, tendiente al diagnóstico y tratamiento temprano de las enfermedades

Predicting key educational outcomes in academic trajectories: a machine-learning approach

Predicting and understanding different key outcomes in a student's academic trajectory such as grade point average, academic retention, and degree completion would allow targeted intervention programs in higher education. Most of the predictive models developed for those key outcomes have been based on traditional methodological approaches. However, these models assume linear relationships between variables and do not always yield accurate predictive classifications. On the other hand, the use of machine-learning approaches such as artificial neural networks has been very effective in the classification of various educational outcomes, overcoming the limitations of traditional methodological approaches. In this study, multilayer perceptron artificial neural network models, with a backpropagation algorithm, were developed to classify levels of grade point average, academic retention, and degree completion outcomes in a sample of 655 students from a private university. Findings showed a high level of accuracy for all the classifications. Among the predictors, learning strategies had the greatest contribution for the prediction of grade point average. Coping strategies were the best predictors for degree completion, and background information had the largest predictive weight for the identification of students who will drop out or not from the university programs.Fil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi". Grupo Vinculado CIIPME - Entre Ríos - Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental "Dr. Horacio J. A. Rimoldi"; ArgentinaFil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Cascallar, Eduardo C.. Katholikie Universiteit Leuven; Bélgic

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.Fil: Rodríguez Hernández, Carlos Felipe. Katholikie Universiteit Leuven; BélgicaFil: Musso, Mariel Fernanda. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Centro Interdisciplinario de Investigaciones en Psicología Matemática y Experimental Dr. Horacio J. A. Rimoldi; ArgentinaFil: Kyndt, Eva. Swinburne University Of Technology; Australia. Universiteit Antwerp; BélgicaFil: Cascallar, Eduardo. Katholikie Universiteit Leuven; Bélgic

Primary prevention for acute kidney injury in ambulatory patients

Acute kidney injury (AKI) is a heterogeneous group of conditions characterized by a sudden decrease in glomerular filtration rate (GFR), which usually induces the accumulation of nitrogenous-waste substances in the blood. It is expressed as an increase in serum creatinine levels (≥ 0.3 mg/dl within 48 hours or ≥1.5 times from baseline within the previous 7 days) or by a urine volume reduction of ˂0.5 ml/kg/h in 6 hours [1]. AKI is a relevant condition since it is usually associated with 1–7% and 30–50% of hospital and intensive care unit (ICU) admissions, respectively; showing a significant morbidity-mortality rate, and progression to chronic kidney disease (CKD) [1–7]. Even though many strategies have been proposed to achieve an early AKI diagnosis (e.g. novel biomarkers, informatics alarms), and an AKI effective treatment (e.g. renal protective drugs, biocompatible renal replacement therapies), both objectives remain unachieved; therefore, AKI prevention is currently the best ‘therapeutic’ strategy for this condition

A Survey on Quantum Computational Finance for Derivatives Pricing and VaR

[Abstract]: We review the state of the art and recent advances in quantum computing applied to derivative pricing and the computation of risk estimators like Value at Risk. After a brief description of the financial derivatives, we first review the main models and numerical techniques employed to assess their value and risk on classical computers. We then describe some of the most popular quantum algorithms for pricing and VaR. Finally, we discuss the main remaining challenges for the quantum algorithms to achieve their potential advantages.Xunta de Galicia; ED431G 2019/01All authors acknowledge the European Project NExt ApplicationS of Quantum Computing (NEASQC), funded by Horizon 2020 Program inside the call H2020-FETFLAG-2020-01 (Grant Agreement 951821). Á. Leitao, A. Manzano and C. Vázquez wish to acknowledge the support received from the Centro de Investigación de Galicia “CITIC”, funded by Xunta de Galicia and the European Union (European Regional Development Fund- Galicia 2014-2020 Program), by Grant ED431G 2019/01