Fibroblast Growth Factor 23 Is an Independent and Specific Predictor of Mortality in Patients With Heart Failure and Reduced Ejection Fraction

BACKGROUND Strategies to improve risk prediction are of major importance in patients with heart failure (HF). Fibroblast growth factor 23 (FGF-23) is an endocrine regulator of phosphate and vitamin D homeostasis associated with an increased cardiovascular risk. We aimed to assess the prognostic effect of FGF-23 on mortality in HF patients with a particular focus on differences between patients with HF with preserved ejection fraction and patients with HF with reduced ejection fraction (HFrEF). METHODS AND RESULTS FGF-23 levels were measured in 980 patients with HF enrolled in the Ludwigshafen Risk and Cardiovascular Health (LURIC) study including 511 patients with HFrEF and 469 patients with HF with preserved ejection fraction and a median follow-up time of 8.6 years. FGF-23 was additionally measured in a second cohort comprising 320 patients with advanced HFrEF. FGF-23 was independently associated with mortality with an adjusted hazard ratio per 1-SD increase of 1.30 (95% confidence interval, 1.14-1.48; P<0.001) in patients with HFrEF, whereas no such association was found in patients with HF with preserved ejection fraction (for interaction, P=0.043). External validation confirmed the significant association with mortality with an adjusted hazard ratio per 1 SD of 1.23 (95% confidence interval, 1.02-1.60; P=0.027). FGF-23 demonstrated an increased discriminatory power for mortality in addition to N-terminal pro-B-type natriuretic peptide (C-statistic: 0.59 versus 0.63) and an improvement in net reclassification index (39.6%; P<0.001). CONCLUSIONS FGF-23 is independently associated with an increased risk of mortality in patients with HFrEF but not in those with HF with preserved ejection fraction, suggesting a different pathophysiologic role for both entities.

Phenotypic plasticity is a negative, though weak, predictor of the commonness of 105 grassland species

Aim The usual hypothesis about the relationship between niche breadth and range size posits that species with the capacity to use a wider range of resources or to tolerate a greater range of environmental conditions should be more widespread. In plants, broader niches are often hypothesized to be due to pronounced phenotypic plasticity, and more plastic species are therefore predicted to be more common. We examined the relationship between the magnitude of phenotypic plasticity in five functional traits, mainly related to leaves, and several measures of abundance in 105 Central European grassland species. We further tested whether mean values of traits, rather than their plasticity, better explain the commonness of species, possibly because they are pre-adapted to exploiting the most common resources. Location Central Europe. Methods In a multispecies experiment with 105 species we measured leaf thickness, leaf greenness, specific leaf area, leaf dry matter content and plant height, and the plasticity of these traits in response to fertilization, waterlogging and shading. For the same species we also obtained five measures of commonness, ranging from plot-level abundance to range size in Europe. We then examined whether these measures of commonness were associated with the magnitude of phenotypic plasticity, expressed as composite plasticity of all traits across the experimental treatments. We further estimated the relative importance of trait plasticity and trait means for abundance and geographical range size. Results More abundant species were less plastic. This negative relationship was fairly consistent across several spatial scales of commonness, but it was weak. Indeed, compared with trait means, plasticity was relatively unimportant for explaining differences in species commonness. Main conclusions Our results do not indicate that larger phenotypic plasticity of leaf morphological traits enhances species abundance. Furthermore, possession of a particular trait value, rather than of trait plasticity, is a more important determinant of species commonness.

Locus of control as a predictor of students dropping out of high school

Education is related to health. In cross-sectional data, education level has been associated with physical functioning. Also, lower levels of education have been associated with health behaviors including smoking, alcohol use, and greater body weight. In school, students may benefit from greater exposed to health-related messages, while students who have dropped out may be more susceptible to influences regarding negative health behaviors such as smoking. ^ Improved school retention might improve long-term health outcomes. However, there is limited evidence regarding modifiable factors that predict likelihood of dropping out. Two likely psychosocial measures are locus of control and parent-child academic conversations. In the current study, data from two waves of a population-based longitudinal survey, the National Education Longitudinal Survey, were utilized to evaluate whether these two psychosocial measures could predict likelihood of dropping out, for students (n = 16,749) in tenth grade at 1990, with dropout status determined at 1992, while controlling for recognized sociodemographic predictors including parental income, parental education level, race/ethnicity, and sex. Locus of control was measured with the Pearlin Mastery Scale, and parent-child academic conversations were measured by three questions concerning course selection at school, school activities and events, and things the student studied in class. ^ In a logistic regression model, with the sociodemographic control measures entered in a first step before entry of the psychosocial measures in a second step, this study determined that lower levels of locus of control were associated with greater likelihood of dropping out after two years (odds ratio (OR) = 1.11, 95% confidence interval (CI) 108 to 1.15, p < .001), and two of the three parent-child academic discussion items were associated with greater likelihood of dropping out after two years (OR = 1.69, CI 1.48-1.93, p < .001; OR = 1.22, CI 1.05-1.41, p = .01; OR = 1.01, CI .88-1.15, p = .94). ^ It is possible that interventions aimed at improving locus of control, and aimed at building parent-child academic conversations, could lower the likelihood of students dropping out, and this in turn could yield improved heath behaviors and health status in the child's future. ^

Baseline quality of life as a predictor of exercise intervention adherence among endometrial cancer survivors

Background: The objective of this analysis is to test whether baseline quality of life (QOL) measurements, body mass index (BMI) and prior exercise behavior are significantly associated with (1) telephone counseling adherence, and (2) activity at the final assessment, in a physical activity promoting intervention among endometrial cancer survivors.^ Methods: One hundred endometrial cancer survivors not currently meeting physical activity guidelines completed baseline QOL and anthropometric assessments to measure general physical and mental health [Medical Outcomes Survey (SF-36)], sleep patterns and sleep quality [Pittsburgh Sleep Quality Index (PSQI)], perceived stress [Perceived Stress Scale (PSS)], cancer-specific concerns of long-term survivors [Quality of Life in Adult Cancer Survivors (QLACS)], and psychological distress [Brief Symptom Inventory-18 (BSI-18)]. Survivors were counseled by telephone during the 6-month intervention and their completion rate determined their adherence. The primary variables of interest included age, baseline BMI, baseline activity level, time since diagnosis, education, treatment received, and the SF-36 physical and mental component scores.^ Results: Final activity was most closely linked with baseline activity (p<.001) and less invasive surgery, being leaner and older, and experiencing less pain and more vitality. Telephone counseling was also predicted well by baseline activity, working less and having better overall cancer-related functioning.^ Conclusion: Above and beyond the QOL measures, baseline activity was the strongest predictor of both final activity and telephone counseling adherence. While education, surgery treatment type and bodily pain were important predictors for final exercise and employment status and cancer-related quality of life were important predictors for telephone counseling adherence, considering adaptive exercise interventions that focus heavily on engaging inactive participants may be a way to produce better exercise-related outcomes in the endometrial cancer survivor population.^

Subjective well -being as predictor of mortality, heart disease, and obesity: Prospective evidence from the Alameda County Study

Based on the World Health Organization's (1965) definition of health, understanding of health requires understanding of positive psychological states. Subjective Well-being (SWB) is a major indicator of positive psychological states. Up to date, most studies of SWB have been focused on its distributions and determinants. However, study of its consequences, especially health consequences, is lacking. This dissertation research examined Subjective Well-being, as operationally defined by constructs drawn from the framework of Positive Psychology, and its sub-scores (Positive Feelings and Negative Feelings) as predictors of three major health outcomes—mortality, heart disease, and obesity. The research used prospective data from the Alameda County Study over 29 years (1965–1994), based on a stratified, randomized, representative sample of the general public in Alameda County, California (Baseline N = 6928). ^ Multivariate analyses (Survival analyses using sequential Cox Proportional Hazard models in the cases of mortality and heart disease, and sequential Logistic Regression analyses in the case of obesity) were performed as the main methods to evaluate the associations of the predictors and the health outcomes. The results revealed that SWB reduced risks of all-cause mortality, natural-cause mortality, and cardiovascular mortality. Positive feelings not only had an even stronger protective effect against all-cause, natural-cause and cardiovascular mortality, but also predicted decreased unnatural-cause mortality which includes deaths from suicide, homicide, accidents, mental disorders, drug dependency, as well as alcohol-related liver diseases. These effects were significant even after adjusted for age, gender, education, and various physical health measures, and, in the case of cardiovascular mortality, obesity and health practices (alcohol consumption, smoking, and physical activities). However, these two positive psychological indicators, SWB and positive feelings, did not predict obesity. And negative feelings had no significant effect on any of the health outcomes evaluated, i.e., all-cause mortality, natural- and unnatural-cause mortality, cardiovascular mortality, or obesity, after covariates were controlled. These findings were discussed (1) in comparison with relevant existing studies, (2) in terms of their implications in health research and promotion, (3) in terms of the independence of positive and negative feelings, and (4) from a Positive Psychology perspective and its significance in Public Health research and practice. ^

Species Diversity is a Good Predictor of Prairie Plant Persistence in Restorations

Prairie restoration projects sometimes fail because of heavy invasion by invasive weeds, especially if they are not intensively managed. Few restoration projects are sampled after the first few years post-establishment, and little is known about what predictors are significant in maintaining restored communities over the very long term. Here, we stopped weeding experimental restoration plots to determine if persistence (that is, remaining unchanged after weeds are allowed to invade) of native prairie in western Iowa was related to planted species diversity

Estudio mecánico sobre la resistencia de la pared de la aorta humana como predictor de riesgo de rotura de aneurisma

Definir el riesgo de rotura de un aneurisma, se considera un factor básico para elegir el momento adecuado de la intervención quirúrgica. Uno de los parámetros clásico utilizados es el diámetro del aneurisma (Ley de la Place). Sin embargo, otro factor mecánico como es el cociente entre la tensión máxima que soporta la pared (depende del diámetro de la aorta y de la morfología) y de la resistencia del material (pared de la aorta) es un elemento poco conocido. La razón estriba en comparar aortas “sanas” con “patológicas”. Este estudio lo hemos realizado en colaboración con el Departamento de Ciencias de Materiales de la Universidad Politécnica de Madrid

Predictor paramétrico de glucosa subcutánea

La diabetes mellitus es una enfermedad que se caracteriza por la nula o insuficiente producción de insulina, o la resistencia del organismo a la misma. La insulina es una hormona que ayuda a que la glucosa (por ejemplo la obtenida a partir de los alimentos ingeridos) llegue a los tejidos periféricos y al sistema nervioso para suministrar energía. Hoy en día la tecnología actual permite abordar el desarrollo del llamado “páncreas endocrino artificial”, que consta de un sensor continuo de glucosa subcutánea, una bomba de infusión subcutánea de insulina y un algoritmo de control en lazo cerrado que calcule la dosis de insulina requerida por el paciente en cada momento, según la medida de glucosa obtenida por el sensor y según unos objetivos. El mayor problema que presentan los sistemas de control en lazo cerrado son los retardos, el sensor de glucosa subcutánea mide la glucosa del líquido intersticial, que representa la que hubo en la sangre un tiempo atrás, por tanto, un cambio en los niveles de glucosa en la sangre, debidos por ejemplo, a una ingesta, tardaría un tiempo en ser detectado por el sensor. Además, una dosis de insulina suministrada al paciente, tarda un tiempo aproximado de 20-30 minutos para la llegar a la sangre. Para evitar trabajar en la medida que sea posible con estos retardos, se intenta predecir cuál será el nivel de glucosa en un futuro próximo, para ello se utilizara un predictor de glucosa subcutánea, con la información disponible de glucosa e insulina. El objetivo del proyecto es diseñar una metodología para estimar el valor futuro de los niveles de glucosa obtenida a partir de un sensor subcutáneo, basada en la identificación recursiva del sistema glucorregulatorio a través de modelos lineales y determinando un horizonte de predicción óptimo de trabajo y analizando la influencia de la insulina en los resultados de la predicción. Se ha implementado un predictor paramétrico basado en un modelo autorregresivo ARX que predice con mejor precisión y con menor RMSE que un predictor ZOH a un horizonte de predicción de treinta minutos. Utilizar información relativa a la insulina no tiene efecto en la predicción. El preprocesado, postprocesado y el tratamiento de la estabilidad tienen un efecto muy beneficioso en la predicción. Diabetes mellitusis a group of metabolic diseases in which a person has high blood sugar, either because the body does not produce enough insulin, or because cells do not respond to the insulin produced. The insulin is a hormone that helps the glucose to reach to outlying tissues and the nervous system to supply energy. Nowadays, the actual technology allows raising the development of the “artificial endocrine pancreas”. It involves a continuous glucose sensor, an insulin bump, and a full closed loop algorithm that calculate the insulin units required by patient at any time, according to the glucose measure obtained by the sensor and any target. The main problem of the full closed loop systems is the delays, the glucose sensor measures the glucose in the interstitial fluid that represents the glucose was in the blood some time ago. Because of this, a change in the glucose in blood would take some time to be detected by the sensor. In addition, insulin units administered by a patient take about 20-30 minutes to reach the blood stream. In order to avoid this effect, it will try to predict the glucose level in the near future. To do that, a subcutaneous glucose predictor is used to predict the future glucose with the information about insulin and glucose. The goal of the proyect is to design a method in order to estimate the future valor of glucose obtained by a subcutaneous sensor. It is based on the recursive identification of the regulatory system through the linear models, determining optimal prediction horizon and analyzing the influence of insuline on the prediction results. A parametric predictor based in ARX autoregressive model predicts with better precision and with lesser RMSE than ZOH predictor in a thirty minutes prediction horizon. Using the relative insulin information has no effect in the prediction. The preprocessing, the postprocessing and the stability treatment have many advantages in the prediction.

Adams methods: implementation as predictor-corrector methods

In this paper, Adams explicit and implicit formulas are obtained in a simple way and a relationship between them is established, allowing for their joint implementation as predictor-corrector methods. It is shown the purposefulness, from a didactic point of view, of Excel spreadsheets for calculations and for the orderly presentation of results in the application of Adams methods to solving initial value problems in ordinary differential equations.

Simulator of the JET real-time disruption predictor

A disruption predictor based on support vector machines (SVM) has been developed to be used in JET. The training process uses thousands of discharges and, therefore, high performance computing has been necessary to obtain the models. To this respect, several models have been generated with data from different JET campaigns. In addition, various kernels (mainly linear and RBF) and parameters have been tested. The main objective of this work has been the implementation of the predictor model under real-time constraints. A “C-code” software application has been developed to simulate the real-time behavior of the predictor. The application reads the signals from the JET database and simulates the real-time data processing, in particular, the specific data hold method to be developed when reading data from the JET ATM real time network. The simulator is fully configurable by means of text files to select models, signal thresholds, sampling rates, etc. Results with data between campaigns C23and C28 will be shown.

Results of the JET real-time disruption predictor in the ITER-like wall campaigns

The impact of disruptions in JET became even more important with the replacement of the previous Carbon Fiber Composite (CFC) wall with a more fragile full metal ITER-like wall (ILW). The development of robust disruption mitigation systems is crucial for JET (and also for ITER). Moreover, a reliable real-time (RT) disruption predictor is a pre-requisite to any mitigation method. The Advance Predictor Of DISruptions (APODIS) has been installed in the JET Real-Time Data Network (RTDN) for the RT recognition of disruptions. The predictor operates with the new ILW but it has been trained only with discharges belonging to campaigns with the CFC wall. 7 realtime signals are used to characterize the plasma status (disruptive or non-disruptive) at regular intervals of 1 ms. After the first 3 JET ILW campaigns (991 discharges), the success rate of the predictor is 98.36% (alarms are triggered in average 426 ms before the disruptions). The false alarm and missed alarm rates are 0.92% and 1.64%.

Implementation of the disruption predictor APODIS in JET Real Time Network using the MARTe framework

Disruptions in tokamaks devices are unavoidable, and they can have a significant impact on machine integrity. So it is very important have mechanisms to predict this phenomenon. Disruption prediction is a very complex task, not only because it is a multi-dimensional problem, but also because in order to be effective, it has to detect well in advance the actual disruptive event, in order to be able to use successful mitigation strategies. With these constraints in mind a real-time disruption predictor has been developed to be used in JET tokamak. The predictor has been designed to run in the Multithreaded Application Real-Time executor (MARTe) framework. The predictor ?Advanced Predictor Of DISruptions? (APODIS) is based on Support Vector Machine (SVM).

La energía disipada en membranas de pericardio sometidas a fatiga uniaxial como predictor de su durabilidad.

El pericardio es un material que se utiliza cuando se hace necesaria la sustitución de los velos de las válvulas cardiacas. En el presente trabajo se evalúa la durabilidad en fatiga de membranas de pericardio de ternera tratadas con glutaraldehído. Con tal propósito, se ensayaron 72 probetas de pericardio en condiciones fisiológicas de humedad y temperatura. Los ensayos se realizaron primero a fatiga hasta un número determinado de ciclos, entre un mínimo de 100 y un máximo de 4000, para luego ensayarse hasta rotura mediante un ensayo uniaxial de tracción simple. Las probetas consideradas control se sometieron a un único ensayo uniaxial de tracción. Se ha comprobado que la energía disipada en los primeros ciclos de las probetas que rompieron prematuramente (antes de finalizar el ciclado) es significativamente mayor que la energía disipada en las probetas que resistieron todos los ciclos de carga y descarga.