Diuretic Resistance in Cardio-Nephrology: Role of Pharmacokinetics, Hypochloremia, and Kidney Remodeling

Background: Diuretic resistance is among the most challenging problems that the cardio-nephrologist must address in daily clinical practice, with a considerable burden on hospital admissions and health care costs. Indeed, loop diuretics are the first-line therapy to overcome fluid overload in heart failure patients. The pathophysiological mechanisms of fluid and sodium retention are complex and depend on several neuro-hormonal signals mainly acting on sodium reabsorption along the renal tubule. Consequently, doses and administration modalities of diuretics must be carefully tailored to patients in order to overcome under- or overtreatment. The frequent and tricky development of diuretic resistance depends in part on post-diuretic sodium retention, reduced tubular secretion of the drug, and reduced sodium/chloride sensing. Sodium and chloride depletions have been recently shown to be major factors mediating these processes. Aquaretics and high-saline infusions have been recently suggested in cases of hyponatremic conditions. This review discusses the limitations and strengths of these approaches. Summary: Long-term diuretic use may lead to diuretic resistance in cardio-renal syndromes. To overcome this complication intravenous administration of loop diuretics and a combination of different diuretic classes have been proposed. In the presence of hyponatremia, high-saline solutions in addition to loop diuretics might be beneficial, whereas aquaretics require caution to avoid overcorrection. Key Messages: Diuretic resistance is a central theme for cardio-renal syndromes. Hyponatremia and hypochloremia may be part of the mechanisms for diuretic resistance. Aquaretics and high-saline solutions have been proposed as possible new therapeutic solutions

Level of hydration and renal function in healthy humans

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Assessing the impact of driving behavior on instantaneous fuel consumption

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Despite the recent technological improvements in vehicles and engines, and the introduction of better fuels, road transportation is still responsible for air pollution in urban areas due to the increasing number of circulating vehicles, and their relative travelled distances. We develop a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables such as Engine Fuel Rate, Speed, Mass Air Flow, Absolute Load, and Manifold Absolute Pressure, all of them obtained from the vehicle’s Electronic Control Unit (ECU). Our platform is able to assist drivers in correcting their bad driving habits, while offering helpful recommendations to improve fuel economy. In this paper we will demonstrate through data mining, to what extent does the driving style really affect (negatively or positively) the fuel consumption, as well as the increase or reduction of greenhouse gas emissions generated by vehicles.This work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01Meseguer Anastasio, JE.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2015). Assessing the impact of driving behavior on instantaneous fuel consumption. IEEE. https://doi.org/10.1109/CCNC.2015.7158016

DrivingStyles: a smartphone application to assess driver behavior

©2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The DrivingStyles architecture integrates both data mining techniques and neural networks to generate a classification of driving styles by analyzing the driver behavior along each route. In particular, based on parameters such as speed, acceleration, and revolutions per minute of the engine (rpm), we have implemented a neural network based algorithm that is able to characterize the type of road on which the vehicle is moving, as well as the degree of aggressiveness of each driver. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. In this work we take advantage of two key-points: the evolution of mobile terminals and the availability of a standard interface to access car data. Our DrivingStyles platform to achieve a symbiosis between smartphones and vehicles able to make the former operate as an onboard unit. Results show that neural networks were able to achieve a high degree of exactitude at classifying both road and driver types based on user traces. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 1550 downloads from different countries in just a few months.This work has been sponsored by the Universitat Politécnica de Valencia through the ABATIS project (PAlD-05-12), and by the Ministry of Science and Innovation through the Walkie-Talkie project (TIN201 1-27543-C03).Meseguer Anastasio, JE.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2013). DrivingStyles: a smartphone application to assess driver behavior. IEEE. https://doi.org/10.1109/ISCC.2013.6755001

DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization

[EN] Intelligent transportation systems (ITS) rely on connected vehicle applications to address real-world problems. Research is currently being conducted to support safety, mobility and environmental applications. This paper presents the DrivingStyles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles and fuel consumption based on driver characterization. In particular, we have implemented an algorithm that is able to characterize the degree of aggressiveness of each driver. We have also developed a methodology to calculate, in real-time, the consumption and environmental impact of spark ignition and diesel vehicles from a set of variables obtained from the vehicle's electronic control unit (ECU). In this paper, we demonstrate the impact of the driving style on fuel consumption, as well as its correlation with the greenhouse gas emissions generated by each vehicle. Overall, our platform is able to assist drivers in correcting their bad driving habits, while offering helpful tips to improve fuel economy and driving safety.This work was partially supported by the Ministerio de Economía y Competitividad, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R.Meseguer Anastasio, JE.; Toh, CK.; Tavares De Araujo Cesariny Calafate, CM.; Cano, J.; Manzoni, P. (2017). DrivingStyles: A Mobile Platform for Driving Styles and Fuel Consumption Characterization. Journal of Communications and Networks. 19(2):162-168. doi:10.1109/JCN.2017.000025S16216819

Age-Related Impairment in Insulin Release The Essential Role of beta(2)-Adrenergic Receptor

In this study, we investigated the significance of β(2)-adrenergic receptor (β(2)AR) in age-related impaired insulin secretion and glucose homeostasis. We characterized the metabolic phenotype of β(2)AR-null C57Bl/6N mice (β(2)AR(-/-)) by performing in vivo and ex vivo experiments. In vitro assays in cultured INS-1E β-cells were carried out in order to clarify the mechanism by which β(2)AR deficiency affects glucose metabolism. Adult β(2)AR(-/-) mice featured glucose intolerance, and pancreatic islets isolated from these animals displayed impaired glucose-induced insulin release, accompanied by reduced expression of peroxisome proliferator-activated receptor (PPAR)γ, pancreatic duodenal homeobox-1 (PDX-1), and GLUT2. Adenovirus-mediated gene transfer of human β(2)AR rescued these defects. Consistent effects were evoked in vitro both upon β(2)AR knockdown and pharmacologic treatment. Interestingly, with aging, wild-type (β(2)AR(+/+)) littermates developed impaired insulin secretion and glucose tolerance. Moreover, islets from 20-month-old β(2)AR(+/+) mice exhibited reduced density of β(2)AR compared with those from younger animals, paralleled by decreased levels of PPARγ, PDX-1, and GLUT2. Overexpression of β(2)AR in aged mice rescued glucose intolerance and insulin release both in vivo and ex vivo, restoring PPARγ/PDX-1/GLUT2 levels. Our data indicate that reduced β(2)AR expression contributes to the age-related decline of glucose tolerance in mice

Age-related impairment in insulin release: the essential role of ϐ(2)-adrenergic receptor.

In this study, we investigated the significance of ϐ (2)-adrenergic receptor (ϐ (2)AR) in age-related impaired insulin secretion and glucose homeostasis. We characterized the metabolic phenotype of ϐ (2)AR-null C57Bl/6N mice (ϐ (2)AR(-/-)) by performing in vivo and ex vivo experiments. In vitro assays in cultured INS-1E ϐ-cells were carried out in order to clarify the mechanism by which ϐ (2)AR deficiency affects glucose metabolism. Adult ϐ (2)AR(-/-) mice featured glucose intolerance, and pancreatic islets isolated from these animals displayed impaired glucose-induced insulin release, accompanied by reduced expression of peroxisome proliferator-activated receptor (PPAR) γ, pancreatic duodenal homeobox-1 (PDX-1), and GLUT2. Adenovirus-mediated gene transfer of human ϐ (2)AR rescued these defects. Consistent effects were evoked in vitro both upon ϐ (2)AR knockdown and pharmacologic treatment. Interestingly, with aging, wild-type (ϐ (2)AR(+/+)) littermates developed impaired insulin secretion and glucose tolerance. Moreover, islets from 20-month-old ϐ (2)AR(+/+) mice exhibited reduced density of ϐ (2)AR compared with those from younger animals, paralleled by decreased levels of PPARγ, PDX-1, and GLUT2. Overexpression of ϐ (2)AR in aged mice rescued glucose intolerance and insulin release both in vivo and ex vivo, restoring PPARγ/PDX-1/GLUT2 levels. Our data indicate that reduced ϐ (2)AR expression contributes to the age-related decline of glucose tolerance in mice

Genetically-Driven Enhancement of Dopaminergic Transmission Affects Moral Acceptability in Females but Not in Males: A Pilot Study

Moral behavior has been a key topic of debate for philosophy and psychology for a long time. In recent years, thanks to the development of novel methodologies in cognitive sciences, the question of how we make moral choices has expanded to the study of neurobiological correlates that subtend the mental processes involved in moral behavior. For instance, in vivo brain imaging studies have shown that distinct patterns of brain neural activity, associated with emotional response and cognitive processes, are involved in moral judgment. Moreover, while it is well-known that responses to the same moral dilemmas differ across individuals, to what extent this variability may be rooted in genetics still remains to be understood. As dopamine is a key modulator of neural processes underlying executive functions, we questioned whether genetic polymorphisms associated with decision-making and dopaminergic neurotransmission modulation would contribute to the observed variability in moral judgment. To this aim, we genotyped five genetic variants of the dopaminergic pathway [rs1800955 in the dopamine receptor D4 (DRD4) gene, DRD4 48 bp variable number of tandem repeat (VNTR), solute carrier family 6 member 3 (SLC6A3) 40 bp VNTR, rs4680 in the catechol-O-methyl transferase (COMT) gene, and rs1800497 in the ankyrin repeat and kinase domain containing 1 (ANKK1) gene] in 200 subjects, who were requested to answer 56 moral dilemmas. As these variants are all located in genes belonging to the dopaminergic pathway, they were combined in multilocus genetic profiles for the association analysis. While no individual variant showed any significant effects on moral dilemma responses, the multilocus genetic profile analysis revealed a significant gender-specific influence on human moral acceptability. Specifically, those genotype combinations that improve dopaminergic signaling selectively increased moral acceptability in females, by making their responses to moral dilemmas more similar to those provided by males. As females usually give more emotionally-based answers and engage the “emotional brain” more than males, our results, though preliminary and therefore in need of replication in independent samples, suggest that this increase in dopamine availability enhances the cognitive and reduces the emotional components of moral decision-making in females, thus favoring a more rationally-driven decision process

Characterizing the driving style behavior using artificial intelligence techniques

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works[EN] The On Board Diagnosis (OBD-II) standard allows accessing the vehicles’ Electronic Control Unit (ECU) easily through a Bluetooth OBD-II connector. This paper presents the DrivingStyles architecture, which adopts data mining techniques and neural networks to analyze and generate a classification of driving styles by analysing the characteristics of the driver along the route followed. The final goal is to assist drivers at correcting the bad habits in their driving behavior, while offering helpful tips to improve fuel economy. Since it is well known that smart driving can lead to a lower fuel consumption, the environmental impact is also reduced. A study involving more than 180 users is being carried out, where their real time traces (with different traffic conditions) is sent periodically to the platform. DrivingStyles is currently available on the Google Play Store platform for free download, and has achieved more than 2800 downloads from different countries in just a few monthsThis work was partially supported by the Ministerio de Ciencia e Innovación, Spain, under Grant TIN2011-27543-C03-01, and by the Universitat Politècnica de València through the ABATIS project (PAID-05-12).Meseguer Anastasio, JE.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2013). Characterizing the driving style behavior using artificial intelligence techniques. IEEE. http://hdl.handle.net/10251/67314