Influence of heart rate in non-linear HRV indices as a sampling rate effect evaluated on supine and standing

The purpose of this study is to characterize and attenuate the influence of mean heart rate (HR) on nonlinear heart rate variability (HRV) indices (correlation dimension, sample, and approximate entropy) as a consequence of being the HR the intrinsic sampling rate of HRV signal. This influence can notably alter nonlinear HRV indices and lead to biased information regarding autonomic nervous system (ANS) modulation. First, a simulation study was carried out to characterize the dependence of nonlinear HRV indices on HR assuming similar ANS modulation. Second, two HR-correction approaches were proposed: one based on regression formulas and another one based on interpolating RR time series. Finally, standard and HR-corrected HRV indices were studied in a body position change database. The simulation study showed the HR-dependence of non-linear indices as a sampling rate effect, as well as the ability of the proposed HR-corrections to attenuate mean HR influence. Analysis in a body position changes database shows that correlation dimension was reduced around 21% in median values in standing with respect to supine position (p < 0.05), concomitant with a 28% increase in mean HR (p < 0.05). After HR-correction, correlation dimension decreased around 18% in standing with respect to supine position, being the decrease still significant. Sample and approximate entropy showed similar trends. HR-corrected nonlinear HRV indices could represent an improvement in their applicability as markers of ANS modulation when mean HR changes

Quantification of Linear and Nonlinear Cardiorespiratory Interactions under Autonomic Nervous System Blockade

This paper proposes a methodology to extract both linear and nonlinear respiratory influences from the heart rate variability (HRV), by decomposing the HRV into a respiratory and a residual component. This methodology is based on least-squares support vector machines (LS-SVM) formulated for nonlinear function estimation. From this decomposition, a better estimation of the respiratory sinus arrhythmia (RSA) and the sympathovagal balance (SB) can be achieved. These estimates are first analyzed during autonomic blockade and an orthostatic maneuver, and then compared against the classical HRV and a model that considers only linear interactions. Results are evaluated using surrogate data analysis and they indicate that the classical HRV and the linear model underestimate the cardiorespiratory interactions. Moreover, the linear and nonlinear interactions appear to be mediated by different control mechanisms. These findings will allow to better assess the ANS and to improve the understanding of the interactions within the cardiorespiratory system

An ICP-MS-based platform for release studies on silver-based nanomaterials

Engineered nanoparticles are being incorporated into different products and nanocomposites. The release of these nanoparticles, as well as other derived species, can subsequently lead to consumer and environmental exposure, being a relevant factor for risk assessment. The need for analytical methods for the detection, characterization and quantitation of these released species under relevant conditions becomes evident. In this work, a platform of methods based on the use of inductively coupled plasma mass spectrometry (ICP-MS) is proposed to obtain information about the release of silver from silver based nanocoatings and nanocomposites. The sensitivity and element specific response of conventional ICP-MS is complemented by the use of the technique in single particle mode and in combination with ultrafiltration and asymmetrical flow field flow fractionation. By using these three methods, information about the release of both dissolved and particulate forms of silver, as well as the size of the nanoparticles, can be obtained under a variety of scenarios at concentrations down to 0.1 µg L-1 and a nanoparticle diameter of 5 nm. The feasibility of the platform was checked through a number of paradigmatic cases

About detectability and limits of detection in single particle inductively coupled plasma mass spectrometry

Single particle inductively coupled plasma mass spectrometry (SP-ICP-MS) offers unique features for the detection of particles, as well as for their quantification and size characterization. The detection capabilities of SP-ICP-MS are therefore not only limited to the concentration domains (of particles and dissolved related species), but also to the mass of element per particle and particle size domains. Discrimination and detection of particle events, based on the use of robust limits of decision (also known as critical values), and the estimation of the limits of detection in the different domains, require standardized metrological approaches that have not been clearly established yet. As a consequence, harmonized approaches and expressions to allow reliable comparisons between methods and instruments, as well as to process SP-ICP-MS data, are required. This paper is an attempt to summarize and review the different approaches applied up to now in relation to the detectability in SP-ICP-MS, and highlight the peculiarities of this topic in SP-ICP-MS. A holistic approach with criteria and expressions for the estimation of the different critical values and limits of detection in terms of the different instrumental and experimental parameters involved is proposed. Additionally, a calculation tool for estimating and predicting critical values and limits of detection under different experimental conditions is also included

Effect of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions during Autonomic Nervous System Blockade

The Heart Rate Variability (HRV) is a noninvasive tool to evaluate the activity of the autonomic nervous system. To study the HRV, different mathematical representations can be used. The selection of a representation might have an effect on the evaluation of the mechanisms that modulate the Heart Rate (HR). One of these mechanisms is the Respiratory Sinus Arrhythmia (RSA), i.e. an increased HR during inhalation and a decreased HR during exhalation. Different methods exist to quantify the RSA. A common approach is to calculate the power in the High Frequency (HF, 0.15 - 0.4 Hz) band of the spectrum of the HRV representation. More recently proposed methods use the respiratory signals to estimate the strength of the RSA.This paper studies the effect of the HRV representations on the quantification of the RSA. To this end, an experiment is used in which the sympathetic and parasympathetic branches of the autonomic nervous system are selectively blocked. Three different HRV representations are considered. Afterwards, the strength of the RSA is estimated using three approaches, namely the spectral content in the HF band of the HRV representations, orthogonal subspace projections and a time-frequency representation.The results suggest that the selection of an HRV representation does not have a significant impact on the RSA estimates in a healthy population

Non-linear analysis of heart rate variability and its application to predict hypotension during spinal anesthesia for cesarean delivery

A non-linear analysis of heart rate variability is carried out through two complexity measures (Correlation Dimension and Pointwise Correlation Dimension) and two regularity measures (Approximate Entropy and Sample Entropy) in order to predict hypotension episodes occurred during spinal anesthesia in cesarean delivery. These methods are applied to RR-interval series, during which woman adopts two alternative positions, one physiologically relaxed (PR) and one physiologically stressed (PS). Results show that women who developed hypotension have significantly higher (p-value = 0.05) complexity measures at PR position, (and significantly lower values for the PS position), than those who did not developed the disease. Regarding the regularity measures, women who developed hypotension have lower values, but not arriving to significance, during PS position than those who did not developed it, whereas those values remain almost constant for PR position

The role of regions in global value chains: an analysis for the European Union

While considerable attention has been directed to the national-level impacts of global value chains, far less attention has been focused on the way in which global production fragmentation has affected regional economies. Using some measures derived from a multiregional, multisectoral input–output model, this paper analyzes the position and share of EU regions in Global Value Chains (GVC). The spatial determinants of these two dimensions are explored using spatial econometric methods to capture the influence of neighboring regions on these outcomes. Empirically, the focus is on a set of NUTS2 European regions for the most recent year (2010) of the EUREGIO database. Our results confirm the hypothesis of spatial dependence between regions conditioning the engagement and position GVCs, suggesting that global production processes are influenced by regional and local factors. In particular, spatial spillover effects play a significant role conditioned by both geographical proximity and similarity of production structures. The results show that sharing certain characteristics, some of them associated to their degree of proximity and the neighbouring situation of regions condition their specialization, participation and positioning in GVC, generating some important insights informative for the formulation of regional development policies

Europeanization vs. Globalization? A deeper look into income and employment embodied in intra-European trade

Production processes are nowadays increasingly global, implying interdependent structures linking goods, processes and countries. Traditional economic blocks and sectoral intra-country linkages coexist with increasing worldwide dependencies. Recent literature supports the hypothesis of a new globalization process taking place in the late 1990s and the 21st century, centred on the consolidation of increasingly competitive macro-regions at a global level, with a growing specialization of countries within them. We propose a multiregional input-output (MRIO) model of the European Union (EU) to analyse whether the generation of employment and income in Europe in recent decades can be defined as a process that is mainly regional or global (involving countries within the region versus countries outside Europe). Our results show that intra-EU trade is an important factor contributing to income and employment growth, more oriented to intermediate inputs, in the same way as extra-EU trade, despite the fact that some European countries are more specialized in final goods, mainly driven by high-income EU countries. Los procesos de producción son hoy en día cada vez más globales, lo que implica estructuras interdependientes que vinculan bienes, procesos y países. Los bloques económicos tradicionales y los vínculos sectoriales dentro del país coexisten con dependencias mundiales crecientes. La literatura reciente respalda la hipótesis de un nuevo proceso de globalización que tuvo lugar a fines de los años 90 y el siglo XXI, centrado en la consolidación de macro-regiones cada vez más competitivas a nivel global, con una creciente especialización de los países dentro de ellas. Proponemos un modelo multirregional input-output (MRIO) de la Unión Europea (UE) para analizar si la generación de empleo e ingresos en Europa en las últimas décadas se puede definir como un proceso que es principalmente regional o global. Nuestros resultados muestran que el comercio intracomunitario es un factor importante que contribuye al crecimiento de los ingresos y el empleo, más orientado a los inputs intermedios, de la misma manera que el comercio extracomunitario, a pesar del hecho de que algunos países europeos están más especializados en productos finales, principalmente demandados por países de altos ingresos de la UE

An Update on Autophagy in Prion Diseases

Autophagy is a dynamic intracellular mechanism involved in protein and organelle turnover through lysosomal degradation. When properly regulated, autophagy supports normal cellular and developmental processes, whereas defects in autophagic degradation have been associated with several pathologies, including prion diseases. Prion diseases, or transmissible spongiform encephalopathies (TSE), are a group of fatal neurodegenerative disorders characterized by the accumulation of the pathological misfolded isoform (PrPSc) of the physiological cellular prion protein (PrPc) in the central nervous system. Autophagic vacuoles have been described in experimental models of TSE and in the natural disease in humans. The precise connection of this process with prion-related neuropathology, or even whether autophagy is completely beneficial or pathogenic during neurodegeneration, is poorly understood. Thus, the biological role of autophagy in these diseases is still open to debate. During the last years, researchers have used a wide range of morphological, genetic and biochemical methods to monitor and manipulate the autophagic pathway and thus determine the specific role of this process in TSE. It has been suggested that PrPc could play a crucial role in modulating the autophagic pathway in neuronal cells, and the presence of abnormal autophagic activity has been frequently observed in several models of TSE both in vitro and in vivo, as well as in human prion diseases. Altogether, these findings suggest that autophagy is implicated in prion neuropathology and points to an impairment or failure of the process, potentially contributing to the pathogenesis of the disease. Additionally, autophagy is now emerging as a host defense response in controlling prion infection that plays a protective role by facilitating the clearance of aggregation-prone proteins accumulated within neurons. Since autophagy is one of the pathways of PrPSc degradation, and drug-induced stimulation of autophagic flux (the dynamic process of autophagic degradation activity) produces anti-prion effects, new treatments based on its activation have been tested to develop therapeutic strategies for prion diseases. In this review, we summarize previous and recent findings concerning the role of autophagy in TSE

Human emotion characterization by heart rate variability analysis guided by respiration

© 2019 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 worksDeveloping a tool which identifies emotions based on their effect on cardiac activity may have a potential impact on clinical practice, since it may help in the diagnosing of psycho-neural illnesses. In this study, a method based on the analysis of heart rate variability (HRV) guided by respiration is proposed. The method was based on redefining the high frequency (HF) band, not only to be centered at the respiratory frequency, but also to have a bandwidth dependent on the respiratory spectrum. The method was first tested using simulated HRV signals, yielding the minimum estimation errors as compared to classical and respiratory frequency centered at HF band based definitions, independently of the values of the sympathovagal ratio. Then, the proposed method was applied to discriminate emotions in a database of video-induced elicitation. Five emotional states, relax, joy, fear, sadness and anger, were considered. The maximum correlation between HRV and respiration spectra discriminated joy vs. relax, joy vs. each negative valence emotion, and fear vs. sadness with p-value = 0.05 and AUC = 0.70. Based on these results, human emotion characterization may be improved by adding respiratory information to HRV analysis.Peer ReviewedPostprint (author's final draft