The adrenergic system in cardiovascular physiology and pathophysiology [[electronic resource] /] / edited by: Giuseppe Rengo

• Record Nr.: UNINA9910688341403321
• Autore: Giuseppe Rengo
• Pubbl/distr/stampa: Frontiers Media SA, 2015; [Lausanne, Switzerland] : , : Frontiers Media SA, , 2015; ©2015
• Edizione: [Second edition.]
• Descrizione fisica: 1 online resource (78 pages) : illustrations; digital, PDF file(s)
• Collana: Frontiers Research Topics; Frontiers in Physiology
• Soggetti: Cardiovascular system - Diseases; Cardiovascular system - Diseases - Pathogenesis; Cardiovascular system - Diseases - Prevention - Research
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references.
• Sommario/riassunto: Cardiovascular diseases pose an enormous clinical challenge, remaining the most common cause of death in the world. β-adrenoceptors play an important role on cardiac, vascular and/or endothelial function at a cellular level with relevant applications in several cardiovascular diseases, such as heart failure and hypertension. G protein–coupled receptors (GPCRs), including β-adrenergic receptors, constitute the most ubiquitous superfamily of plasma membrane receptors and represent the single most important type of therapeutic drug target. Sympathetic nervous system hyperactivity, which characterizes several cardiovascular diseases, such as heart failure and hypertension, as well as physiological ageing, has been proved to exert in the long-term detrimental effects in a wide range of cardiovascular diseases. Acutely, sympathetic hyperactivity represents the response to an insult to the myocardium, aiming to compensate for decreased cardiac output. This process involves the activation of beta-adrenergic receptors by catecholamine with consequent heart rate and cardiac contractility increase. However, long-term exposure of the heart to ; elevated norepinephrine and epinephrine levels, originating from sympathetic nerve endings and chromaffin cells of the adrenal gland, results in further progressive deterioration in cardiac structure and function. At the molecular level, sustained sympathetic nervous system hyperactivity is responsible for several alterations including altered beta-adrenergic receptor signaling and function (down-regulation/desensitization). Moreover, the detrimental effects of catecholamine affect also the function of different cell types including, but not limited to, endothelial cells, fibroblasts and smooth muscle cells. Thus, the success of beta-blocker therapy is due, at least in part, to the protection of the heart and the vasculature from the noxious effects of augmented catecholamine levels. The current research topic aims to support the progress towards understanding the role of sympathetic nervous system under physiological conditions, and the contribution of its hyperactivity in the pathogenesis and progression of cardiovascular diseases. The topic is open to original studies, descriptions of new methodologies, reviews and opinions.

Basic and advanced Bayesian structural equation modeling : with applications in the medical and behavioral sciences / / Sik-Yum Lee and Xin-Yuan Song

• Record Nr.: UNINA9910821778903321
• Autore: Lee Sik-Yum
• Pubbl/distr/stampa: Hoboken, : Wiley, 2012
• ISBN: 1-118-35887-2; 1-280-87995-5; 9786613721266; 1-118-35880-5; 1-118-35888-0; 1-118-35943-7
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (397 p.)
• Collana: Wiley series in probability and statistics
• Classificazione: MAT029000
• Altri autori (Persone): SongXin-Yuan
• Disciplina: 519.5/3
• Soggetti: Structural equation modeling; Bayesian statistical decision theory
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Basic and Advanced Bayesian Structural Equation Modeling; Contents; About the authors; Preface; 1 Introduction; 1.1 Observed and latent variables; 1.2 Structural equation model; 1.3 Objectives of the book; 1.4 The Bayesian approach; 1.5 Real data sets and notation; Appendix 1.1: Information on real data sets; References; 2 Basic concepts and applications of structural equation models; 2.1 Introduction; 2.2 Linear SEMs; 2.2.1 Measurement equation; 2.2.2 Structural equation and one extension; 2.2.3 Assumptions of linear SEMs; 2.2.4 Model identification; 2.2.5 Path diagram; 2.3 SEMs with fixed covariates 2.3.1 The model; 2.3.2 An artificial example; 2.4 Nonlinear SEMs; 2.4.1 Basic nonlinear SEMs; 2.4.2 Nonlinear SEMs with fixed covariates; 2.4.3 Remarks; 2.5 Discussion and conclusions; References; 3 Bayesian methods for estimating structural equation models; 3.1 Introduction; 3.2 Basic concepts of the Bayesian estimation and prior distributions; 3.2.1 Prior distributions; 3.2.2 Conjugate prior distributions in Bayesian analyses of SEMs; 3.3 Posterior analysis using Markov chain Monte Carlo methods; 3.4 Application of Markov chain Monte Carlo methods; 3.5 Bayesian estimation via WinBUGS Appendix 3.1: The gamma, inverted gamma, Wishart, and inverted Wishart distributions and their characteristics; Appendix 3.2: The Metropolis-Hastings algorithm; Appendix 3.3: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω]; Appendix 3.4: Conditional distributions [Ω|Y,θ] and [θ|Y,Ω] in nonlinear SEMs with covariates; Appendix 3.5: WinBUGS code; Appendix 3.6: R2WinBUGS code; References; 4 Bayesian model comparison and model checking; 4.1 Introduction; 4.2 Bayes factor; 4.2.1 Path sampling; 4.2.2 A simulation study; 4.3 Other model comparison statistics; 4.3.1 Bayesian information criterion and Akaike information criterion 4.3.2 Deviance information criterion; 4.3.3 Lν-measure; 4.4 Illustration; 4.5 Goodness of fit and model checking methods; 4.5.1 Posterior predictive p-value; 4.5.2 Residual analysis; Appendix 4.1: WinBUGS code; Appendix 4.2: R code in Bayes factor example; Appendix 4.3: Posterior predictive p-value for model assessment; References; 5 Practical structural equation models; 5.1 Introduction; 5.2 SEMs with continuous and ordered categorical variables; 5.2.1 Introduction; 5.2.2 The basic model; 5.2.3 Bayesian analysis; 5.2.4 Application: Bayesian analysis of quality of life data 5.2.5 SEMs with dichotomous variables; 5.3 SEMs with variables from exponential family distributions; 5.3.1 Introduction; 5.3.2 The SEM framework with exponential family distributions; 5.3.3 Bayesian inference; 5.3.4 Simulation study; 5.4 SEMs with missing data; 5.4.1 Introduction; 5.4.2 SEMs with missing data that are MAR; 5.4.3 An illustrative example; 5.4.4 Nonlinear SEMs with nonignorable missing data; 5.4.5 An illustrative real example; Appendix 5.1: Conditional distributions and implementation of the MH algorithm for SEMs with continuous and ordered categorical variables
• Sommario/riassunto: "This book introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject's recent advances"--