The role of the autonomic nervous system in hypertension: a bond graph model study

A Cross Talk between the Endocannabinoid System and Different Systems Involved in the Pathogenesis of Hypertensive Retinopathy

The prognosis of hypertension leads to organ damage by causing nephropathy, stroke, retinopathy, and cardiomegaly. Retinopathy and blood pressure have been extensively discussed in relation to catecholamines of the autonomic nervous system (ANS) and angiotensin II of the renin–angiotensin aldosterone system (RAAS) but very little research has been conducted on the role of the ECS in the regulation of retinopathy and blood pressure. The endocannabinoid system (ECS) is a unique system in the body that can be considered as a master regulator of body functions. It encompasses the endogenous production of its cannabinoids, its degrading enzymes, and functional receptors which innervate and perform various functions in different organs of the body. Hypertensive retinopathy pathologies arise normally due to oxidative stress, ischemia, endothelium dysfunction, inflammation, and an activated renin–angiotensin system (RAS) and catecholamine which are vasoconstrictors in their biological nature. The question arises of which system or agent counterbalances the vasoconstrictors effect of noradrenaline and angiotensin II (Ang II) in normal individuals? In this review article, we discuss the role of the ECS and its contribution to the pathogenesis of hypertensive retinopathy. This review article will also examine the involvement of the RAS and the ANS in the pathogenesis of hypertensive retinopathy and the crosstalk between these three systems in hypertensive retinopathy. This review will also explain that the ECS, which is a vasodilator in its action, either independently counteracts the effect produced with the vasoconstriction of the ANS and Ang II or blocks some of the common pathways shared by the ECS, ANS, and Ang II in the regulation of eye functions and blood pressure. This article concludes that persistent control of blood pressure and normal functions of the eye are maintained either by decreasing systemic catecholamine, ang II, or by upregulation of the ECS which results in the regression of retinopathy induced by hypertension.

Model-based assessment of cardiopulmonary autonomic regulation in paced deep breathing

Autonomic dysfunction can lead to many physical and psychological diseases. The assessment of autonomic regulation plays an important role in the prevention, diagnosis, and treatment of these diseases. A physiopathological mathematical model for cardiopulmonary autonomic regulation, namely Respiratory-Autonomic-Sinus (RSA) regulation Model, is proposed in this study. A series of differential equations are used to simulate the whole process of RSA phenomenon. Based on this model, with respiration signal and ECG signal simultaneously acquired in paced deep breathing scenario, we manage to obtain the cardiopulmonary autonomic regulation parameters (CARP), including the sensitivity of respiratory-sympathetic nerves and respiratory-parasympathetic nerves, the time delay of sympathetic, the sensitivity of norepinephrine and acetylcholine receptor, as well as cardiac remodeling factor by optimization algorithm. An experimental study has been conducted in healthy subjects, along with subjects with hypertension and coronary heart disease. CARP obtained in the experiment have shown their clinical significance.

Seven Mathematical Models of Hemorrhagic Shock

Although mathematical modelling of pressure-flow dynamics in the cardiocirculatory system has a lengthy history, readily finding the appropriate model for the experimental situation at hand is often a challenge in and of itself. An ideal model would be relatively easy to use and reliable, besides being ethically acceptable. Furthermore, it would address the pathogenic features of the cardiovascular disease that one seeks to investigate. No universally valid model has been identified, even though a host of models have been developed. The object of this review is to describe several of the most relevant mathematical models of the cardiovascular system: the physiological features of circulatory dynamics are explained, and their mathematical formulations are compared. The focus is on the whole-body scale mathematical models that portray the subject's responses to hypovolemic shock. The models contained in this review differ from one another, both in the mathematical methodology adopted and in the physiological or pathological aspects described. Each model, in fact, mimics different aspects of cardiocirculatory physiology and pathophysiology to varying degrees: some of these models are geared to better understand the mechanisms of vascular hemodynamics, whereas others focus more on disease states so as to develop therapeutic standards of care or to test novel approaches. We will elucidate key issues involved in the modeling of cardiovascular system and its control by reviewing seven of these models developed to address these specific purposes.

Modeling photoplethysmographic signals in camera-based perfusion measurements: optoelectronic skin phantom

The remote acquisition of photoplethysmographic (PPG) signals via a video camera, also known as photoplethysmography imaging (PPGI), is not yet standardized. In general, PPGI is investigated with test persons in a laboratory setting. While these in-vivo tests have the advantage of generating real-life data, they suffer from the lack of repeatability and are comparatively effort-intensive because human subjects are required. Consequently, studying changes in signal morphology, for example, due to aging or pathological effects, is practically impossible. As a tool to study these effects, a hardware PPG simulator has been developed: this is a phantom which simulates and generates both 1D and locally resolved 2D optical PPG signals. Here, we demonstrate that it is possible to generate PPG-like signals with various signal morphologies by means of a purely optoelectronic setup, namely an LED array, and to analyze them by means of PPGI. Signals extracted via a camera show good agreement with simulated generated signals. In fact, the first phantom design is suitable to demonstrate this qualitatively.

Requirements for the formal representation of pathophysiology mechanisms by clinicians

Knowledge of multiscale mechanisms in pathophysiology is the bedrock of clinical practice. If quantitative methods, predicting patient-specific behaviour of these pathophysiology mechanisms, are to be brought to bear on clinical decision-making, the Human Physiome community and Clinical community must share a common computational blueprint for pathophysiology mechanisms. A number of obstacles stand in the way of this sharing-not least the technical and operational challenges that must be overcome to ensure that (i) the explicit biological meanings of the Physiome's quantitative methods to represent mechanisms are open to articulation, verification and study by clinicians, and that (ii) clinicians are given the tools and training to explicitly express disease manifestations in direct contribution to modelling. To this end, the Physiome and Clinical communities must co-develop a common computational toolkit, based on this blueprint, to bridge the representation of knowledge of pathophysiology mechanisms (a) that is implicitly depicted in electronic health records and the literature, with (b) that found in mathematical models explicitly describing mechanisms. In particular, this paper makes use of a step-wise description of a specific disease mechanism as a means to elicit the requirements of representing pathophysiological meaning explicitly. The computational blueprint developed from these requirements addresses the Clinical community goals to (i) organize and manage healthcare resources in terms of relevant disease-related knowledge of mechanisms and (ii) train the next generation of physicians in the application of quantitative methods relevant to their research and practice.

Heart rate dependence of aortic pulse wave velocity at different arterial pressures in rats

Arterial stiffness, as measured by aortic pulse wave velocity (PWV), is an independent marker of cardiovascular disease and events in both healthy and diseased populations. Although some cardiovascular risk factors, such as age and blood pressure, show a strong association with PWV, the association between heart rate (HR) and PWV is not firmly established. Furthermore, this association has not been investigated at different arterial blood pressures. To study effects of HR on aortic PWV at different mean arterial pressures (MAPs), adult (12 weeks; n=7), male, anesthetized Sprague-Dawley rats were randomly paced at HRs of between 300 and 450 bpm, at 50-bpm steps. At each pacing step, aortic PWV was measured across a physiological MAP range of 60 to 150 mmHg by infusing sodium nitroprusside and phenylephrine. When compared at the same MAP, increases in HR resulted in significant increases in PWV at all of the MAPs >80 mmHg (ANOVA, P<0.05), with the greatest significant change of 6.03±0.93% observed in the range 110 to 130 mmHg. The positive significant association between HR and PWV remained when PWV was adjusted for MAP (ANOVA, P<0.001). These results indicate that HR dependency of PWV is different at higher pressures than at lower pressures and that HR may be a confounding factor that should be taken into consideration when performing analysis based on PWV measurements.

Review of zero-D and 1-D models of blood flow in the cardiovascular system

Background

Zero-dimensional (lumped parameter) and one dimensional models, based on simplified representations of the components of the cardiovascular system, can contribute strongly to our understanding of circulatory physiology. Zero-D models provide a concise way to evaluate the haemodynamic interactions among the cardiovascular organs, whilst one-D (distributed parameter) models add the facility to represent efficiently the effects of pulse wave transmission in the arterial network at greatly reduced computational expense compared to higher dimensional computational fluid dynamics studies. There is extensive literature on both types of models.

Method and Results

The purpose of this review article is to summarise published 0D and 1D models of the cardiovascular system, to explore their limitations and range of application, and to provide an indication of the physiological phenomena that can be included in these representations. The review on 0D models collects together in one place a description of the range of models that have been used to describe the various characteristics of cardiovascular response, together with the factors that influence it. Such models generally feature the major components of the system, such as the heart, the heart valves and the vasculature. The models are categorised in terms of the features of the system that they are able to represent, their complexity and range of application: representations of effects including pressure-dependent vessel properties, interaction between the heart chambers, neuro-regulation and auto-regulation are explored. The examination on 1D models covers various methods for the assembly, discretisation and solution of the governing equations, in conjunction with a report of the definition and treatment of boundary conditions. Increasingly, 0D and 1D models are used in multi-scale models, in which their primary role is to provide boundary conditions for sophisticate, and often patient-specific, 2D and 3D models, and this application is also addressed. As an example of 0D cardiovascular modelling, a small selection of simple models have been represented in the CellML mark-up language and uploaded to the CellML model repository http://models.cellml.org/. They are freely available to the research and education communities.

Conclusion

Each published cardiovascular model has merit for particular applications. This review categorises 0D and 1D models, highlights their advantages and disadvantages, and thus provides guidance on the selection of models to assist various cardiovascular modelling studies. It also identifies directions for further development, as well as current challenges in the wider use of these models including service to represent boundary conditions for local 3D models and translation to clinical application.