Arterial Baroreflexes and Cardiovascular Modeling

Many cardiovascular models involve prediction of changes that occur when a subject is perturbed in some way, to move from one state to another. A successful, predictive model should involve at least two elements: First, the model should include some index of the intensity of the perturbation that elicits the response; effective responses should, in some fashion, be linearly or nonlinearity related to perturbations. Second, the model should factor in subjects’ abilities to meet the challenges posed by the perturbations. This review indicates that these two basic components of a successful model may be difficult to incorporate. In the simple case of passive upright tilt, blood pressure measurements may not accurately indicate the stimulus, because blood pressure reductions are reversed by rapidly occurring reflex blood pressure increases. Since not all subject populations respond identically to hemodynamic challenges, it also may be important to characterize baroreflex responsiveness, and include such a term in a model. Although vagal and sympathetic baroreflex responses to stereotyped challenges can be measured accurately, recent research points to extraordinary variability of baroreflex responsiveness. The complexities discussed in this review should be considered, whether they are, or even can be incorporated into cardiovascular models.     

Imaging and Modeling of Myocardial Metabolism

Current imaging methods have focused on evaluation of myocardial anatomy and function. However, since myocardial metabolism and function are interrelated, metabolic myocardial imaging techniques, such as positron emission tomography, single photon emission tomography, and magnetic resonance spectroscopy present novel opportunities for probing myocardial pathology and developing new therapeutic approaches. Potential clinical applications of metabolic imaging include hypertensive and ischemic heart disease, heart failure, cardiac transplantation, as well as cardiomyopathies. Furthermore, response to therapeutic intervention can be monitored using metabolic imaging. Analysis of metabolic data in the past has been limited, focusing primarily on isolated metabolites. Models of myocardial metabolism, however, such as the oxygen transport and cellular energetics model and constraint-based metabolic network modeling, offer opportunities for evaluation interactions between greater numbers of metabolites in the heart. In this review, the roles of metabolic myocardial imaging and analysis of metabolic data using modeling methods for expanding our understanding of cardiac pathology are discussed.     

Modeling of diastole

Modeling methods have been employed to further characterize the physical and physiologic processes of filling and diastolic function. They have led to more detailed understanding of the effect of alteration of physiologic parameters on the Doppler E-wave contour as well as pulmonary vein flow. Depending on the modeling approach, different aspects of the filling process have been considered from AV gradient and net compliance to atrial appendage function to the mechanical suction pump attribute of the heart. The models have been applied for further characterization of diastolic function and elucidation of novel basic physiologic relations. We trust that readers recognize that this article could not serve as a comprehensive and global review of the state-of-the-art in physiologic modeling, but rather as a selective overview, with emphasis on the main modeling principles and options currently in use. Modeling of systems physiology, especially as it relates to the function of the four-chamber heart, remains a fertile area of investigation. Future progress is likely to have profound influence on (noninvasive) diagnosis and quantitation of the effect of therapy and lead to continued discovery of "new" (macroscopic, cellular, and molecular biologic) physiology.     

Modeling and design by hierarchical natural moves

We develop a unique algorithm implemented in the program MOSAICS (Methodologies for Optimization and Sampling in Computational Studies) that is capable of nanoscale modeling without compromising the resolution of interest. This is achieved by modeling with customizable hierarchical degrees of freedom, thereby circumventing major limitations of conventional molecular modeling. With the emergence of RNA-based nanotechnology, large RNAs in all-atom representation are used here to benchmark our algorithm. Our method locates all favorable structural states of a model RNA of significant complexity while improving sampling accuracy and increasing speed many fold over existing all-atom RNA modeling methods. We also modeled the effects of sequence mutations on the structural building blocks of tRNA-based nanotechnology. With its flexibility in choosing arbitrary degrees of freedom as well as in allowing different all-atom energy functions, MOSAICS is an ideal tool to model and design biomolecules of the nanoscale.     

Modeling of cellular metabolism and microcirculatory transport

Oxygen and other substrates, waste products, hormone messengers, and cells and other particles of the immune system are all transported in a closed-loop circulatory system in vertebrates, within which pumped blood travels to within diffusion distances of practically every cell in the body. Exchange of oxygen and carbon dioxide in the pulmonary capillaries and absorption of nutrients in the gut provide the circulating blood with biochemical reactants to sustain bioenergetic processes throughout the body. Inputs and outputs transported by the microcirculation are necessary to drive the open-system nonequilibrium chemical reactions of metabolism that are essential for cellular function. In turn, metabolically derived signals influence microcirculatory dynamics. Indeed, the microcirculation is the key system that ties processes at the whole-body level of the cardiovascular system to subcellular phenomena. This tight integration between cellular metabolism and microcirculatory transport begs for integrative simulations that span the cell, tissue, and organ scales.     

Modeling river delta formation

A model to simulate the time evolution of river delta formation process is presented. It is based on the continuity equation for water and sediment flow and a phenomenological sedimentation/erosion law. Different delta types are reproduced by using different parameters and erosion rules. The structures of the calculated patterns are analyzed in space and time and compared with real data patterns. Furthermore, our model is capable of simulating the rich dynamics related to the switching of the mouth of the river delta. The simulation results are then compared with geological records for the Mississippi River.     

Data-Driven Parameter Selection and Modeling for Concrete Carbonation

Concrete carbonation is known as a stochastic process. Its uncertainties mainly result from parameters that are not considered in prediction models. Parameter selection, therefore, is important. In this paper, based on 8204 sets of data, statistical methods and machine learning techniques were applied to choose appropriate influence factors in terms of three aspects: (1) the correlation between factors and concrete carbonation; (2) factors’ influence on the uncertainties of carbonation depth; and (3) the correlation between factors. Both single parameters and parameter groups were evaluated quantitatively. The results showed that compressive strength had the highest correlation with carbonation depth and that using the aggregate–cement ratio as the parameter significantly reduced the dispersion of carbonation depth to a low level. Machine learning models manifested that selected parameter groups had a large potential in improving the performance of models with fewer parameters. This paper also developed machine learning carbonation models and simplified them to propose a practical model. The results showed that this concise model had a high accuracy on both accelerated and natural carbonation test datasets. For natural carbonation datasets, the mean absolute error of the practical model was 1.56 mm.     

Modeling chick to assess diabetes pathogenesis and treatment

Animal models have been used extensively in diabetes research. Studies on animal models have contributed to the discovery and purification of insulin, development of new therapeutic approaches, and progress in fundamental and clinical research. However, conventional rodent and large animal mammalian models face ethical, practical, or technical limitations. Therefore, it would be beneficial developing an alternative model for diabetes research which would overcome these limitations. Amongst other vertebrates, birds are phylogenically closer to mammals, and amongst birds, the chick has been used as one of the favored models in developmental biology, toxicology, cancer research, immunology, and drug testing. Chicken eggs are readily available, have a short incubation period and easily accessible embryos. Based on these inimitable advantages, the present review article aims to discuss the suitability of the chick as a model system to study specific aspects of diabetes. The review focuses on the application of i) chick pancreatic islets for screening of antidiabetic agents and for islet banking, (ii) shell-less chick embryo culture as a model to study hyperglycemia-induced malformations observed in mammalian embryos, and (iii) chick chorioallantoic membrane (CAM) to examine glucose-induced endothelial damage leading to inhibition of angiogenesis.     

Iontophoresis: Modeling, Methodology, and Evaluation

Iontophoresis is a noninvasive and painless means of delivering various drugs into the body. Many drugs, in particular peptides, proteins, and hormones are given parenterally either through intravenous, subcutaneous, or intramuscular injections. Transdermal delivery using iontophoresis circumvents hepatic clearance and breakdown by the gastric juices thus allowing local high concentrations of active compounds. Local delivery of these compounds is much safer than parenteral routes since lower concentrations are necessary to reach the target sites. The present analysis focuses on previously overlooked areas including skin impedance, iontophoretic waveforms, skin modeling, optimization of delivery parameters, and their effects on iontophoretic delivery. Particular emphases are placed on modeling, methodology, and evaluations of the efficacy of iontophoresis.     

Modeling the Distribution and Consequences of Alcohol Consumption

This paper is based on the session "Modeling the Distribution and Consequences of Alcohol Consumption" that was held on May 12-14, 1997 at the International Workshop on Consumption Measures and Models for Use in Policy Development and Evaluation, Bethesda, MD. The session chair was Paul J. Gruenewald; presenters were Timo Alanko, Bryan Johnstone, Paul J. Gruenewald, Susan Bondy, Thomas Harford, Raul Caetano, and Maria Elena Medina-Mora; and the discussants were Jiirgen Rehm and Bryan Johnstone.     

Mailborne transmission of anthrax: Modeling and implications

A mathematical model is developed to analyze the transmission of inhalational anthrax through the postal system by cross-contamination of mail. The model consists of state vectors describing the numbers of cross-contaminated letters generated, the numbers of anthrax spores on these letters, the numbers of resulting infections in recipients, and matrices of transition probabilities acting on these vectors. The model simulates the recent outbreak in the United States, and provides a general framework to investigate the potential impact of possible future outbreaks.     

Modeling of coronary arteries and cardiovascular risk factors

The aim of this study was to assess the impact of cardiovascular risk factors on the modelling of atherosclerotic coronary arteries. One hundred consecutive patients who underwent coronary angioplasty were studied by endocoronary ultrasonography at the site of dilatation. At the site of the treated stenosis of the dilated arteries there was either compensatory widening or positive modelling (PM), or focal contraction or negative modelling (NM) if the total surface area (TSA) of the artery at the site of dilatation was greater or smaller than the total surface area of the proximal or distal reference segments. PM was observed in 53 cases (53%) and NM in 47 cases (47%). Lesions with NM had smaller TSA (13.7 +/- 5.8 versus 20.8 +/- 6.4 mm2, p < 0.0001) and a smaller atheromatous plaque (11.8 +/- 5.6 versus 19.1 +/- 6.5 mm2, p < 0.0001) than lesions with PM. Cardiovascular risk factors such as hypercholesterolaemia, smoking and hypertension were not predictive of either form of arterial modelling and there was no relationship between the cardiovascular risk factors and the qualitative appearances of the plaque studied.