Nonlinear Hierarchical Models for Longitudinal Experimental Infection Studies

Experimental infection (EI) studies, involving the intentional inoculation of animal or human subjects with an infectious agent under controlled conditions, have a long history in infectious disease research. Longitudinal infection response data often arise in EI studies designed to demonstrate vaccine efficacy, explore disease etiology, pathogenesis and transmission, or understand the host immune response to infection. Viral loads, antibody titers, symptom scores and body temperature are a few of the outcome variables commonly studied. Longitudinal EI data are inherently nonlinear, often with single-peaked response trajectories with a common pre- and post-infection baseline. Such data are frequently analyzed with statistical methods that are inefficient and arguably inappropriate, such as repeated measures analysis of variance (RM-ANOVA). Newer statistical approaches may offer substantial gains in accuracy and precision of parameter estimation and power. We propose an alternative approach to modeling single-peaked, longitudinal EI data that incorporates recent developments in nonlinear hierarchical models and Bayesian statistics. We begin by introducing a nonlinear mixed model (NLMM) for a symmetric infection response variable. We employ a standard NLMM assuming normally distributed errors and a Gaussian mean response function. The parameters of the model correspond directly to biologically meaningful properties of the infection response, including baseline, peak intensity, time to peak and spread. Through Monte Carlo simulation studies we demonstrate that the model outperforms RM-ANOVA on most measures of parameter estimation and power. Next we generalize the symmetric NLMM to allow modeling of variables with asymmetric time course. We implement the asymmetric model as a Bayesian nonlinear hierarchical model (NLHM) and discuss advantages of the Bayesian approach. Two illustrative applications are provided. Finally we consider modeling of viral load. For several reasons, a normal-errors model is not appropriate for viral load. We propose and illustrate a Bayesian NLHM with the individual responses at each time point modeled as a Poisson random variable with the means across time points related through a Tricube mean response function. We conclude with discussion of limitations and open questions, and a brief survey of broader applications of these models

A mathematical model for breath gas analysis of volatile organic compounds with special emphasis on acetone

Recommended standardized procedures for determining exhaled lower respiratory nitric oxide and nasal nitric oxide have been developed by task forces of the European Respiratory Society and the American Thoracic Society. These recommendations have paved the way for the measurement of nitric oxide to become a diagnostic tool for specific clinical applications. It would be desirable to develop similar guidelines for the sampling of other trace gases in exhaled breath, especially volatile organic compounds (VOCs) which reflect ongoing metabolism. The concentrations of water-soluble, blood-borne substances in exhaled breath are influenced by: (i) breathing patterns affecting gas exchange in the conducting airways; (ii) the concentrations in the tracheo-bronchial lining fluid; (iii) the alveolar and systemic concentrations of the compound. The classical Farhi equation takes only the alveolar concentrations into account. Real-time measurements of acetone in end-tidal breath under an ergometer challenge show characteristics which cannot be explained within the Farhi setting. Here we develop a compartment model that reliably captures these profiles and is capable of relating breath to the systemic concentrations of acetone. By comparison with experimental data it is inferred that the major part of variability in breath acetone concentrations (e.g., in response to moderate exercise or altered breathing patterns) can be attributed to airway gas exchange, with minimal changes of the underlying blood and tissue concentrations. Moreover, it is deduced that measured end-tidal breath concentrations of acetone determined during resting conditions and free breathing will be rather poor indicators for endogenous levels. Particularly, the current formulation includes the classical Farhi and the Scheid series inhomogeneity model as special limiting cases.Comment: 38 page

Cardiac cell modelling: Observations from the heart of the cardiac physiome project

In this manuscript we review the state of cardiac cell modelling in the context of international initiatives such as the IUPS Physiome and Virtual Physiological Human Projects, which aim to integrate computational models across scales and physics. In particular we focus on the relationship between experimental data and model parameterisation across a range of model types and cellular physiological systems. Finally, in the context of parameter identification and model reuse within the Cardiac Physiome, we suggest some future priority areas for this field

Modeling and Estimation of Cardiorespiratory Function, with Application to Mechanical Ventilation

Evidence-based medicine is at the heart of current medical practice where clinical decisions are driven by research data. However, most current therapy recommendations follow generalized protocols and guidelines that are based on epidemiological (population) studies and thus not suited for the individual patient's demands. Patient-tailored therapies are considered, hence, an unmet clinical need. We believe that mathematical models of the physiology can attend to such a clinical need, because they can be tuned to the individual patient. Such models provide a sound mathematical framework for personalized clinical decisions. In particular, physiological models in medicine can serve the following two purposes: 1) They can be an efficient tool to quantify cardiopulmonary dynamics, conduct virtual clinical/physiological experiments, and investigate the effects of specific treatments. 2) Model-based estimation techniques can assess physiological parameters or variables, which are otherwise impractical or dangerous to measure; they can effectively tune a generic model to become patient-specific, able to mimic the behavior of a particular patient. In this thesis, we propose a series of modifications to a previously developed cardiopulmonary model (CP Model) in order to better replicate heart-lung interaction phenomena that are typically observed under mechanical ventilation, hence allowing for a more accurate analysis of ventilation-induced changes in cardiac function. The response of this modified model is validated with experimental data collected during mechanical ventilation conditions. Further, as an industrial application of mathematical models, we present a patient emulator system that comprises the modified CP Model, a physical ventilator, and a piston-cylinder arrangement that serves as an electrical-to-hydraulic transducer. The modified CP Model then serves as the virtual patient that is being ventilated, where disease conditions can be instilled. Such a system is designed to offer a well-controlled experimental environment for ventilator manufacturers to efficaciously test and compare ventilation modalities and therapies, thereby enhancing their verification and validation manufacturing processes. Finally, we develop a model-based approach to estimate (noninvasively) the function of the cardiovascular system, in terms of cardiac performance (i.e., cardiac output) and the dynamics of the systemic arterial tree (i.e., time constant). With this technique, we envision to provide continuous and real-time bedside monitoring of changes in cardiovascular function, such as those induced by changes in ventilator settings

Integrated Heart - Coupling multiscale and multiphysics models for the simulation of the cardiac function

Mathematical modelling of the human heart and its function can expand our understanding of various cardiac diseases, which remain the most common cause of death in the developed world. Like other physiological systems, the heart can be understood as a complex multiscale system involving interacting phenomena at the molecular, cellular, tissue, and organ levels. This article addresses the numerical modelling of many aspects of heart function, including the interaction of the cardiac electrophysiology system with contractile muscle tissue, the sub-cellular activation-contraction mechanisms, as well as the hemodynamics inside the heart chambers. Resolution of each of these sub-systems requires separate mathematical analysis and specially developed numerical algorithms, which we review in detail. By using specific sub-systems as examples, we also look at systemic stability, and explain for example how physiological concepts such as microscopic force generation in cardiac muscle cells, translate to coupled systems of differential equations, and how their stability properties influence the choice of numerical coupling algorithms. Several numerical examples illustrate three fundamental challenges of developing multiphysics and multiscale numerical models for simulating heart function, namely: (i) the correct upscaling from single-cell models to the entire cardiac muscle, (ii) the proper coupling of electrophysiology and tissue mechanics to simulate electromechanical feedback, and (iii) the stable simulation of ventricular hemodynamics during rapid valve opening and closure

Nervous system differences between the sexes and across the menstrual cycle

Sex hormones have in vitro effects on the nervous system. Furthermore, the effects of estradiol and progesterone metabolites have neurologic responses on the motor system, evidenced by transcranial magnetic stimulation studies. Sex hormone effects on the nervous system may underlie some of the sex discrepancies seen in athletic performance, injury and cardiovascular events. Investigating sex differences is often complicated by hormonal oscillations across the menstrual cycle; therefore, the aims of this research were to investigate sex and menstrual cycle effects on the motor and cardiovascular systems and the interaction of these two systems. In study 1, motor unit (MU) recruitment patterns of the vastus medialis (VM) and vastus medialis oblique (VMO) were examined in males and females at five menstrual cycle phases. Initial discharge rate between the VM and VMO were different only in females. This VM/VMO discharge discrepancy was only evident in females during the ovulatory and mid luteal menstrual phases. Study 2 examined the frequency domain relationship of the VM and VMO MUs between the sexes and across the menstrual cycle. Males have 256% to 741% greater odds of having coherent MU oscillations in the common drive band than females, indicating a greater common rate modulation. Further evidence indicated MU pairs from the VMO and VM/VMO have 228% and 212% greater odds of having beta band oscillations than the VM, indicating control of those muscle groups has a common cortical modulation. Study 3 looked at changes in the autonomic nervous system across the menstrual cycle via heart rate variability analysis. Heart rate variability decreases from the follicular to the luteal phases of the menstrual cycle, indicating a decrease in parasympathetic control. In study 4, the time and frequency domain relationship between electrocardiogram and MU discharge timing was examined between the sexes and across the menstrual cycle. The time domain relationship indicated that both males and females have MU time lag centered between 20-25 milliseconds, with an apparent modulation of this relationship across the menstrual cycle. The findings from this series of studies indicate that there are differences between the sexes which are often modified by the menstrual cycle in females.Kinesiology and Health Educatio

Aerospace medicine and biology. A continuing bibliography with indexes, supplement 195

This bibliography lists 148 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1979