804 research outputs found
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
A delay recruitment model of the cardiovascular control system.
Copyright will be owned by Springer. We develop a nonlinear delay-differential equation for the human cardiovascular control system, and use it to explore blood pressure and heart rate variability under short-term baroreflex control. The model incorporates an intrinsically stable heart rate in the absence of nervous control, and features baroreflex influence on both heart rate and peripheral resistance. Analytical simplifications of the model allow a general investigation of the rôles played by gain and delay, and the effects of ageing.
Physiological modeling of isoprene dynamics in exhaled breath
Human breath contains a myriad of endogenous volatile organic compounds
(VOCs) which are reflective of ongoing metabolic or physiological processes.
While research into the diagnostic potential and general medical relevance of
these trace gases is conducted on a considerable scale, little focus has been
given so far to a sound analysis of the quantitative relationships between
breath levels and the underlying systemic concentrations. This paper is devoted
to a thorough modeling study of the end-tidal breath dynamics associated with
isoprene, which serves as a paradigmatic example for the class of low-soluble,
blood-borne VOCs.
Real-time measurements of exhaled breath under an ergometer challenge reveal
characteristic changes of isoprene output in response to variations in
ventilation and perfusion. Here, a valid compartmental description of these
profiles is developed. By comparison with experimental data it is inferred that
the major part of breath isoprene variability during exercise conditions can be
attributed to an increased fractional perfusion of potential storage and
production sites, leading to higher levels of mixed venous blood concentrations
at the onset of physical activity. In this context, various lines of supportive
evidence for an extrahepatic tissue source of isoprene are presented.
Our model is a first step towards new guidelines for the breath gas analysis
of isoprene and is expected to aid further investigations regarding the
exhalation, storage, transport and biotransformation processes associated with
this important compound.Comment: 14 page
Physiological modeling of isoprene dynamics in exhaled breath
Human breath contains a myriad of endogenous volatile organic compounds
(VOCs) which are reflective of ongoing metabolic or physiological processes.
While research into the diagnostic potential and general medical relevance of
these trace gases is conducted on a considerable scale, little focus has been
given so far to a sound analysis of the quantitative relationships between
breath levels and the underlying systemic concentrations. This paper is devoted
to a thorough modeling study of the end-tidal breath dynamics associated with
isoprene, which serves as a paradigmatic example for the class of low-soluble,
blood-borne VOCs.
Real-time measurements of exhaled breath under an ergometer challenge reveal
characteristic changes of isoprene output in response to variations in
ventilation and perfusion. Here, a valid compartmental description of these
profiles is developed. By comparison with experimental data it is inferred that
the major part of breath isoprene variability during exercise conditions can be
attributed to an increased fractional perfusion of potential storage and
production sites, leading to higher levels of mixed venous blood concentrations
at the onset of physical activity. In this context, various lines of supportive
evidence for an extrahepatic tissue source of isoprene are presented.
Our model is a first step towards new guidelines for the breath gas analysis
of isoprene and is expected to aid further investigations regarding the
exhalation, storage, transport and biotransformation processes associated with
this important compound.Comment: 14 page
From Inverse Problems in Mathematical Physiology to Quantitative Differential Diagnoses
The improved capacity to acquire quantitative data in a clinical setting has generally failed to improve outcomes in acutely ill patients, suggesting a need for advances in computer-supported data interpretation and decision making. In particular, the application of mathematical models of experimentally elucidated physiological mechanisms could augment the interpretation of quantitative, patient-specific information and help to better target therapy. Yet, such models are typically complex and nonlinear, a reality that often precludes the identification of unique parameters and states of the model that best represent available data. Hypothesizing that this non-uniqueness can convey useful information, we implemented a simplified simulation of a common differential diagnostic process (hypotension in an acute care setting), using a combination of a mathematical model of the cardiovascular system, a stochastic measurement model, and Bayesian inference techniques to quantify parameter and state uncertainty. The output of this procedure is a probability density function on the space of model parameters and initial conditions for a particular patient, based on prior population information together with patient-specific clinical observations. We show that multimodal posterior probability density functions arise naturally, even when unimodal and uninformative priors are used. The peaks of these densities correspond to clinically relevant differential diagnoses and can, in the simplified simulation setting, be constrained to a single diagnosis by assimilating additional observations from dynamical interventions (e.g., fluid challenge). We conclude that the ill-posedness of the inverse problem in quantitative physiology is not merely a technical obstacle, but rather reflects clinical reality and, when addressed adequately in the solution process, provides a novel link between mathematically described physiological knowledge and the clinical concept of differential diagnoses. We outline possible steps toward translating this computational approach to the bedside, to supplement today's evidence-based medicine with a quantitatively founded model-based medicine that integrates mechanistic knowledge with patient-specific information
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Whole-body mathematical model for simulating intracranial pressure dynamics
A whole-body mathematical model (10) for simulating intracranial pressure dynamics. In one embodiment, model (10) includes 17 interacting compartments, of which nine lie entirely outside of intracranial vault (14). Compartments (F) and (T) are defined to distinguish ventricular from extraventricular CSF. The vasculature of the intracranial system within cranial vault (14) is also subdivided into five compartments (A, C, P, V, and S, respectively) representing the intracranial arteries, capillaries, choroid plexus, veins, and venous sinus. The body's extracranial systemic vasculature is divided into six compartments (I, J, O, Z, D, and X, respectively) representing the arteries, capillaries, and veins of the central body and the lower body. Compartments (G) and (B) include tissue and the associated interstitial fluid in the intracranial and lower regions. Compartment (Y) is a composite involving the tissues, organs, and pulmonary circulation of the central body and compartment (M) represents the external environment
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