Impact of endocrine disruptors from mother's diet on immuno-hormonal orchestration of brain development and introduction of the virtual human twin tool

Diet has long been known to modify physiology during development and adulthood. However, due to a growing number of manufactured contaminants and additives over the last few decades, diet has increasingly become a source of exposure to chemicals that has been associated with adverse health risks. Sources of food contaminants include the environment, crops treated with agrochemicals, inappropriate storage (e.g., mycotoxins) and migration of xenobiotics from food packaging and food production equipment. Hence, consumers are exposed to a mixture of xenobiotics, some of which are endocrine disruptors (EDs). The complex interactions between immune function and brain development and their orchestration by steroid hormones are insufficiently understood in human populations, and little is known about the impact on immune-brain interactions by transplacental fetal exposure to EDs via maternal diet. To help to identify the key data gaps, this paper aims to present (a) how transplacental EDs modify immune system and brain development, and (b) how these mechanisms may correlate with diseases such as autism and disturbances of lateral brain development. Attention is given to disturbances of the subplate, a transient structure of crucial significance in brain development. Additionally, we describe cutting edge approaches to investigate the developmental neurotoxicity of EDs, such as the application of artificial intelligence and comprehensive modelling. In the future, highly complex investigations will be performed using virtual brain models constructed using sophisticated multi-physics/multi-scale modelling strategies based on patient and synthetic data, which will enable a greater understanding of healthy or disturbed brain development.

Sensitivity analysis of a strongly-coupled human-based electromechanical cardiac model: Effect of mechanical parameters on physiologically relevant biomarkers

The human heart beats as a result of multiscale nonlinear dynamics coupling subcellular to whole organ processes, achieving electrophysiologically-driven mechanical contraction. Computational cardiac modelling and simulation have achieved a great degree of maturity, both in terms of mathematical models of underlying biophysical processes and the development of simulation software. In this study, we present the detailed description of a human-based physiologically-based, and fully-coupled ventricular electromechanical modelling and simulation framework, and a sensitivity analysis focused on its mechanical properties. The biophysical detail of the model, from ionic to whole-organ, is crucial to enable future simulations of disease and drug action. Key novelties include the coupling of state-of-the-art human-based electrophysiology membrane kinetics, excitation-contraction and active contraction models, and the incorporation of a pre-stress model to allow for pre-stressing and pre-loading the ventricles in a dynamical regime. Through high performance computing simulations, we demonstrate that 50% to 200% - 1000% variations in key parameters result in changes in clinically-relevant mechanical biomarkers ranging from diseased to healthy values in clinical studies. Furthermore mechanical biomarkers are primarily affected by only one or two parameters. Specifically, ejection fraction is dominated by the scaling parameter of the active tension model and its scaling parameter in the normal direction ( k ort 2 ); the end systolic pressure is dominated by the pressure at which the ejection phase is triggered ( P ej ) and the compliance of the Windkessel fluid model ( C ); and the longitudinal fractional shortening is dominated by the fibre angle ( ϕ ) and k ort 2 . The wall thickening does not seem to be clearly dominated by any of the considered input parameters. In summary, this study presents in detail the description and implementation of a human-based coupled electromechanical modelling and simulation framework, and a high performance computing study on the sensitivity of mechanical biomarkers to key model parameters. The tools and knowledge generated enable future investigations into disease and drug action on human ventricles.

Separation of the reservoir and wave pressure and velocity from measurements at an arbitrary location in arteries

Previous studies based on measurements made in the ascending aorta have demonstrated that it can be useful to separate the arterial pressure P into a reservoir pressure P* generated by the windkessel effect and a wave pressure p generated by the arterial waves: P = P*+p. The separation in these studies was relatively straightforward since the flow into the arterial system was measured. In this study the idea is extended to measurements of pressure and velocity at sites distal to the aortic root where flow into the arterial system is not known. P* is calculated from P at an arbitrary location in a large artery by fitting the pressure fall-off in diastole to an exponential function and assuming that p is proportional to the flow into the arterial system. A local reservoir velocity U* that is proportional to P* is also defined. The separation algorithm is applied to in vivo human and canine data and to numerical data generated using a one-dimensional model of pulse wave propagation in the larger conduit arteries. The results show that the proposed algorithm is reasonably robust, allowing for the separation of the measured pressure and velocity into reservoir and wave pressures and velocities. Application to data measured simultaneously in the aorta of the dog shows that the reservoir pressure is fairly uniform along the aorta, a test of self-consistency of the assumptions leading to the algorithm. Application to data generated with a validated numerical model indicates that the parameters derived by fitting the pressure data are close to the known values which were used to generate the numerical data. Finally, application to data measured in the human thoracic aorta indicates the potential usefulness of the separation.

Arterial pulse wave velocity in coronary arteries

Pulse wave velocity is related to arterial stiffness. Pulse wave velocity changes with age and disease and is a useful indicator of cardiovascular disease. Different methods are used for evaluating pulse wave velocity in systemic vessels, but none is applicable to coronary arteries. In this study we first compare values of wave speed (c) calculated from measurements of pressure (P) and velocity (U) using different analytical methods: PU-loop, beta stiffness parameter, characteristic impedance, foot-to-foot method, and the sum of squares (Sigma(2)), a novel way of calculating the wave speed (calculated from the square root of the sum of the ratio of the dP(2) and dU(2) over a complete cardiac cycle). Results from human measurements using Doppler ultrasound on carotid arteries show good correlation between the PU-loop method, beta stiffness parameter and Sigma(2). Characteristic impedance calculations show the greatest variation of all methods. The Sigma(2) method was further assessed in vitro for use in coronary vessels. Pressure and velocity measurements were obtained from human coronary arteries following angiographic studies. The measurements were made invasively by co-locating two wires with pressure and velocity transducers. Pressure and velocity data in the left anterior descending, circumflex, left main stem and right coronary arteries were acquired simultaneously along with the ECG signal. Wave speed was calculated using Sigma(2). Wave intensity analysis was used to determine forward and backward traveling waves at different times in different locations, for which wave speed, approximate distance and timings between waves need to be known.