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BACKGROUND: Young-onset hypertension is defined as hypertension diagnosed before the age of 40 years. Aortic pulse wave velocity is an indication of aortic stiffness. MRI assessment has been well verified compared to invasive pressure recordings for evaluating aortic pulse wave velocity. In this study, we aimed to determine whether aortic stiffness played a role in the aetiology of young-onset hypertension by calculating pulse wave velocity using MRI. METHODS: We enrolled 20 patients diagnosed with young-onset hypertension and 20 volunteers without hypertension. Aortic pulse wave velocity was measured by cardiac MRI and protocol for the pulse wave velocity measurement involved the use of a 1.5 T scanner to acquire velocity-encoded, phase-contrast transverse aortic cine images. Sagittal oblique images used to measure the distance (ΔX) between the ascending aorta and descending aorta for the calculation of pulse wave velocity. The aortic flow versus time curves of ascending aorta and descending aorta were automatically obtained from the phase-contrast MRI images. Using these curves, the temporal shift (ΔT) was measured by Segment Medviso. FINDINGS: The mean pulse wave velocity was 8.72 (SD 2.34) m/second (range: 7-12.8 m/second) for the patient group and 5.96 (standard deviation 1.86) m/second (range: 4.8-7.1 m/second) for the control group. The pulse wave velocity values were significantly higher in the patient group compared to the control group (p < 0.001). INTERPRETATION: Aortic stiffness may play a role in the aetiology of young-onset hypertension and serve as a non-invasive and reliable screening tool when measured by MRI.

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Experiments using conventional experimental approaches to capture the dynamics of ion channels are not always feasible, and even when possible and feasible, some can be time-consuming. In this work, the ionic current-time dynamics during cardiac action potentials (APs) are predicted from a single AP waveform by means of artificial neural networks (ANNs). The data collection is accomplished by the use of a single-cell model to run electrophysiological simulations in order to identify ionic currents based on fluctuations in ion channel conductance. The relevant ionic currents, as well as the corresponding cardiac AP, are then calculated and fed into the ANN algorithm, which predicts the desired currents solely based on the AP curve. The validity of the proposed methodology for the Bayesian approach is demonstrated by the R (validation) scores obtained from training data, test data, and the entire data set. The Bayesian regularization's (BR) strength and dependability are further supported by error values and the regression presentations, all of which are positive indicators. As a result of the high convergence between the simulated currents and the currents generated by including the efficacy of a developed Bayesian solver, it is possible to generate behavior of ionic currents during time for the desired AP waveform for any electrical excitable cell.

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Mathematical models for the action potential (AP) generation of the electrically excitable cells including the heart are involved different mechanisms including the voltage-dependent currents with nonlinear time- and voltage-gating properties. From the shape of the AP waveforms to the duration of the refractory periods or heart rhythms are greatly affected by the functions describing the features or the quantities of these ion channels. In this work, a mathematical measure to analyze the regional contributions of voltage-gated channels is defined by dividing the AP into phases, epochs, and intervals of interest. The contribution of each time-dependent current for the newly defined cardiomyocyte model is successfully calculated and it is found that the contribution of dominant ion channels changes substantially not only for each phase but also for different regions of the cardiac AP. Besides, the defined method can also be applied in all Hodgkin-Huxley types of electrically excitable cell models to be able to understand the underlying dynamics better.

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Neuronal noise is a major factor affecting the communication between coupled neurons. In this work, we propose a statistical toolset to infer the coupling between two neurons under noise. We estimate these statistical dependencies from data which are generated by a coupled Hodgkin-Huxley (HH) model with additive noise. To infer the coupling using observation data, we employ copulas and information-theoretic quantities, such as the mutual information (MI) and the transfer entropy (TE). Copulas and MI between two variables are symmetric quantities, whereas TE is asymmetric. We demonstrate the performances of copulas and MI as functions of different noise levels and show that they are effective in the identification of the interactions due to coupling and noise. Moreover, we analyze the inference of TE values between neurons as a function of noise and conclude that TE is an effective tool for finding out the direction of coupling between neurons under the effects of noise.

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Mathematical action potential (AP) modeling is a well-established but still-developing area of research to better understand physiological and pathological processes. In particular, changes in AP mechanisms in the isoproterenol (ISO) -induced hypertrophic heart model are incompletely understood. Here we present a mathematical model of the rat AP based on recordings from rat ventricular myocytes. In our model, for the first time, all channel kinetics are defined with a single type of function that is simple and easy to apply. The model AP and channels dynamics are consistent with the APs recorded from rats for both Control (absence of ISO) and ISO-treated cases. Our mathematical model helps us to understand the reason for the prolongation in AP duration after ISO application while ISO treatment helps us to validate our mathematical model. We reveal that the smaller density and the slower gating kinetics of the transient K+ current help explain the prolonged AP duration after ISO treatment and the increasing amplitude of the rapid and the slow inward rectifier currents also contribute to this prolongation alongside the flux in Ca2+ currents. ISO induced an increase in the density of the Na+ current that can explain the faster upstroke. We believe that AP dynamics from rat ventricular myocytes can be reproduced very well with this mathematical model and that it provides a powerful tool for improved insights into the underlying dynamics of clinically important AP properties such as ISO application.

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