The impact of apelin and relaxin plasma levels in masked hypertension and white coat hypertension.

Masked hypertension (HTN) and white coat hypertension represent two reverse forms of clinical HTN with questionable prognostic significance. Recent evidence supports that low apelin and relaxin plasma levels contribute to vascular damage accelerating atherogenesis and predisposing to HTN and cardiovascular (CV) events. The aim of this study was to compare apelin and relaxin plasma levels between patients with masked hypertension (MH) and those with white coat HTN (WCH). Overall, 130 patients not receiving antihypertensive therapy were studied. All patients underwent 24-hour ambulatory BP monitoring (ABPM) and office BP measurements. Plasma apelin and relaxin levels were measured by ELISA method. According to BP recordings, 24 subjects had MH (group A) and 32 had WCH (group B). Apelin (200 ± 111 pg/mL vs 305 ± 127 pg/mL, P < 0.01) and relaxin (35.2 ± 6.7 pg/mL vs 46.8 ± 23.6 pg/mL, P < 0.01) plasma levels were significantly lower in patients with MH compared to those with WCH, respectively. In conclusion, our findings showed that patients with MH had significantly lower apelin and relaxin levels compared to those with WCH. This observation implies an additional prognostic role for adipokines supporting the concept that MH is closer to essential HTN whereas WCH is a more benign condition.

Mitral-Aortic Intervalvular Fibrosa Pseudoaneurysm.

Pseudoaneurysm of the mitral aortic intervalvular fibrosa (MAIVF-P) usually ensues as a complication of endocarditis or aortic valve surgery. When large, symptomatic or related to complications (rupture, compression of adjacent structures, embolic events, mitral regurgitation or heart failure) it warrants surgical excision. The natural course of uncomplicated/asymptomatic MAIVF-Ps is largely unknown since most patients are offered surgery. Increased surgical risk imposed by repeat operations in the majority of these patients is an important consideration and conservative treatment should not be excluded in selected cases. Herein we present two illustrative cases of MAIVF-P manifesting with significant arrhythmogenesis and complex endocarditis respectively. Both patients were managed conservatively. By briefly reviewing the existing literature, we discuss important diagnostic and therapeutic issues for MAIVF-Ps. To our knowledge complex ventricular arrhythmia has not been previously described as a prominent manifestation of MAIVF-P.

Control of synchronization of brain dynamics leads to control of epileptic seizures in rodents.

We have designed and implemented an automated, just-in-time stimulation, seizure control method using a seizure prediction method from nonlinear dynamics coupled with deep brain stimulation in the centromedial thalamic nuclei in epileptic rats. A comparison to periodic stimulation, with identical stimulation parameters, was also performed. The two schemes were compared in terms of their efficacy in control of seizures, as well as their effect on synchronization of brain dynamics. The automated just-in-time (JIT) stimulation showed reduction of seizure frequency and duration in 5 of the 6 rats, with significant reduction of seizure frequency (>50%) in 33% of the rats. This constituted a significant improvement over the efficacy of the periodic control scheme in the same animals. Actually, periodic stimulation showed an increase of seizure frequency in 50% of the rats, reduction of seizure frequency in 3 rats and significant reduction in 1 rat. Importantly, successful seizure control was highly correlated with desynchronization of brain dynamics. This study provides initial evidence for the use of closed-loop feedback control systems in epileptic seizures combining methods from seizure prediction and deep brain stimulation.

Homeostasis of brain dynamics in epilepsy: a feedback control systems perspective of seizures.

In an effort to understand basic functional mechanisms that can produce epileptic seizures, some key features are introduced in coupled lumped-parameter neural population models that produce "seizure"-like events and dynamics similar to the ones during the route of the epileptic brain towards seizures. In these models, modified from existing ones in the literature, internal feedback mechanisms are incorporated to maintain the normal low level of synchronous behavior in the presence of coupling variations. While the internal feedback is developed using basic feedback systems principles, it is also functionally equivalent to actual neurophysiological mechanisms such as homeostasis that act to maintain normal activity in neural systems that are subject to extrinsic and intrinsic perturbations. Here it is hypothesized that a plausible cause of seizures is a pathology in the internal feedback action; normal internal feedback quickly regulates an abnormally high coupling between the neural populations, whereas pathological internal feedback can lead to "seizure"-like high amplitude oscillations. Several external seizure-control paradigms, that act to achieve the operational objective of maintaining normal levels of synchronous behavior, are also developed and tested in this paper. In particular, closed-loop "modulating" control with predefined stimuli, and closed-loop feedback decoupling control are considered. Among these, feedback decoupling control is the consistently successful and robust seizure-control strategy. The proposed model and remedies are consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology. The results from the analysis of these models have two key implications, namely, developing a basic theory for epilepsy and other brain disorders, and the development of a robust seizure-control device through electrical stimulation and/or drug intervention modalities.

Measuring resetting of brain dynamics at epileptic seizures: application of global optimization and spatial synchronization techniques.

Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL(max)), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion's sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL(max), a measure of phase (ϕ(max)) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is shown that using either of the two resetting criteria, and for all three dynamical measures, dynamical resetting at seizures occurs with a significantly higher probability (α = 0.05) than resetting at randomly selected non-seizure points in days of EEG recordings per patient. It is also shown that dynamical resetting at seizures using time constants of STL(max) synchronization/desynchronization occurs with a higher probability than using the other synchronization measures, whereas dynamical resetting at seizures using the level of synchronization/desynchronization criterion is detected with similar probability using any of the three measures of synchronization. These findings show the robustness of seizure resetting with respect to measures of EEG dynamics and criteria of resetting utilized, and the critical role it might play in further elucidation of ictogenesis, as well as in the development of novel treatments for epilepsy.

Controlling synchronization in a neuron-level population model.

We have studied coupled neural populations in an effort to understand basic mechanisms that maintain their normal synchronization level despite changes in the inter-population coupling levels. Towards this goal, we have incorporated coupling and internal feedback structures in a neuron-level population model from the literature. We study two forms of internal feedback--regulation of excitation, and compensation of excessive excitation with inhibition. We show that normal feedback actions quickly regulate/compensate an abnormally high coupling between the neural populations, whereas a pathology in these feedback actions can lead to abnormal synchronization and "seizure"-like high amplitude oscillations. We then develop an external control paradigm, termed feedback decoupling, as a robust synchronization control strategy. The external feedback decoupling controller acts to achieve the operational objective of maintaining normal-level synchronous behavior irrespective of the pathology in the internal feedback mechanisms. Results from such an analysis have an interesting physical interpretation and specific implications for the treatment of diseases such as epilepsy. The proposed remedy is consistent with a variety of recent observations in the human and animal epileptic brain, and with theories from nonlinear systems, adaptive systems, optimization, and neurophysiology.