This paper presents a physiological long-term model of the cardiovascular system. It integrates the previous models developed by Guyton, Uttamsingh and Coleman. Additionally it introduces mechanisms of direct effects of the renal sympathetic nerve activity (rsna) on tubular sodium reabsorption and renin secretion in accordance with experimental data from literature. The resulting mathematical model constitutes the first long-term model of the cardiovascular system accounting for the effects of rsna on kidney functions in such detail. The objective of developing such a model is to observe the consequences of long-term rsna increase and impairment of rsna inhibition under volume loading. This model provides an understanding of the rsna-related mechanisms, which cause mean arterial pressure increase in hypertension and total sodium amount increase (sodium retention) in congestive heart failure, nephrotic syndrome and cirrhosis.

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Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time-frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time-frequency analysis of PCG signals.

The aim of this study is to analyze the differences between early and delayed use of low-level laser therapy (LLLT) in functional and morphological recovery of the peripheral nerve. Thirty male Wistar rats were divided into three groups after the sciatic nerve was crushed: (1) control group without laser treatment, (2) early group with laser treatment started immediately after surgery and lasted 14 days, and (3) delayed group with laser treatment starting on the postoperative day 7 and lasted until day 21. A 650-nm diode laser (model: DH650-24-3(5), Huanic, China) with an output power of 25 mW exposed transcutaneously at three equidistant points on the surgical mark corresponding to the crushed nerve. The length of the laser application was calculated as 57 s to satisfy approximately 10 J/cm(2). A Sciatic Functional Index (SFI) was used to evaluate functional improvement in groups at pre- and post-surgery (on days 7, 14, and 21). Compound action potential (CAP) was measured after the sacrifice and histological examination was performed for all groups. SFI results showed that there was no significant difference between groups at different days (p > 0.05). On the other hand, the latency of CAP decreased significantly (p < 0.05) in the delayed group. Histological examination confirmed that the number of mononuclear cells was lower (p < 0.05) in both early and delayed groups. In conclusion, results supported the hypothesis that LLLT could accelerate the rate of recovery of injured peripheral nerves in this animal model. Though both laser groups had positive outcomes, delayed group showed better recovery.

In this article, we introduce the Bioinspired Neuroprosthetic Design Environment (BNDE) as a practical platform for the development of novel brain–machine interface (BMI) controllers, which are based on spiking model neurons. We built the BNDE around a hard real-time system so that it is capable of creating simulated synapses from extracellularly recorded neurons to model neurons. In order to evaluate the practicality of the BNDE for neuroprosthetic control experiments, a novel, adaptive BMI controller was developed and tested using real-time closed-loop simulations. The present controller consists of two in silico medium spiny neurons, which receive simulated synaptic inputs from recorded motor cortical neurons. In the closed-loop simulations, the recordings from the cortical neurons were imitated using an external, hardware-based neural signal synthesizer. By implementing a reward-modulated spike timing-dependent plasticity rule, the controller achieved perfect target reach accuracy for a two-target reaching task in one-dimensional space. The BNDE combines the flexibility of software-based spiking neural network (SNN) simulations with powerful online data visualization tools and is a low-cost, PC-based, and all-in-one solution for developing neurally inspired BMI controllers. We believe that the BNDE is the first implementation, which is capable of creating hybrid biological/in silico neural networks for motor neuroprosthetic control and utilizes multiple CPU cores for computationally intensive real-time SNN simulations.