Pomegranate juice is a rich source of ellagitannins (ETs) believed to contribute to a wide range of pomegranate’s health benefits. While a lot of experimental studies have been devoted to Alzheimer disease and hypoxic-ischemic brain injury, our knowledge of pomegranate’s effects against Parkinson’s disease (PD) is very limited. It is suggested that its neuroprotective effects are mediated by ETs-derived metabolites—urolithins. In this study, we examined the capability of pomegranate juice for protection against PD in a rat model of parkinsonism induced by rotenone. To evaluate its efficiency, assessment of postural instability, visualization of neurodegeneration, determination of oxidative damage to lipids and α-synuclein level, as well as markers of antioxidant defense status, inflammation, and apoptosis, were performed in the midbrain. We also check the presence of plausible active pomegranate ETs-derived metabolite, urolithin A, in the plasma and brain. Our results indicated that pomegranate juice treatment provided neuroprotection as evidenced by the postural stability improvement, enhancement of neuronal survival, its protection against oxidative damage and α-synuclein aggregation, the increase in mitochondrial aldehyde dehydrogenase activity, and maintenance of antiapoptotic Bcl-xL protein at the control level. In addition, we have provided evidence for the distribution of urolithin A to the brain.

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We present a new programmable neighborhood mechanism for hardware implemented Kohonen self-organizing maps (SOMs) with three different map topologies realized on a single chip. The proposed circuit comes as a fully parallel and asynchronous architecture. The mechanism is very fast. In a medium sized map with several hundreds neurons implemented in the complementary metal-oxide semiconductor 0.18 μm technology, all neurons start adapting the weights after no more than 11 ns. The adaptation is then carried out in parallel. This is an evident advantage in comparison with the commonly used software-realized SOMs. The circuit is robust against the process, supply voltage and environment temperature variations. Due to a simple structure, it features low energy consumption of a few pJ per neuron per a single learning pattern. In this paper, we discuss different aspects of hardware realization, such as a suitable selection of the map topology and the initial neighborhood range, as the optimization of these parameters is essential when looking from the circuit complexity point of view. For the optimal values of these parameters, the chip area and the power dissipation can be reduced even by 60% and 80%, respectively, without affecting the quality of learning.

Melissa officinalis (MO, English: lemon balm, Lamiaceae), one of the oldest and still most popular aromatic medicinal plants, is used in phytomedicine for the prevention and treatment of nervous disturbances. The aim of our study was to assess the effect of subchronic (28-fold) administration of a 50% ethanol extract of MO leaves (200 mg/kg, p.o.) compared with rosmarinic acid (RA, 10 mg/kg, p.o.) and huperzine A (HU, 0.5 mg/kg, p.o.) on behavioral and cognitive responses in scopolamine-induced rats. The results were linked with acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), and beta-secretase (BACE-1) mRNA levels and AChE and BuChE activities in the hippocampus and frontal cortex of rats. In our study, MO and HU, but not RA, showed an improvement in long-term memory. The results were in line with mRNA levels, since MO produced a decrease of AChE mRNA level by 52% in the cortex and caused a strong significant inhibition of BACE1 mRNA transcription (64% in the frontal cortex; 50% in the hippocampus). However, the extract produced only an insignificant inhibition of AChE activity in the frontal cortex. The mechanisms of MO action are probably more complicated, since its role as a modulator of beta-secretase activity should be taken into consideration.

a b s t r a c tAn efficient transistor level implementation of a flexible, programmable Triangular Function (TF) that can be used as a Triangular Neighborhood Function (TNF) in ultra-low power, self-organizing maps (SOMs) realized as Application-Specific Integrated Circuit (ASIC) is presented. The proposed TNF block is a component of a larger neighborhood mechanism, whose role is to determine the distance between the winning neuron and all neighboring neurons. Detailed simulations carried out for the software model of such network show that the TNF forms a good approximation of the Gaussian Neighborhood Function (GNF), while being implemented in a much easier way in hardware. The overall mechanism is very fast.In the CMOS 0.18 µm technology, distances to all neighboring neurons are determined in parallel, within the time not exceeding 11 ns, for an example neighborhood range, R, of 15. The TNF blocks in particular neurons require another 6 ns to calculate the output values directly used in the adaptation process. This is also performed in parallel in all neurons. As a result, after determining the winning neuron, the entire map is ready for the adaptation after the time not exceeding 17 ns, even for large numbers of neurons. This feature allows for the realization of ultra low power SOMs, which are hundred times faster than similar SOMs realized on PC. The signal resolution at the output of the TNF block has a dominant impact on the overall energy consumption as well as the silicon area. Detailed system level simulations of the SOM show that even for low resolutions of 3 to 6 bits, the learning abilities of the SOM are not affected. The circuit performance has been verified by means of transistor level Hspice simulations carried out for different transistor models and different values of supply voltage and the environment temperature -a typical procedure completed in case of commercial chips that makes the obtained results reliable.