Uncoupling protein-2 prevents neuronal death and diminishes brain dysfunction after stroke and brain trauma

Whereas uncoupling protein 1 (UCP-1) is clearly involved in thermogenesis, the role of UCP-2 is less clear. Using hybridization, cloning techniques and cDNA array analysis to identify inducible neuroprotective genes, we found that neuronal survival correlates with increased expression of Ucp2. In mice overexpressing human UCP-2, brain damage was diminished after experimental stroke and traumatic brain injury, and neurological recovery was enhanced. In cultured cortical neurons, UCP-2 reduced cell death and inhibited caspase-3 activation induced by oxygen and glucose deprivation. Mild mitochondrial uncoupling by 2,4-dinitrophenol (DNP) reduced neuronal death, and UCP-2 activity was enhanced by palmitic acid in isolated mitochondria. Also in isolated mitochondria, UCP-2 shifted the release of reactive oxygen species from the mitochondrial matrix to the extramitochondrial space. We propose that UCP-2 is an inducible protein that is neuroprotective by activating cellular redox signaling or by inducing mild mitochondrial uncoupling that prevents the release of apoptogenic proteins.     

Structure-activity study of phosphoramido acid esters as acetylcholinesterasf inhibitors

Phosphoramido acid esters (CH₃)₂NP(O)X(p-OC₆H₄-CH₃) (containing P-Cl (1), P-O (2), P-F (3), P-CN (5), and P-N (4,6) bonds, X for 2, 4 and 6 is OCH₃, (C₂H₅)₂N and morpholin) have been synthesized to investigate the structure-activity study of AChE enzyme inhibition, through the parameters logP, δ³¹P and IC₅₀. After their characterization by ³¹P, ³¹P{¹H}, ¹³C, ¹H NMR, IR and mass spectroscopy, the parameters logP and δ³¹P (³¹P chemical shift in NMR) were used to evaluated the lipophilicity and electronical properties. The ability of compounds to inhibit human AChE was predicted by PASS software (version 1.193), and experimentally evaluated by a modified Ellman's assay.     

Adaptive neuro-fuzzy inference system: Estimation of soil aggregates stability

In present study, the capabilities of multiple linear regression (MLR) and adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions (PTFs) for estimating geometric mean diameter (GMD) and mean weight diameter (MWD), from routine soil properties and combination of routine soil properties and fractal dimension of aggregates were evaluated. For this reason 101 samples were collected form the Northwest of Iran and some their properties such as soil texture, pH, cation exchange capacity (CEC), and organic matter (OM), fractal dimension of aggregates between number-diameter (Dn), mass-diameter (Dmt), and bulk density-diameter (Dmy) were determined and used as an input variables for determining of mean weight diameter (MWD) and geometric mean diameter (GMD) by MLR and ANFIS PTFs. Results showed that the application of fractal dimension of aggregates as a predictor in two methods improved the accuracy of PTFs. As well as, results showed that ANFIS have greater potential for determination of the relationships between soil aggregate stability indices and other soil properties in compared with MLR. Therefore using of adaptive neuro-fuzzy inference system (ANFIS) in developing pedotransfer functions is recommended.     

Synthesis and anticonvulsant activity of new 2-substituted-5- [2-(2-fluorophenoxy)phenyl]-1,3,4-oxadiazoles and 1,2,4-triazoles

A series of new 2-substituted-5-[2-(2-fluorophenoxy)phenyl]-1,3,4-oxadiazoles has been synthesized and screened for their anticonvulsant activities. Compound 3 shows considerable anticonvulsant activity both in PTZ and MES models. It seems that this effect is mediated by benzodiazepine receptors and other unknown mechanism, respectively.     

Hemodynamics-driven mathematical model of first and second heart sound generation

We propose a novel, two-degree of freedom mathematical model of mechanical vibrations of the heart that generates heart sounds in CircAdapt, a complete real-time model of the cardiovascular system. Heart sounds during rest, exercise, biventricular (BiVHF), left ventricular (LVHF) and right ventricular heart failure (RVHF) were simulated to examine model functionality in various conditions. Simulated and experimental heart sound components showed both qualitative and quantitative agreements in terms of heart sound morphology, frequency, and timing. Rate of left ventricular pressure (LV dp/dt_max) and first heart sound (S1) amplitude were proportional with exercise level. The relation of the second heart sound (S2) amplitude with exercise level was less significant. BiVHF resulted in amplitude reduction of S1. LVHF resulted in reverse splitting of S2 and an amplitude reduction of only the left-sided heart sound components, whereas RVHF resulted in a prolonged splitting of S2 and only a mild amplitude reduction of the right-sided heart sound components. In conclusion, our hemodynamics-driven mathematical model provides fast and realistic simulations of heart sounds under various conditions and may be helpful to find new indicators for diagnosis and prognosis of cardiac diseases.New & noteworthyTo the best of our knowledge, this is the first hemodynamic-based heart sound generation model embedded in a complete real-time computational model of the cardiovascular system. Simulated heart sounds are similar to experimental and clinical measurements, both quantitatively and qualitatively. Our model can be used to investigate the relationships between heart sound acoustic features and hemodynamic factors/anatomical parameters.     

A new approach to blood flow simulation in vascular networks

A proper analysis of blood flow is contingent upon accurate modelling of the branching pattern and vascular geometry of the network of interest. It is challenging to reconstruct the entire vascular network of any organ experimentally, in particular the pulmonary vasculature, because of its very high number of vessels, complexity of the branching pattern and poor accessibility in vivo. The objective of our research is to develop an innovative approach for the reconstruction of the full pulmonary vascular tree from available morphometric data. Our method consists of the use of morphometric data on those parts of the pulmonary vascular tree that are too small to reconstruct by medical imaging methods. This method is a three-step technique that reconstructs the entire pulmonary arterial tree down to the capillary bed. Vessels greater than 2 mm are reconstructed from direct volume and surface analysis using contrast-enhanced computed tomography. Vessels smaller than 2 mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray's laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray's laws to every vessel bifurcation simultaneously leads to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the ?rst capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-de?ned Strahler system, are assigned. In conclusion, the present model provides a morphological foundation for future analysis of blood flow in the pulmonary circulation     

Improving ECG classification accuracy using an ensemble of neural network modules

This paper illustrates the use of a combined neural network model based on Stacked Generalization method for classification of electrocardiogram (ECG) beats. In conventional Stacked Generalization method, the combiner learns to map the base classifiers' outputs to the target data. We claim adding the input pattern to the base classifiers' outputs helps the combiner to obtain knowledge about the input space and as the result, performs better on the same task. Experimental results support our claim that the additional knowledge according to the input space, improves the performance of the proposed method which is called Modified Stacked Generalization. In particular, for classification of 14966 ECG beats that were not previously seen during training phase, the Modified Stacked Generalization method reduced the error rate for 12.41% in comparison with the best of ten popular classifier fusion methods including Max, Min, Average, Product, Majority Voting, Borda Count, Decision Templates, Weighted Averaging based on Particle Swarm Optimization and Stacked Generalization.