Probiotics for cure of Helicobacter pylori infection: A review

Human physiology normally contains pathogenic and non-pathogenic microorganisms in the gastrointestinal flora. Disturbance of these microorganism balance results in the formation of infection. Extensive use of antibiotics for cure of these disturbances like Helicobacter pylori (H. pylori) infection leads to patient discomfort and associated side-effects. There is a need to adopt adjunct or alternative approach in order to minimize such conditions. Probiotics is one of the potential therapies to cure gastrointestinal discomforts especially associated with H. pylori. It competes through non-immune and immune systems. This review article concludes that probiotics are used to eradicate the infection at increased rate, and decreased associated side-effects are caused by triple therapy. A proper evaluation of these probiotics is demanded before their use in future as a commercial product. Furthermore, their effect on immune system requires more research work so that their usage for other chronic disorders can also be considered.     

Synthesis,characterization and comparison of polythiophene–carbon nanocomposite materials as Pt electrocatalyst supports for fuel cell applications

A novel polymer–carbon (PTh–C) nanocomposites containing different percentages of polythiophene (10, 20 and 50% (w/w)) and carbon (Vulcan XC-72) was prepared by a facile solution dispersion method and used to support platinum nanoparticles. The effect of using different percentages of polythiophene in nanocomposites and subsequently prepared electrocatalysts was investigated. The resultant electrocatalysts were extensively characterized by physical (X-ray diffraction (XRD) and transmission electron microscopy (TEM)) and electrochemical (cyclic voltammetry (CV)) techniques. The TEM results showed that the fine Pt nanoparticles prepared by ethylene glycol (EG) method were distributed on the surface of the 50% PTh–C nanocomposites successfully. From the XRD patterns, the average size of dispersed Pt nanoparticles with the face-centered cubic (fcc) structure on 50% PTh–C, 20% PTh–C, 10% PTh–C and carbon were about 4.9, 5.2, 5.4 and 6.1 nm, respectively. The conductivity of PTh–C with different percentages of pure PTh was compared with the conductivity of the corresponding support of pure PTh. It is observed that the conductivity of 50% PTh–C nanocomposites is about 600 times higher than that of pure PTh. Finally, CV measurements of hydrogen and methanol oxidations indicated that Pt/50% PTh–C had a higher electrochemical surface area and higher catalytic activity for methanol oxidation reaction compared to other electrocatalysts. These measurements showed that the Pt/50% PTh–C electrocatalyst by the value of 3.85 had higher \(I_{\mathrm{f}}/I_{\mathrm{b}}\) ratio with respect to Pt/10% PTh–C and Pt/20% PTh–C by the values of 2.66 and 2.0, respectively.     

Effects of silymarin on biochemical and oxidative stress markers in end‐stage renal disease patients undergoing peritoneal dialysis

Introduction End‐stage renal disease (ESRD) patients especially those undergoing dialysis are vulnerable to several complications, in particular those related to oxidative stress. Silymarin is an herbal medicine commonly used as an antioxidant in different pathologies. Methods To evaluate the effect of silymarin on biochemical and oxidative stress markers, 50 ESRD patients undergoing peritoneal dialysis were randomly divided into two groups of silymarin (n = 28) and control (n = 22) and received silymarin (140 mg every 8 hours) or placebo for 2 months, respectively. Ferric reducing antioxidant power and total 8‐iso‐prostaglandin F_2α were measured in plasma, while catalase enzyme activity was measured in erythrocytes of both groups before and after treatment. Findings Ferric reducing antioxidant power values after treatment were significantly decreased in silymarin group compared to before treatment values (17.2 ± 2.9 and 15.9 ± 3.1 µM equivalent of quercetin/dL, respectively, P < 0.05). Conversely, catalase levels were increased 17.3% after silymarin consumption, while it was decreased 9.1% in control group. Further, hemoglobin (from 10.94 ± 2.17 to 11.54 ± 2.03 g/dL, P < 0.05) and albumin levels (from 3.48 ± 0.67 to 3.61 ± 0.53 g/dL, P < 0.05) were significantly increased after silymarin administration. Discussion It is concluded that silymarin could be regarded as a supplementary therapy for ESRD patients undergoing peritoneal dialysis in order to reduce complications.     

Stochastic multiscale fracture analysis of three-dimensional functionally graded composites

A new moment-modified polynomial dimensional decomposition (PDD) method is presented for stochastic multiscale fracture analysis of three-dimensional, particle-matrix, functionally graded materials (FGMs) subject to arbitrary boundary conditions. The method involves Fourier-polynomial expansions of component functions by orthonormal polynomial bases, an additive control variate in conjunction with Monte Carlo simulation for calculating the expansion coefficients, and a moment-modified random output to account for the effects of particle locations and geometry. A numerical verification conducted on a two-dimensional FGM reveals that the new method, notably the univariate PDD method, produces the same crude Monte Carlo results with a five-fold reduction in the computational effort. The numerical results from a three-dimensional, edge-cracked, FGM specimen under a mixed-mode deformation demonstrate that the statistical moments or probability distributions of crack-driving forces and the conditional probability of fracture initiation can be efficiently generated by the univariate PDD method. There exist significant variations in the probabilistic characteristics of the stress-intensity factors and fracture-initiation probability along the crack front. Furthermore, the results are insensitive to the subdomain size from concurrent multiscale analysis, which, if selected judiciously, leads to computationally efficient estimates of the probabilistic solutions.     

Generation of Daily and Hourly Weather Variables for use in Climate Change Vulnerability Assessment

This paper evaluates the impact of climate change, as projected by two Global Climate Models (GCMs) on the occurrence of extreme precipitation events in the Upper Thames River Basin in the Canadian province of Ontario. The modelling approach presented herein involves a two-stage process of generating daily weather data followed by disaggregation to an hourly time step of select variables for some events. Monthly change fields for three weather variables (maximum temperature, minimum temperature, and precipitation) were obtained from the output of two GCMs. The historical data set is modified by applying change fields to the weather variables simultaneously and then using this as the driving data set for an improved K-nearest neighbour weather-generating model. Weather sequences representative of climatic conditions in 2050 were simulated. Disaggregation of precipitation data is carried out using a new method that is a hybrid key site approach. A distinct practical advantage of the approach presented here is that extreme wet and dry spells are simulated, which is crucial for evaluation of effective flood and drought management policies for the basin.     

Data dissemination in wireless broadcast channels: Network coding versus cooperation

Network coding and cooperative diversity have each extensively been explored in the literature as a means to substantially improve the performance of wireless networks. Yet, little work has been conducted to compare their performance under a common framework. Our goal in this paper is to fill in this gap. Specifically, we consider a single-hop wireless network consisting of a base station and N receivers. We perform an asymptotic analysis, as N → ∞, of the expected delay associated with the broadcasting of a file consisting of K packets. We show that if K is fixed, cooperation outperforms network coding, in the sense that the expected delay is proportional to K (and thus within a constant factor of the optimal delay) in the former case while it grows logarithmically with N in the latter case. On the other hand, if K grows with N at a rate at least as fast as (logN)^r, for r ≫ 1, then we show that the average delay of network coding is also proportional to K and lower than the average delay of cooperation if the packet error probability is smaller than 0.36. Our analytical findings are validated through extensive numerical simulations.     

Frame size adaptation for indoor wireless networks

An optimal frame size predictor for indoor wireless networks is proposed and studied. Kalman filtering is employed to predict the optimal frame size in order to obtain a guaranteed channel utilisation. Through simulation, it is shown that the proposed algorithm can substantially lower the estimation error     

Influence of polymerization conditions on the template free synthesis of nanoparticles of poly (1-naphthylamine)

The present article highlights some preliminary investigations on the role of oxidant and medium on the nanostructured morphology of poly (1-naphthylamine) (PNA) prepared via chemical oxidative polymerization. Results revealed that the nano morphology can be controlled under appropriate preparative conditions. The particle sizes were obtained in the range of 5–50 nm exhibiting a much smaller range than polyaniline (PANI) nanoparticles synthesized under similar conditions.     

Microscopic image analysis for quantitative measurement and featureidentification of normal and cancerous colonic mucosa

The development of an automated algorithm for the categorization of normal and cancerous colon mucosa is reported. Six features based on texture analysis were studied. They were derived using the co-occurrence matrix and were angular second moment, entropy, contrast, inverse difference moment, dissimilarity, and correlation. Optical density was also studied. Forty-four normal images and 58 cancerous images from sections of the colon were analyzed. These two groups were split equally into two subgroups: one set was used for supervised training and the other to test the classification algorithm. A stepwise selection procedure showed that correlation and entropy were the features that discriminated most strongly between normal and cancerous tissue (P<0.0001). A parametric linear-discriminate function was used to determine the classification rule. For the training set, a sensitivity and specificity of 93.1% and 81.8%, respectively, were achieved, with an overall accuracy of 88.2%. These results mere confirmed with the test set, with a sensitivity and specificity of 93.1% and 86.4%, respectively, and an overall accuracy of 90.2%