Therapeutic role of glucogalactan polysaccharide extracted from Agaricus bisporus on trimethyltin chloride induced neuropathy in rats

Trimethyltin (TMT) chloride induces limbic system neuro-degeneration, resulting in behavioral alterations like cognitive deficits. This study investigates the effect of glucogalactan polysaccharide (GA) extract, which was purified from the roots of Agaricus bisporus mushroom, on trimethyltin chloride (TMT) induced neuropathy in rats. Adult male rats (200 ±10 g) were divided into four groups that were fed with basal diet throughout the experiment (28 days). The first group (G I) was control group, the second group (GII) was treated with 300 mg/kg BW GA intraperitoneally (i.p.) daily for 28 day. The third group (GIII) was administered i.p. with 8.0 mg TMT /kg body weight (BW), and the fourth (G IV) was treated like the third group and was injected with GA 300 mg/kg BW daily for 28 day after 48 h of TMT. Many bioactive compounds, which were found in GA did not cause any changes in the second group compared to normal control group. The results reveal that GA given 48 h after TMT treatment has excellent neuropathy effect, lowers the average of MDA, HSP70, homocystein and the neurotransmitters in brain tissue homogenate; they were markedly reduced by the administration of GA to almost normal levels. Neurotransmitters and nitric oxide were significantly increased in the group given GA treatment compared to TMT group. The comet assay for DNA revealed that, TMT induced statistically significant (P<0.05) increase in the mean value of the tail length and tail moment of the rats. They significantly decreased after GA treatment, suggesting alleviated oxidative stress mediated by TMT. GA administered TMT-treated rats had improved brain histology, diminished level of MDA and nitric oxide (NO) in brain tissue and enhanced total antioxidant capacity in serum compared to TMT group. It could be concluded that GA plays a positive role in the improvement of brain function after TMT-induced neuropathy. Taken together, our results suggest that GA will be useful in developing strategies for protecting nervous system and improving the brain.Keywords: Trimethyltin, neuro-degeneration, Agaricusbisporus, glucogalactan

Emergence of scale-free close-knit friendship structure in online social networks

Despite the structural properties of online social networks have attracted much attention, the properties of the close-knit friendship structures remain an important question. Here, we mainly focus on how these mesoscale structures are affected by the local and global structural properties. Analyzing the data of four large-scale online social networks reveals several common structural properties. It is found that not only the local structures given by the indegree, outdegree, and reciprocal degree distributions follow a similar scaling behavior, the mesoscale structures represented by the distributions of close-knit friendship structures also exhibit a similar scaling law. The degree correlation is very weak over a wide range of the degrees. We propose a simple directed network model that captures the observed properties. The model incorporates two mechanisms: reciprocation and preferential attachment. Through rate equation analysis of our model, the local-scale and mesoscale structural properties are derived. In the local-scale, the same scaling behavior of indegree and outdegree distributions stems from indegree and outdegree of nodes both growing as the same function of the introduction time, and the reciprocal degree distribution also shows the same power-law due to the linear relationship between the reciprocal degree and in/outdegree of nodes. In the mesoscale, the distributions of four closed triples representing close-knit friendship structures are found to exhibit identical power-laws, a behavior attributed to the negligible degree correlations. Intriguingly, all the power-law exponents of the distributions in the local-scale and mesoscale depend only on one global parameter -- the mean in/outdegree, while both the mean in/outdegree and the reciprocity together determine the ratio of the reciprocal degree of a node to its in/outdegree.Comment: 48 pages, 34 figure

Predicting Land Cover Using a GIS-Based Markov Chain and Sea Level Inundation for a Coastal Area

New Hanover County, North Carolina, has been experiencing rapid population growth and is expected to continue this growth, leading to increased land use and development in the area. The county is also threatened by sea level rise (SLR) and its effects because of its coastal location and frequent occurrences of major storms and hurricanes. This study used a land change modeler to map the land cover change throughout the county over a period of 20 years, and predicted land cover distribution in the area in the years 2030 and 2050. Statistics revealed that the developed land in the area increased by 85 km2 between 2000 and 2010, and by 60 km2 between 2010 and 2020. Such land is predicted to increase by another 73 km2 by 2030, and 63 km2 by 2050. This increase in development is expected to occur mainly in the central area of the county and along the barrier islands. Modeling of SLR illustrated that the northwestern part of New Hanover County along the Cape Fear River, as well as the beach towns located on the barrier islands, are estimated be the most affected locations. Results indicate that sections of major highways throughout the county, including I-140 near downtown Wilmington and US-421 in Carolina Beach, may be inundated by SLR, which might delay residents during mandatory evacuations for emergency situations such as hurricanes. Some routes may be unusable, leading to traffic congestion on other routes, which may impede some residents from reaching safety before the emergency. Wrightsville Beach and Carolina Beach are estimated to have the highest levels of inundation, with 71.17% and 40.58% of their land being inundated under the most extreme SLR scenario of 3 m, respectively. The use of the present research approach may provide a practical, quick, and low-cost method in modeling rapidly growing urban areas along the eastern United States coastline and locating areas at potential risk of future SLR inundation