Influence of flavonols as in vitro on low density lipoprotein glycation

INTRODUCTION: The non-enzymatic glycation of Low density lipoprotein (LDL) is a naturally occurring chemical modification of apolipoprotein B as a result of condensation between lysine residues and glucose. Glycated LDL is poorly recognized by LDL receptors and initiates different processes that can be considered proatherogenic. Thus, LDL glycation may contribute in the increased atherosclerotic risk of patients with diabetes. The objective of this study was to investigate the effect of naturally occurring flavonols on LDL glycation in vitro. METHODS: In this study, LDL was isolated from EDTA-plasma by ultracentrifugation using a single step discontinuous gradient. Then, glucose was added to LDL and LDL glycation level was estimated in absence and presence of flavonols by sodium periodate assay. RESULTS: This study was showed that five flavonols: quercetin, myricetin, kaempferol, rutin and morin decreased LDL glycation in a dose-dependent manner. Also, it was demonstrated this nutrients decreased electrophoretic mobility of glycated LDL. CONCLUSION: The results of this investigation show that flavonols probably with their antioxidant properties inhibited LDL glycation and thus may have a role in ameliorating atherosclerotic risk of patients with diabetes mellitus.     

Cardinal temperatures for seed germination of wild barley,barley grass and hoary cress

It is necessary to understand the effects of environmental conditions, especially temperature, on weed seed germination patterns, in order to make comprehensive and effective weed management decisions. The effect of cardinal temperature (0°C, 5°C, 10°C, 15°C, 20°C, 25°C, 30°C, 35°C and 40°C) on the germination pattern of three noxious weeds, such as wild barley (Hordeum spontaneum Koch.), barley grass (H. murinum L.) and hoary cress (Cardaria draba L.) in wheat fields of Iran, was studied in three separate experiments. The experiments were conducted based on completely randomized design (CRD) with four replications. The result showed that the germination of all three species started at low temperatures and there was no significant difference in germination percentage (GP) at lower temperatures. About 50% of the weed population germinated within a short period when using a dent-like model indicating the high ability of these weeds to compete for space and resources. Segmented and dent-like models were used to evaluate the cardinal temperatures. The results showed that dent-like model was the preferred model to evaluate germination rate in relation to variable temperatures in hoary cress, while the segmented model was better in wild barley. However, there was no significant difference between the two models for barley grass. The results of this study showed that mechanical, cultural (pre-planting irrigation) and chemical practices could be more useful and effective at 10–30°C.     

Antibacterial activity of Pulicaria dysenterica extracts

Aqueous, methanolic and chloroformic extracts of Pulicaria dysenterica aerial parts were tested for their antibacterial activity using the disc-diffusion assay technique. The methanolic extract was found to be the most effective extract against three out of six tested bacteria. All of the extracts were active against Vibrio cholera.     

Evaluation of CRU TS,GPCC, AgMERRA,and AgCFSR meteorological datasets for estimating climate and crop variables: A case study of maize in Qazvin Province,Iran

In the past few decades, meteorological datasets from remote sensing techniques in agricultural and water resources management have been used by various researchers and managers. Based on the literature, meteorological datasets are not more accurate than synoptic stations, but their various advantages, such as spatial coverage, time coverage, accessibility, and free use, have made these techniques superior, and sometimes we can use them instead of synoptic stations. In this study, we used four meteorological datasets, including Climatic Research Unit gridded Time Series (CRU TS), Global Precipitation Climatology Centre (GPCC), Agricultural National Aeronautics and Space Administration Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), Agricultural Climate Forecast System Reanalysis (AgCFSR), to estimate climate variables, i.e., precipitation, maximum temperature, and minimum temperature, and crop variables, i.e., reference evapotranspiration, irrigation requirement, biomass, and yield of maize, in Qazvin Province of Iran during 1980-2009. At first, data were gathered from the four meteorological datasets and synoptic station in this province, and climate variables were calculated. Then, after using the AquaCrop model to calculate the crop variables, we compared the results of the synoptic station and meteorological datasets. All the four meteorological datasets showed strong performance for estimating climate variables. AgMERRA and AgCFSR had more accurate estimations for precipitation and maximum temperature. However, their normalized root mean square error was inferior to CRU for minimum temperature. Furthermore, they were all very efficient for estimating the biomass and yield of maize in this province. For reference evapotranspiration and irrigation requirement CRU TS and GPCC were the most efficient rather than AgMERRA and AgCFSR. But for the estimation of biomass and yield, all the four meteorological datasets were reliable. To sum up, GPCC and AgCFSR were the two best datasets in this study. This study suggests the use of meteorological datasets in water resource management and agricultural management to monitor past changes and estimate recent trends.