The biological basis and clinical significance of hormonal imprinting, an epigenetic process

The biological phenomenon, hormonal imprinting, was named and defined by us (Biol Rev, 1980, 55, 47-63) 30 years ago, after many experimental works and observations. Later, similar phenomena were also named to epigenetic imprinting or metabolic imprinting. In the case of hormonal imprinting, the first encounter between a hormone and its developing target cell receptor—usually at the perinatal period—determines the normal receptor-hormone connection for life. However, in this period, molecules similar to the target hormone (members of the same hormone family, synthetic drugs, environmental pollutants, etc), which are also able to bind to the receptor, provoke faulty imprinting also with lifelong—receptorial, behavioral, etc.,—consequences. Faulty hormonal imprinting could also be provoked later in life in continuously dividing cells and in the brain. Faulty hormonal imprinting is a disturbance of gene methylation pattern, which is epigenenetically inherited to the further generations (transgenerational imprinting). The absence of the normal or the presence of false hormonal imprinting predispose to or manifested in different diseases (e.g., malignant tumors, metabolic syndrome) long after the time of imprinting or in the progenies

Assessment of variations in benzene concentration produced from vehicles and gas stations in Tehran using GIS

This study investigates the variations of benzene concentration levels in district 1, situated in the north part of Tehran, capital of Iran. Thirty-three stations in five categories, namely roadsides, busy roads, residential areas, traffic intersections, and the vicinity of gas stations, were monitored during the rush hours in the afternoon once a week over a period of 1 year. Accordingly, benzene concentration levels were measured and predicted by inverse distance weight model. The recorded benzene concentration levels were then compared with those reported in other parts of the world. According to the results, the annual concentration levels of benzene was 13.85 ppb for roads with heavy traffic flow, 14.98 ppb for traffic intersections, 29.01 ppb for the vicinity of the gas stations, 3.26 ppb for residential areas, and 9.97 ppb for roadsides. The concentration of benzene in the vicinity of the gas station sampling point was higher than in the other stations, and at all locations was found to be so much more than the standard concentration levels (1.56 ppb) prescribed by Environmental Protection Agency for the ambient air quality. The results of the study revealed that the benzene concentration levels in Tehran are distinctly more than its standard level. This is mainly attributed to the poor quality of fuel and lack of a standard system for controlling petrol vapors in the gas stations

The evaluation and determination of heavy metals pollution in edible vegetables, water and soil in the south of Tehran province by GIS

In this study, heavy metals pollutions in waters, soils and vegetables were investigated from farms, near oil refinery in south of Tehran city, Iran (Shahre Ray). The most important heavy metals in Iranian oil are vanadium, cobalt, nickel, arsenic and mercury (V, Co, Ni, As, Hg). In this region, the concentration of heavy metals in soils, well waters and leafy edible vegetables were evaluated in ten different points of farms. Geographic information systems (GIS) were used to estimate the levels of heavy metals concentration at unmeasured locations. After sample preparation, concentrations of heavy metals in vegetables, soils and waters were determined by atomic absorption spectrometry (AAS). Five different leafy edible vegetables from farms, i.e., Persian leek, dill, parsley, spinach and radish were sampled in spring, summer and autumn 2012. In vegetables and well water samples, the concentrations of V, Ni and Co were above the permissible limit of heavy metals as compared to WHO guidelines and the concentrations of these metals in agricultural soils were found to be lower in accordance to soil references. The industrial waste waters had high concentration of heavy metals in this area. In consequence, the results of this study indicate that industrial waste water can cause pollution in well waters and edible vegetables. So, this region is not suitable for cultivation and growing vegetables

The evaluation and determination of heavy metals pollution in edible vegetables, water and soil in the south of Tehran province by GIS

In this study, heavy metals pollutions in waters, soils and vegetables were investigated from farms, near oil refinery in south of Tehran city, Iran (Shahre Ray). The most important heavy metals in Iranian oil are vanadium, cobalt, nickel, arsenic and mercury (V, Co, Ni, As, Hg). In this region, the concentration of heavy metals in soils, well waters and leafy edible vegetables were evaluated in ten different points of farms. Geographic information systems (GIS) were used to estimate the levels of heavy metals concentration at unmeasured locations. After sample preparation, concentrations of heavy metals in vegetables, soils and waters were determined by atomic absorption spectrometry (AAS). Five different leafy edible vegetables from farms, i.e., Persian leek, dill, parsley, spinach and radish were sampled in spring, summer and autumn 2012. In vegetables and well water samples, the concentrations of V, Ni and Co were above the permissible limit of heavy metals as compared to WHO guidelines and the concentrations of these metals in agricultural soils were found to be lower in accordance to soil references. The industrial waste waters had high concentration of heavy metals in this area. In consequence, the results of this study indicate that industrial waste water can cause pollution in well waters and edible vegetables. So, this region is not suitable for cultivation and growing vegetables

Renin-angiotensin system affects endothelial morphology and permeability of renal afferent arteriole

The afferent arteriole (AA) is an important regulatory site of renal function and blood pressure. We have demonstrated endothelial fenestration and high permeability in the vicinity of renin granulated epithelioid cells in the juxtaglomerular portion of the afferent arteriole in different mammals. The permeability of fenestrated endothelium of afferent arteriole may be important in connection to various physiologic and pathophysiologic processes. We have assumed that the permeable fenestration may serve as a communication channel between the intravascular circulation and a pathway for renin secretion. Utilising the multiphoton image technique we were able to visualise the endothelial fenestration and renin granules of the in vitro microperfused AA and in vivo AA. We demonstrated that ferritin-positive, i.e., permeable portion of the afferent arteriole, under control conditions is on average 45 μm, which is about one-third to half of the total length of the afferent arteriole. The length of this portion is not constant and can change by physiologic and pharmacologic manipulation of renin formation. The permeability of the afferent arteriole is not changing only parallel with the pharmacologically stimulated renin secretion as already demonstrated in adult rats, but also with the change of renin appearance in afferent arteriole within the very first few days of life after birth. Independently from the age there is a significant correlation between the renin-positive and permeable portion of the AA. Further studies are necessary to clarify the physiological significance of afferent arteriolar permeability and its changes in the postnatal development of the kidney, as well as in correlation with activity of renin- angiotensin system

Prediction of permanent deformation in asphalt pavements using a novel symbiotic organisms search–least squares support vector regression

The prediction of asphalt performance can be very important in terms of increasing service life and performance while saving energy and money. In this study, a new hybrid artificial intelligence (AI) system, SOS-LSSVR, has been proposed to predict the permanent deformation potential of asphalt pavement mixtures. SOS-LSSVR utilizes the symbiotic organisms search (SOS) and the least squares support vector regression (LSSVR), which are seen as a complementary system. The prediction model can be established from all input and output data pairs for LSSVR, while SOS optimizes the systems tuning parameters. To avoid sampling bias and to partition the dataset into testing and training, a cross-validation technique was chosen. The results can be compared to those of previous studies and other predictive methods. Through the use of four error indicators, SOS-LSSVR accuracy was verified in predicting the permanent deformation behavior of an asphalt mixture. The present study demonstrates that the proposed AI system is a valuable decision-making tool for road designers. Additionally, the success of SOS-LSSVR in building an accurate prediction model suggests that the proposed self-optimized prediction framework has found an underlying pattern in the current database and thus can potentially be implemented in various disciplines