Mineral and heavy metals content in tilapia fish (Oreochromis niloticus) collected from the River Nile in Damietta governorate, Egypt and evaluation of health risk from tilapia consumption

This study was conducted to determine heavy metals and trace elements content in tilapia fish collected from three sources in Damietta governorate, Egypt and to evaluate the human health risk due to tilapia consumption. Tilapia samples were collected from two locations in the River Nile stream, tow fish farms and two sluiceways. Health risk assessment was evaluated based on the consumption habits of adult human. The results revealed that all samples vary in elements concentrations. The calculation of human health risk revealed that the consumption of tilapia in the three tested area does not pose any health risk except for Selenium. It could be concluded that consumption of such fish may be a risk for consumers who eat fish more than one time per week. Consequently, precautions should be taken and warning against eating tilapia fish caught from these regions should be announced.This study was conducted to determine heavy metals and trace elements content in tilapia fish collected from three sources in Damietta governorate, Egypt and to evaluate the human health risk due to tilapia consumption. Tilapia samples were collected from two locations in the River Nile stream, tow fish farms and two sluiceways. Health risk assessment was evaluated based on the consumption habits of adult human. The results revealed that all samples vary in elements concentrations. The calculation of human health risk revealed that the consumption of tilapia in the three tested area does not pose any health risk except for Selenium. It could be concluded that consumption of such fish may be a risk for consumers who eat fish more than one time per week. Consequently, precautions should be taken and warning against eating tilapia fish caught from these regions should be announced

Structural representations of DNA regulatory substrates can enhance sequence-based algorithms by associating functional sequence variants

The nucleotide sequence representation of DNA can be inadequate for resolving protein-DNA binding sites and regulatory substrates, such as those involved in gene expression and horizontal gene transfer. Considering that sequence-like representations are algorithmically very useful, here we fused over 60 currently available DNA physicochemical and conformational variables into compact structural representations that can encode single DNA binding sites to whole regulatory regions. We find that the main structural components reflect key properties of protein-DNA interactions and can be condensed to the amount of information found in a single nucleotide position. The most accurate structural representations compress functional DNA sequence variants by 30% to 50%, as each instance encodes from tens to thousands of sequences. We show that a structural distance function discriminates among groups of DNA substrates more accurately than nucleotide sequence-based metrics. As this opens up a variety of implementation possibilities, we develop and test a distance-based alignment algorithm, demonstrating the potential of using the structural representations to enhance sequence-based algorithms. Due to the bias of most current bioinformatic methods to nucleotide sequence representations, it is possible that considerable performance increases might still be achievable with such solutions.Comment: 20 pages, 8 figures, 3 tables, conferenc

Magnetic resonance lung function – a breakthrough for lung imaging and functional assessment? A phantom study and clinical trial

BACKGROUND: Chronic lung diseases are a major issue in public health. A serial pulmonary assessment using imaging techniques free of ionizing radiation and which provides early information on local function impairment would therefore be a considerably important development. Magnetic resonance imaging (MRI) is a powerful tool for the static and dynamic imaging of many organs. Its application in lung imaging however, has been limited due to the low water content of the lung and the artefacts evident at air-tissue interfaces. Many attempts have been made to visualize local ventilation using the inhalation of hyperpolarized gases or gadolinium aerosol responding to MRI. None of these methods are applicable for broad clinical use as they require specific equipment. METHODS: We have shown previously that low-field MRI can be used for static imaging of the lung. Here we show that mathematical processing of data derived from serial MRI scans during the respiratory cycle produces good quality images of local ventilation without any contrast agent. A phantom study and investigations in 85 patients were performed. RESULTS: The phantom study proved our theoretical considerations. In 99 patient investigations good correlation (r = 0.8; p ≤ 0.001) was seen for pulmonary function tests and MR ventilation measurements. Small ventilation defects were visualized. CONCLUSION: With this method, ventilation defects can be diagnosed long before any imaging or pulmonary function test will indicate disease. This surprisingly simple approach could easily be incorporated in clinical routine and may be a breakthrough for lung imaging and functional assessment

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences