Urinary bisphenol A concentrations in girls from rural and urban Egypt: a pilot study

Abstract Background Exposure to endocrine active compounds, including bisphenol A (BPA), remains poorly characterized in developing countries despite the fact that behavioral practices related to westernization have the potential to influence exposure. BPA is a high production volume chemical that has been associated with metabolic dysfunction as well as behavioral and developmental effects in people, including children. In this pilot study, we evaluate BPA exposure and assess likely pathways of exposure among girls from urban and rural Egypt. Methods We measured urinary concentrations of total (free plus conjugated) species of BPA in spot samples in urban (N = 30) and rural (N = 30) Egyptian girls, and compared these concentrations to preexisting data from age-matched American girls (N = 47) from the U.S. National Health and Nutrition Examination Survey (NHANES). We also collected anthropometric and questionnaire data regarding food storage behaviors to assess potential routes of exposure. Results Urban and rural Egyptian girls exhibited similar concentrations of urinary total BPA, with median unadjusted values of 1.00 and 0.60 ng/mL, respectively. Concentrations of urinary BPA in this group of Egyptian girls (median unadjusted: 0.70 ng/mL) were significantly lower compared to age-matched American girls (median unadjusted: 2.60 ng/mL) according to NHANES 2009-2010 data. Reported storage of food in plastic containers was a significant predictor of increasing concentrations of urinary BPA. Conclusions Despite the relatively low urinary BPA concentrations within this Egyptian cohort, the significant association between food storage behaviors and increasing urinary BPA concentration highlights the need to understand food and consumer product patterns that may be closing the gap between urban and rural lifestyles.http://deepblue.lib.umich.edu/bitstream/2027.42/112495/1/12940_2011_Article_523.pd

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases