Exposure from the Chernobyl accident had adverse effects on erythrocytes, leukocytes, and, platelets in children in the Narodichesky region, Ukraine: A 6-year follow-up study

<p>Abstract</p> <p>Background</p> <p>After the Chernobyl nuclear accident on April 26, 1986, all children in the contaminated territory of the Narodichesky region, Zhitomir Oblast, Ukraine, were obliged to participate in a yearly medical examination. We present the results from these examinations for the years 1993 to 1998. Since the hematopoietic system is an important target, we investigated the association between residential soil density of ¹³⁷Caesium (¹³⁷Cs) and hemoglobin concentration, and erythrocyte, platelet, and leukocyte counts in 1,251 children, using 4,989 repeated measurements taken from 1993 to 1998.</p> <p>Methods</p> <p>Soil contamination measurements from 38 settlements were used as exposures. Blood counts were conducted using the same auto-analyzer in all investigations for all years. We used linear mixed models to compensate for the repeated measurements of each child over the six year period. We estimated the adjusted means for all markers, controlling for potential confounders.</p> <p>Results</p> <p>Data show a statistically significant reduction in red and white blood cell counts, platelet counts and hemoglobin with increasing residential ¹³⁷Cs soil contamination. Over the six-year observation period, hematologic markers did improve. In children with the higher exposure who were born before the accident, this improvement was more pronounced for platelet counts, and less for red blood cells and hemoglobin. There was no exposure×time interaction for white blood cell counts and not in 702 children who were born after the accident. The initial exposure gradient persisted in this sub-sample of children.</p> <p>Conclusion</p> <p>The study is the first longitudinal analysis from a large cohort of children after the Chernobyl accident. The findings suggest persistent adverse hematological effects associated with residential ¹³⁷Cs exposure.</p

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