All ETDs from UAB

Advisory Committee Chair

Martin R Johnson

Advisory Committee Members

William Parker

Bo Xu

Hassan Fathallah-Shaykh

John Fiveash

Donald Buchsbaum

Document Type


Date of Award


Degree Name by School

Doctor of Philosophy (PhD) Heersink School of Medicine


Following a radiological or nuclear disaster, radiation dose assessment is imperative to minimize morbidity and mortality through rationally directed medical intervention. Current methods of retrospective dosimetry are not amenable to mass exposure scenarios and remain limited to monitoring of clinical symptoms (nausea/vomiting and lymphocyte depletion) and cytogenetic analysis. The goal of this study was to identify radiation biomarkers capable of qualitative (non-irradiated/irradiated) and quantitative (dose) assessment of radiation exposure. Initial analyses revealed 17 radiation-responsive cytokine/chemokine genes in blood samples from 6 pediatric cancer patients undergoing fractionated total body irradiation (TBI). These 17 genes were combined with 29 additional IR-inducible genes (46 genes total) identified in the literature to formulate a rationally designed Taqman Low Density Array (TLDA) for subsequent studies. TLDA analysis was performed in blood samples from 9 healthy volunteers and from 4 additional patients at baseline and 5, 23, and 48 hours following the initial 2 Gy fraction of TBI. Of these 46 genes, 8 demonstrated increased expression post-TBI, with no significant differences between volunteers and baseline patient samples. These 8 genes were examined in C57BL6 mice at 0, 5, 12, 23, and 48 hours post-TBI (0, 1, 2, or 6 Gy). There were significant increases in Bbc3, Ccng1, Cdkn1a, Serpine1, and Tnfrsf10b post-TBI as well as linear dose-dependent responses at 48 hours. A weighted voting algorithm followed by leave-one-out cross validation was utilized to identify molecular signatures capable of accurately classifying mice as exposed versus not exposed. At 48 hours, expression of Ccng1 and Cdkn1a correctly classified mice with an accuracy of 92.6%. Testing of this model in an independent validation set of mice (exposed to 0, 1, 2, 4, 6, or 8 Gy) revealed 96.3% accuracy in segregating mice into correct exposure categories. Multiple linear regression analysis at 48 hours using these same two genes predicted doses for the 0, 1, 2, 4, 6, and 8 Gy treatment groups as 0.0±0.2, 1.6±1.0, 2.9±1.4, 5.1±2.0, 5.3±0.7, and 10.5±5.6 Gy, respectively. These studies suggest that incorporation of gene expression analysis into current biodosimetry protocols may facilitate triage of exposed individuals and direct treatment decisions.



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