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Acute Radiation Syndrome Severity Score System in Mouse Total-Body Irradiation Model

Ossetrova, Natalia I.; Ney, Patrick H.; Condliffe, Donald P.; Krasnopolsky, Katya; Hieber, Kevin P.

doi: 10.1097/HP.0000000000000499
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Radiation accidents or terrorist attacks can result in serious consequences for the civilian population and for military personnel responding to such emergencies. The early medical management situation requires quantitative indications for early initiation of cytokine therapy in individuals exposed to life-threatening radiation doses and effective triage tools for first responders in mass-casualty radiological incidents. Previously established animal (Mus musculus, Macaca mulatta) total-body irradiation (γ-exposure) models have evaluated a panel of radiation-responsive proteins that, together with peripheral blood cell counts, create a multiparametic dose-predictive algorithm with a threshold for detection of ~1 Gy from 1 to 7 d after exposure as well as demonstrate the acute radiation syndrome severity score systems created similar to the Medical Treatment Protocols for Radiation Accident Victims developed by Fliedner and colleagues. The authors present a further demonstration of the acute radiation sickness severity score system in a mouse (CD2F1, males) TBI model (1–14 Gy, 60Co γ-rays at 0.6 Gy min−1) based on multiple biodosimetric endpoints. This includes the acute radiation sickness severity Observational Grading System, survival rate, weight changes, temperature, peripheral blood cell counts and radiation-responsive protein expression profile: Flt‐3 ligand, interleukin 6, granulocyte-colony stimulating factor, thrombopoietin, erythropoietin, and serum amyloid A. Results show that use of the multiple-parameter severity score system facilitates identification of animals requiring enhanced monitoring after irradiation and that proteomics are a complementary approach to conventional biodosimetry for early assessment of radiation exposure, enhancing accuracy and discrimination index for acute radiation sickness response categories and early prediction of outcome.

*Uniformed Services University (USU), Armed Forces Radiobiology Research Institute (AFRRI), Scientific Research Department, 8901 Wisconsin Avenue, Bethesda, MD 20889‐5603; †United States Army Medical Command (MEDCOM), United States Army Medical Research Institute of Chemical Defense (USAMRICD), 3100 Ricketts Point Road, Aberdeen Proving Ground, MD 21010‐5400.

The authors declare no conflicts of interest.

For correspondence contact: Natalia I. Ossetrova, AFRRI, 8901 Wisconsin Avenue, Bethesda, MD 20889‐5603, or email at natalia.ossetrova@usuhs.edu.

(Manuscript accepted 6 January 2016)

© 2016 by the Health Physics Society