Novel biodosimetry assays are needed to categorize both acute ionizing radiation injury and delayed effects of radiation exposure, such as radiation-induced lung injury (RILI) -associated mortality. In this study, we utilized the C57L/J mouse model, a well-established system for replicating the clinical pathology of RILI. Lung injury was induced using a combination of neutron total-body irradiation (TBI) (30% of total dose +7% of total dose concomitant gamma rays) and whole-thoracic X-irradiation (WTI) boost for the balance of the required dose at total doses of 9, 9.5, 10 and 10.5 Gy. The animals were monitored for a period of 180 days postirradiation to evaluate the progression of injury. Both male and female mice were included in the study, with cohorts exposed to either sham dose (0 Gy) or 100% X-ray WTI at 11.35 Gy (LD_50/180 dose) to serve as controls. Tissue injury was characterized using whole-body plethysmography, histopathology, and targeted lipidomics. Urinary metabolites were detected using untargeted metabolomic profiling to determine if they could serve as early predictors of RILI survival. A survival rate of 40–45% was observed at 180 days postirradiation consistent with the established LD_50/180 value for WTI (11.35 Gy), except at 10.5 Gy, where survival dropped to 20%. Irradiated mice exhibited increased pulmonary immune infiltration and collagen deposition, reduced alveolar spaces, thickened bronchiolar walls, and dose-independent alterations in lipid profiles that were not sex-specific. We developed a multiplex urinary metabolite panel that was associated with RILI and radiation exposure. Some compounds were statistically different between sham-irradiated male and female mice, with sex specific differences at 120 days were observed for homocitrulline, xanthosine, acetyl-arginine, methylhistidine, niacinamide, xanthurenic acid, cyclic adenosine monophosphate, taurine, and prolyl-proline urinary metabolite levels. Baseline differences in sham-irradiated C57L/J mice show sex needs to be considered as a variable when developing biomarker panels for long-term RILI effects. However, urinary metabolite panels can provide excellent to very good sensitivity and specificity at predicting survival from RILI.

The objective of the work was to estimate the dose dependence of mortality risk from solid cancers in a cohort that includes members of two cohorts of residents of the Southern Urals who received chronic environmental low-dose, low-dose-rate radiation exposure from releases of the Mayak Plutonium Production Association. These analyses use dose and dose uncertainty estimates from a recently developed Monte-Carlo dosimetry system. The 47,950 members of the cohort include the Techa River Cohort of people who lived in the villages on the Techa River between 1950 and the end of 1960 and the East Urals Radioactive Trace Cohort of people who lived in territories of Chelyabinsk Oblast contaminated by the explosion of a radioactive waste depository on September 29, 1957, between the date of the accident and the end of 1959. As of the end of 2016, there were 25,723 deaths, including 3,783 solid cancer deaths, with 1,392,394 person years among non-migrant cohort members. The solid cancer mortality rate dose response adjusted for the effect of smoking was estimated using an excess relative risk model. Parameter estimates and confidence intervals were computed using maximum likelihood methods. The corrected information matrix method was used to determine risk estimate confidence intervals (CI) adjusted for dose uncertainty using information on the statistical uncertainty of the parameter estimates and individual dose uncertainty information provided by the dosimetry system. The smoking-adjusted linear excess relative risk (ERR) per 100 mGy for solid cancer mortality was 0.060 (95% CI 0.018 to 0.108) at age 70. The ERR increased significantly in proportion to age to the power 3.1 (95% CI 0.44 to 6.4). The joint effect of radiation and smoking on solid cancer rates appeared to be multiplicative. Adjustment for smoking had little impact on the estimated ERR. Adjusting the ERR confidence interval for dose uncertainty slightly increased the upper confidence bound (adjusted 95% CI 0.018 to 0.120). There was no evidence of nonlinearity in the solid cancer dose response. Except for liver cancer, ERR estimates for various specific types of cancer were positive. However, they were statistically significant only for stomach and female breast cancers. Statistically significant smoking effects were seen for cancers of the lung, stomach, and esophagus. Risk estimates for the two groups in the cohort did not differ significantly. The risk estimates in this cohort were consistent with data in two major occupational cohorts, they were higher than those seen in the Mayak Worker Cohort. While the ERR estimates at age 70 are like those seen in the atomic bomb survivor life span study, the ERR age dependencies were strikingly different. These findings strengthen the evidence for low-dose, low-dose-rate radiation effects on solid cancer mortality rates.

Radiation damage and deposition caused by radiological or nuclear public health incidents (e.g., accidents or attacks) may lead to acute radiation syndrome and other complications. Accurate and effective radiation dose assessment is necessary for triaging irradiated patients and determining treatment plans. However, there is no systematic evaluation of whether radiation biodosimetry is affected by comorbidities. The weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEG) co-analysis of the RNA-sequencing data in human peripheral blood after irradiation from the Gene Expression Omnibus (GEO) database identified seven radiation-specific genes, including five upregulated genes and two down-regulated genes. Five radiation-specific genes (CCNG1, CDKN1A, GADD45A, GZMB, PHLDA3) showed a strong linear correlation with the total-body X-ray radiation model. The above five genes were used to validate further several radiation combined injury models, including infection, trauma, and burns, while considering different sexes and ages in animal studies on the radiation response from 0 to 10 Gy. The receiving operator characteristic (ROC) curve analysis revealed that the CCNG1 and CDKN1A genes performed the best in radiation dose-response across both mice and humans. Moreover, the CCNG1 protein could accurately predict the absorbed doses for up to 28 days after exposure (>95%). Our findings suggested that the CCNG1 and CDKN1A mRNA performed optimally in radiation dose response, independent of trauma, burns, age, and sex. Additionally, the CCNG1 protein revealed a strong linear correlation between radiation dose and time postirradiation. Our study demonstrated the potential feasibility of using CCNG1 and CDKN1A as injury biomarkers in radiation accident management.

The limited availability of post-Gamma Knife radiosurgery (GKRS) samples and the unsuitability of clinical GKRS devices for small animals highlight the need to develop devices that enable the application of a clinical GKRS device in mouse models. This study introduces a novel platform specifically designed for utilizing the Leksell Gamma Knife in mouse studies. The 3D-printed device comprises a positioning platform and a head fixation device. Six-week-old C57BL/6N mice underwent irradiation targeting the left caudate putamen (CPu) or left anterior frontobase areas. Clinical Gamma Knife prescription doses (central radiation doses of 80 Gy, 60 Gy, 50 Gy, 40 Gy, 20 Gy, and 10 Gy) were administered as single exposures. Dose conversion experiments confirmed that the actual radiation dose delivered to mice was consistently 1.5-fold higher than the planned clinical dose. MRI and H&E staining revealed clear radiation necrosis (RN) in the targeted areas when the planned clinical dose of 80 Gy was applied to the CPu and anterior frontobase, confirming the device's accuracy. γ-H2AX staining showed significant DNA double-strand breaks in the targeted region, particularly after a planned clinical dose of 40 Gy and higher. H&E staining also indicated parenchymal hemorrhage, tissue loss, and edema in the targeted areas among groups exposed to the planned clinical central doses of 80 Gy, 60 Gy, and 50 Gy. Immunofluorescence staining of CD68, IBA1, and NeuN showed significant neuroinflammation in the targeted areas of the high-dose groups (planned clinical doses of 80 Gy, 60 Gy, 50 Gy, or 40 Gy), characterized by increased microglia activation, macrophage infiltration, and neuronal death. This study developed a novel mouse platform for the Leksell Gamma Knife, enabling precise GKRS in mouse brains. For adult C57BL/6N mice, a planned clinical central dose of 40 Gy may be considered a suitable threshold for radiation-induced brain injury.

Ionizing radiation exposure induces cellular and molecular damage, leading to a chain of events that results in tissue and organ injury. Proteomics studies help identify, validate, and quantify alterations in protein abundance downstream of radiation-induced genomic changes. The current study strives to characterize and validate the proteomic changes at the preterminal stage (moribund animals) in serum samples collected from rhesus macaques lethally and acutely irradiated with two different doses of cobalt-60 gamma-radiation. Peripheral blood samples were collected prior to exposure, after exposure, and at the preterminal stage from nonhuman primates (NHPs) that did not survive after 7.2 or 7.6 Gy total-body irradiation (LD_60-80/60). Using mass spectrometry-based proteomics, we analyzed samples collected at various time points after irradiation. Our findings revealed that radiation induced significant time-dependent proteomic alterations compared to pre-exposure samples. More pronounced dysregulation in pathways related to immune response and hemostasis, specifically platelet function, was present in preterminal samples, suggesting that alterations in these pathways may indicate the preterminal phenotype. These results offer important insights for the identification and validation of biomarkers for radiation-induced lethality that would be of great importance for triage during a radiological/nuclear mass casualty event.

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. The workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.