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Following a large-scale radiological event, timely collection of samples from all potentially exposed individuals may be precluded, and high-throughput bioassays capable of rapid and individualized dose assessment several days post-exposure will be essential for population triage and efficient implementation of medical treatment. The objective of this work was to validate the performance of a biomarker panel of radiosensitive intracellular leukocyte proteins (ACTN1, DDB2, and FDXR) and blood cell counts (CD19+ B-cells and CD3+ T-cells) for retrospective classification of exposure and dose estimation up to 7 days post-exposure in an in-vivo C57BL/6 mouse model. Juvenile and adult C57BL/6 mice of both sexes were total body irradiated with 0, 1, 2, 3, or 4 Gy, peripheral blood was collected 1, 4, and 7-days post-exposure, and individual blood biomarkers were quantified by imaging flow cytometry. An ensemble machine learning platform was used to identify the strongest predictor variables and combine them for biodosimetry outputs. This approach generated successful exposure classification (ROC AUC = 0.94, 95% CI: 0.90-0.97) and quantitative dose reconstruction (R 2  = 0.79, RMSE = 0.68 Gy, MAE = 0.53 Gy), supporting the potential utility of the proposed biomarker assay for determining exposure and received dose in an individual., (© 2024. The Author(s).)

In major radiological events, rapid assays to detect ionizing radiation exposure are crucial for effective medical interventions. The purpose of these assays is twofold: to categorize affected individuals into groups for initial treatments, and to provide definitive dose estimates for continued care and epidemiology. However, existing high-throughput cytogenetic biodosimetry assays take about 3 days to yield results, which delays critical interventions. We have developed a multiwell-based variant of the chemical-induced G0-phase Premature Chromosome Condensation Assay that delivers same-day results. Our findings revealed that using a concentration of phosphatase inhibitor lower than recommended significantly increases the yield of cells with highly condensed chromosomes. These chromosomes exhibited increased fragmentation in a dose-dependent manner, enabling to quantify radiation damage using a custom Deep Learning algorithm. This algorithm demonstrated reasonable performance in categorizing doses into distinct treatment groups (84% and 80% accuracy for three and four iso-treatment dose bins, respectively) and showed reliability in determining the actual doses received (correlation coefficient of 0.879). This method is amendable to full automation and has the potential to address the need for same-day, high-throughput cytogenetic test for both dose categorization and dose reconstruction in large-scale radiation emergencies., (© 2024. The Author(s).)

There are currently no available FDA-cleared biodosimetry tools for rapid and accurate assessment of absorbed radiation dose following a radiation/nuclear incident. Previously we developed a protein biomarker-based FAST-DOSE bioassay system for biodosimetry. The aim of this study was to integrate an ELISA platform with two high-performing FAST-DOSE biomarkers, BAX and DDB2, and to construct machine learning models that employ a multiparametric biomarker strategy for enhancing the accuracy of exposure classification and radiation dose prediction. The bioassay showed 97.92% and 96% accuracy in classifying samples in human and non-human primate (NHP) blood samples exposed ex vivo to 0-5 Gy X-rays, respectively up to 48 h after exposure, and an adequate correlation between reconstructed and actual dose in the human samples (R 2  = 0.79, RMSE = 0.80 Gy, and MAE = 0.63 Gy) and NHP (R 2  = 0.80, RMSE = 0.78 Gy, and MAE = 0.61 Gy). Biomarker measurements in vivo from four NHPs exposed to a single 2.5 Gy total body dose showed a persistent upregulation in blood samples collected on days 2 and 5 after irradiation. The data indicates that using a combined approach of targeted proteins can increase bioassay sensitivity and provide a more accurate dose prediction., (© 2024. The Author(s).)

This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles. SHAP (SHapley Additive exPlanations) values and variable importance scores were employed for interpretation. The findings revealed clear dose-response trends, with cancer being the predominant cause of mortality. SHAP value dose-response shapes differed, showing saturation for cancer hazard at high doses (> 2 Gy) and a more linear pattern at lower doses. Non-cancer responses remained more linear throughout the entire dose range. There was a potential inverse dose rate effect for cancer, while the evidence for non-cancer was less conclusive. Sex and age effects were less pronounced. This investigation, utilizing machine learning, enhances our understanding of the patterns of non-cancer and cancer mortality induced by densely-ionizing radiations, emphasizing the importance of such approaches in radiation research, including space travel and radioprotection., (© 2024. The Author(s).)

An emerging intervention for control of airborne-mediated pandemics and epidemics is whole-room far-UVC (200-235 nm). Laboratory studies have shown that 222-nm light inactivates airborne pathogens, potentially without harm to exposed occupants. While encouraging results have been reported in benchtop studies and in room-sized bioaerosol chambers, there is a need for quantitative studies of airborne pathogen reduction in occupied rooms. We quantified far-UVC mediated reduction of aerosolized murine norovirus (MNV) in an occupied mouse-cage cleaning room within an animal-care facility. Benchtop studies suggest that MNV is a conservative surrogate for airborne viruses such as influenza and coronavirus. Using four 222-nm fixtures installed in the ceiling, and staying well within current recommended regulatory limits, far-UVC reduced airborne infectious MNV by 99.8% (95% CI: 98.2-99.9%). Similar to previous room-sized bioaerosol chamber studies on far-UVC efficacy, these results suggest that aerosolized virus susceptibility is significantly higher in room-scale tests than in bench-scale laboratory studies. That said, as opposed to controlled laboratory studies, uncertainties in this study related to airflow patterns, virus residence time, and dose to the collected virus introduce uncertainty into the inactivation estimates. This study is the first to directly demonstrate far-UVC anti-microbial efficacy against airborne pathogens in an occupied indoor location., (© 2024. The Author(s).)

The biological effects of densely-ionizing radiations such as neutrons and heavy ions encountered in space travel, nuclear incidents, and cancer radiotherapy, significantly differ from those of sparsely-ionizing photons and necessitate a comprehensive understanding for improved protection measures. Data on lifespan studies of laboratory rodents exposed to fission neutrons, accumulated in the Janus archive, afford unique insights into the impact of densely ionizing radiation on mortality from cancers and various organ dysfunction. We extracted and analyzed data for 21,308 individual B6CF1 mice to investigate the effects of neutron dose, fractionation, protraction, age, and sex on mortality. As Cox regression encountered limitations owing to assumption violations, we turned to Random Survival Forests (RSF), a machine learning algorithm adept at modeling nonlinear relationships. RSF interpretation using Shapley Additive Explanations revealed a dose response for mortality risk that curved upwards at low doses < 20 cGy, became nearly-linear over 20-150 cGy, and saturated at high doses. The response was enhanced by fractionation/protraction of irradiation (exhibiting an inverse dose rate effect), and diminished by older age at exposure. Somewhat reduced mortality was predicted for males vs females. This research expands our knowledge on the long-term effects of densely ionizing radiations on mammal mortality., (© 2023. The Author(s).)

There is a persistent risk of a large-scale malicious or accidental exposure to ionizing radiation that may affect a large number of people. Exposure will consist of both a photon and neutron component, which will vary in magnitude between individuals and is likely to have profound impacts on radiation-induced diseases. To mitigate these potential disasters, there exists a need for novel biodosimetry approaches that can estimate the radiation dose absorbed by each person based on biofluid samples, and predict delayed effects. Integration of several radiation-responsive biomarker types (transcripts, metabolites, blood cell counts) by machine learning (ML) can improve biodosimetry. Here we integrated data from mice exposed to various neutron + photon mixtures, total 3 Gy dose, using multiple ML algorithms to select the strongest biomarker combinations and reconstruct radiation exposure magnitude and composition. We obtained promising results, such as receiver operating characteristic curve area of 0.904 (95% CI: 0.821, 0.969) for classifying samples exposed to ≥ 10% neutrons vs. < 10% neutrons, and R 2 of 0.964 for reconstructing photon-equivalent dose (weighted by neutron relative biological effectiveness) for neutron + photon mixtures. These findings demonstrate the potential of combining various -omic biomarkers for novel biodosimetry., (© 2023. The Author(s).)

During a large-scale radiological event such as an improvised nuclear device detonation, many survivors will be shielded from radiation by environmental objects, and experience only partial-body irradiation (PBI), which has different consequences, compared with total-body irradiation (TBI). In this study, we tested the hypothesis that applying machine learning to a combination of radiation-responsive biomarkers (ACTN1, DDB2, FDXR) and B and T cell counts will quantify and distinguish between PBI and TBI exposures. Adult C57BL/6 mice of both sexes were exposed to 0, 2.0-2.5 or 5.0 Gy of half-body PBI or TBI. The random forest (RF) algorithm trained on ½ of the data reconstructed the radiation dose on the remaining testing portion of the data with mean absolute error of 0.749 Gy and reconstructed the product of dose and exposure status (defined as 1.0 × Dose for TBI and 0.5 × Dose for PBI) with MAE of 0.472 Gy. Among irradiated samples, PBI could be distinguished from TBI: ROC curve AUC = 0.944 (95% CI: 0.844-1.0). Mouse sex did not significantly affect dose reconstruction. These results support the hypothesis that combinations of protein biomarkers and blood cell counts can complement existing methods for biodosimetry of PBI and TBI exposures., (© 2023. The Author(s).)

A large-scale malicious or accidental radiological event can expose vast numbers of people to ionizing radiation. The dicentric chromosome (DCA) and cytokinesis-block micronucleus (CBMN) assays are well-established biodosimetry methods for estimating individual absorbed doses after radiation exposure. Here we used machine learning (ML) to test the hypothesis that combining automated DCA and CBMN assays will improve dose reconstruction accuracy, compared with using either cytogenetic assay alone. We analyzed 1349 blood sample aliquots from 155 donors of different ages (3-69 years) and sexes (49.1% males), ex vivo irradiated with 0-8 Gy at dose rates from 0.08 Gy/day to ≥ 600 Gy/s. We compared the performances of several state-of-the-art ensemble ML methods and found that random forest generated the best results, with R 2 for actual vs. reconstructed doses on a testing data subset = 0.845, and mean absolute error = 0.628 Gy. The most important predictor variables were CBMN and DCA frequencies, and age. Removing CBMN or DCA data from the model significantly increased squared errors on testing data (p-values 3.4 × 10 -8 and 1.1 × 10 -6 , respectively). These findings demonstrate the promising potential of combining CBMN and DCA assay data to reconstruct radiation doses in realistic scenarios of heterogeneous populations exposed to a mass-casualty radiological event., (© 2022. The Author(s).)

In the search for biological markers after a large-scale exposure of the human population to radiation, gene expression is a sensitive endpoint easily translatable to in-field high throughput applications. Primarily, the ex-vivo irradiated healthy human blood model has been used to generate available gene expression datasets. This model has limitations i.e., lack of signaling from other irradiated tissues and deterioration of blood cells cultures over time. In vivo models are needed; therefore, we present our novel approach to define a gene signature in mouse blood cells that quantitatively correlates with radiation dose (at 1 Gy/min). Starting with available microarray datasets, we selected 30 radiation-responsive genes and performed cross-validation/training-testing data splits to downselect 16 radiation-responsive genes. We then tested these genes in an independent cohort of irradiated adult C57BL/6 mice (50:50 both sexes) and measured mRNA by quantitative RT-PCR in whole blood at 24 h. Dose reconstruction using net signal (difference between geometric means of top 3 positively correlated and top 4 negatively correlated genes with dose), was highly improved over the microarrays, with a root mean square error of ± 1.1 Gy in male and female mice combined. There were no significant sex-specific differences in mRNA or cell counts after irradiation., (© 2022. The Author(s).)

The space radiation environment is qualitatively different from Earth, and its radiation hazard is generally quantified relative to photons using quality factors that allow assessment of biologically-effective dose. Two approaches exist for estimating radiation quality factors in complex low/intermediate-dose radiation environments: one is a fluence-based risk cross-section approach, which requires very detailed in silico characterization of the radiation field and biological cross sections, and thus cannot realistically be used for in situ monitoring. By contrast, the microdosimetric approach, using measured (or calculated) distributions of microdosimetric energy deposition together with empirical biological weighting functions, is conceptually and practically simpler. To demonstrate feasibility of the microdosimetric approach, we estimated a biological weighting function for one specific endpoint, heavy-ion-induced tumorigenesis in APC 1638N/+ mice, which was unfolded from experimental results after a variety of heavy ion exposures together with corresponding calculated heavy ion microdosimetric energy deposition spectra. Separate biological weighting functions were unfolded for targeted and non-targeted effects, and these differed substantially. We folded these biological weighting functions with microdosimetric energy deposition spectra for different space radiation environments, and conclude that the microdosimetric approach is indeed practical and, in conjunction with in-situ measurements of microdosimetric spectra, can allow continuous readout of biologically-effective dose during space flight., (© 2022. The Author(s).)

Ionizing radiations encountered by astronauts on deep space missions produce biological damage by two main mechanisms: (1) Targeted effects (TE) due to direct traversals of cells by ionizing tracks. (2) Non-targeted effects (NTE) caused by release of signals from directly hit cells. The combination of these mechanisms generates non-linear dose response shapes, which need to be modeled quantitatively to predict health risks from space exploration. Here we used a TE + NTE model to analyze data on APC (1638N/+) mouse tumorigenesis induced by space-relevant doses of protons, 4 He, 12 C, 16 O, 28 Si or 56 Fe ions, or γ rays. A customized weighted Negative Binomial distribution was used to describe the radiation type- and dose-dependent data variability. This approach allowed detailed quantification of dose-response shapes, NTE- and TE-related model parameters, and radiation quality metrics (relative biological effectiveness, RBE, and radiation effects ratio, RER, relative to γ rays) for each radiation type. Based on the modeled responses for each radiation type, we predicted the tumor yield for a Mars-mission-relevant mixture of these radiations, using the recently-developed incremental effect additivity (IEA) synergy theory. The proposed modeling approach can enhance current knowledge about quantification of space radiation quality effects, dose response shapes, and ultimately the health risks for astronauts., (© 2021. The Author(s).)

Large quantities of radionuclides released by the Fukushima nuclear power plant accident entered terrestrial and marine ecosystems. The resulting radioactive contamination of large omnivorous wild mammals such as wild boar (Sus scrofa) and Asian black bear (Ursus thibetanus) varied greatly depending on location, season, and time after the accident. Quantitative modeling of how such factors influence radionuclide burdens in these species is important for enhancing current knowledge of chronic radionuclide exposure consequences in mammalian populations, and for assessing potential human risks from consumption of contaminated animal meat. Here we modeled the time course of radioactive cesium ( 134 Cs +  137 Cs) concentrations in boar and black bears from Fukushima Prefecture over ~ 7 years after the accident, using nonlinear robust and quantile regressions and mixed-effects modeling. To estimate predictive performance, models fitted to the full data set were compared with those fitted only to the first 3.5 years of data, and tested on the last 3.5 years of data. Ecological half-lives for radioactive cesium, and magnitudes and phase shifts for sinusoidal seasonal oscillations in cesium burdens, were estimated by each analysis method for each species. These results can improve the understanding and prediction of radionuclide concentrations in large mammals that inhabit radioactively contaminated areas.

Radiation-induced cognitive dysfunction is increasingly recognized as an important risk for human exploration of distant planets. Mechanistically-motivated mathematical modeling helps to interpret and quantify this phenomenon. Here we considered two general mechanisms of ionizing radiation-induced damage: targeted effects (TE), caused by traversal of cells by ionizing tracks, and non-targeted effects (NTE), caused by responses of other cells to signals released by traversed cells. We compared the performances of 18 dose response model variants based on these concepts, fitted by robust nonlinear regression to a large published data set on novel object recognition testing in rats exposed to multiple space-relevant radiation types (H, C, O, Si, Ti and Fe ions), covering wide ranges of linear energy transfer (LET) (0.22-181 keV/µm) and dose (0.001-2 Gy). The best-fitting model (based on Akaike information criterion) was an NTE + TE variant where NTE saturate at low doses (~ 0.01 Gy) and occur at all tested LETs, whereas TE depend on dose linearly with a slope that increases with LET. The importance of NTE was also found by additional analyses of the data using quantile regression and random forests. These results suggest that NTE-based radiation effects on brain function are potentially important for astronaut health and for space mission risk assessments.

Stress response signals can propagate between cells damaged by targeted effects (TE) of ionizing radiation (e.g. energy depositions and ionizations in the nucleus) and undamaged "bystander" cells, sometimes over long distances. Their consequences, called non-targeted effects (NTE), can substantially contribute to radiation-induced damage (e.g. cell death, genomic instability, carcinogenesis), particularly at low doses/dose rates (e.g. space exploration, some occupational and accidental exposures). In addition to controlled laboratory experiments, analysis of observational data on wild animal and plant populations from areas contaminated by radionuclides can enhance our understanding of radiation responses because such data span wide ranges of dose rates applied over many generations. Here we used a mechanistically-motivated mathematical model of TE and NTE to analyze published embryonic mortality data for plants (Arabidopsis thaliana) and rodents (Clethrionomys glareolus) from the Chernobyl nuclear power plant accident region. Although these species differed strongly in intrinsic radiosensitivities and post-accident radiation exposure magnitudes, model-based analysis suggested that NTE rather than TE dominated the responses of both organisms to protracted low-dose-rate irradiation. TE were predicted to become dominant only above the highest dose rates in the data. These results support the concept of NTE involvement in radiation-induced health risks from chronic radiation exposures.

We implemented machine learning in the radiation biodosimetry field to quantitatively reconstruct neutron doses in mixed neutron + photon exposures, which are expected in improvised nuclear device detonations. Such individualized reconstructions are crucial for triage and treatment because neutrons are more biologically damaging than photons. We used a high-throughput micronucleus assay with automated scanning/imaging on lymphocytes from human blood ex-vivo irradiated with 44 different combinations of 0-4 Gy neutrons and 0-15 Gy photons (542 blood samples), which include reanalysis of past experiments. We developed several metrics that describe micronuclei/cell probability distributions in binucleated cells, and used them as predictors in random forest (RF) and XGboost machine learning analyses to reconstruct the neutron dose in each sample. The probability of "overfitting" was minimized by training both algorithms with repeated cross-validation on a randomly-selected subset of the data, and measuring performance on the rest. RF achieved the best performance. Mean R 2 for actual vs. reconstructed neutron doses over 300 random training/testing splits was 0.869 (range 0.761 to 0.919) and root mean squared error was 0.239 (0.195 to 0.351) Gy. These results demonstrate the promising potential of machine learning to reconstruct the neutron dose component in clinically-relevant complex radiation exposure scenarios.

Following a large-scale radiological incident, there is a need for FDA-approved biodosimetry devices and biomarkers with the ability to rapidly determine past radiation exposure with sufficient accuracy for early population triage and medical management. Towards this goal, we have developed FAST-DOSE (Fluorescent Automated Screening Tool for Dosimetry), an immunofluorescent, biomarker-based system designed to reconstruct absorbed radiation dose in peripheral blood samples collected from potentially exposed individuals. The objective of this study was to examine the performance of the FAST-DOSE assay system to quantify intracellular protein changes in blood leukocytes for early biodosimetry triage from humanized NOD-scid-gamma (Hu-NSG) mice and non-human primates (NHPs) exposed to ionizing radiation up to 8 days after radiation exposure. In the Hu-NSG mice studies, the FAST-DOSE biomarker panel was able to generate delivered dose estimates at days 1, 2 and 3 post exposure, whereas in the NHP studies, the biomarker panel was able to successfully classify samples by dose categories below or above 2 Gy up to 8 days after total body exposure. These results suggest that the FAST-DOSE bioassay has large potential as a useful diagnostic tool for rapid and reliable screening of potentially exposed individuals to aid early triage decisions within the first week post-exposure.

A direct approach to limit airborne viral transmissions is to inactivate them within a short time of their production. Germicidal ultraviolet light, typically at 254 nm, is effective in this context but, used directly, can be a health hazard to skin and eyes. By contrast, far-UVC light (207-222 nm) efficiently kills pathogens potentially without harm to exposed human tissues. We previously demonstrated that 222-nm far-UVC light efficiently kills airborne influenza virus and we extend those studies to explore far-UVC efficacy against airborne human coronaviruses alpha HCoV-229E and beta HCoV-OC43. Low doses of 1.7 and 1.2 mJ/cm 2 inactivated 99.9% of aerosolized coronavirus 229E and OC43, respectively. As all human coronaviruses have similar genomic sizes, far-UVC light would be expected to show similar inactivation efficiency against other human coronaviruses including SARS-CoV-2. Based on the beta-HCoV-OC43 results, continuous far-UVC exposure in occupied public locations at the current regulatory exposure limit (~3 mJ/cm 2 /hour) would result in ~90% viral inactivation in ~8 minutes, 95% in ~11 minutes, 99% in ~16 minutes and 99.9% inactivation in ~25 minutes. Thus while staying within current regulatory dose limits, low-dose-rate far-UVC exposure can potentially safely provide a major reduction in the ambient level of airborne coronaviruses in occupied public locations.

Biodosimetry-based individualized reconstruction of complex irradiation scenarios (partial-body shielding and/or neutron + photon mixtures) can improve treatment decisions after mass-casualty radiation-related incidents. We used a high-throughput micronucleus assay with automated scanning and imaging software on ex-vivo irradiated human lymphocytes to: a) reconstruct partial-body and/or neutron exposure, and b) estimate separately the photon and neutron doses in a mixed exposure. The mechanistic background is that, compared with total-body photon irradiations, neutrons produce more heavily-damaged lymphocytes with multiple micronuclei/binucleated cell, whereas partial-body exposures produce fewer such lymphocytes. To utilize these differences for biodosimetry, we developed metrics that describe micronuclei distributions in binucleated cells and serve as predictors in machine learning or parametric analyses of the following scenarios: (A) Homogeneous gamma-irradiation, mimicking total-body exposures, vs. mixtures of irradiated blood with unirradiated blood, mimicking partial-body exposures. (B) X rays vs. various neutron + photon mixtures. The results showed high accuracies of scenario and dose reconstructions. Specifically, receiver operating characteristic curve areas (AUC) for sample classification by exposure type reached 0.931 and 0.916 in scenarios A and B, respectively. R 2 for actual vs. reconstructed doses in these scenarios reached 0.87 and 0.77, respectively. These encouraging findings demonstrate a proof-of-principle for the proposed approach of high-throughput reconstruction of clinically-relevant complex radiation exposure scenarios.

The mouse (Mus musculus) is an extensively used model of human disease and responses to stresses such as ionizing radiation. As part of our work developing gene expression biomarkers of radiation exposure, dose, and injury, we have found many genes are either up-regulated (e.g. CDKN1A, MDM2, BBC3, and CCNG1) or down-regulated (e.g. TCF4 and MYC) in both species after irradiation at ~4 and 8 Gy. However, we have also found genes that are consistently up-regulated in humans and down-regulated in mice (e.g. DDB2, PCNA, GADD45A, SESN1, RRM2B, KCNN4, IFI30, and PTPRO). Here we test a hematopoietically humanized mouse as a potential in vivo model for biodosimetry studies, measuring the response of these 14 genes one day after irradiation at 2 and 4 Gy, and comparing it with that of human blood irradiated ex vivo, and blood from whole body irradiated mice. We found that human blood cells in the hematopoietically humanized mouse in vivo environment recapitulated the gene expression pattern expected from human cells, not the pattern seen from in vivo irradiated normal mice. The results of this study support the use of hematopoietically humanized mice as an in vivo model for screening of radiation response genes relevant to humans.

In the event of a nuclear attack or large-scale radiation event, there would be an urgent need for assessing the dose to which hundreds or thousands of individuals were exposed. Biodosimetry approaches are being developed to address this need, including transcriptomics. Studies have identified many genes with potential for biodosimetry, but, to date most have focused on classification of samples by exposure levels, rather than dose reconstruction. We report here a proof-of-principle study applying new methods to select radiation-responsive genes to generate quantitative, rather than categorical, radiation dose reconstructions based on a blood sample. We used a new normalization method to reduce effects of variability of signal intensity in unirradiated samples across studies; developed a quantitative dose-reconstruction method that is generally under-utilized compared to categorical methods; and combined these to determine a gene set as a reconstructor. Our dose-reconstruction biomarker was trained using two data sets and tested on two independent ones. It was able to reconstruct dose up to 4.5 Gy with root mean squared error (RMSE) of ± 0.35 Gy on a test dataset using the same platform, and up to 6.0 Gy with RMSE of ± 1.74 Gy on a test set using a different platform.

Exposure to chronic ionizing radiation (CIR) from nuclear power plant accidents, acts of terrorism, and space exploration poses serious threats to humans. Fungi are a group of highly radiation-resistant eukaryotes, and an understanding of fungal CIR resistance mechanisms holds the prospect of protecting humans. We compared the abilities of 95 wild-type yeast and dimorphic fungal isolates, representing diverse Ascomycota and Basidiomycota, to resist exposure to five environmentally-relevant stressors: CIR (long-duration growth under 36 Gy/h) and acute (10 kGy/h) ionizing radiation (IR), heavy metals (chromium, mercury), elevated temperature (up to 50 °C), and low pH (2.3). To quantify associations between resistances to CIR and these other stressors, we used correlation analysis, logistic regression with multi-model inference, and customized machine learning. The results suggest that resistance to acute IR in fungi is not strongly correlated with the ability of a given fungal isolate to grow under CIR. Instead, the strongest predictors of CIR resistance in fungi were resistance to chromium (III) and to elevated temperature. These results suggest fundamental differences between the mechanisms of resistance to chronic and acute radiation. Convergent evolution towards radioresistance among genetically distinct groups of organisms is considered here.

Modelling the biokinetics of radionuclide excretion or retention is important in nuclear medicine and following accidental/malicious radioactivity releases. Sums of discrete exponential decay rates are often used, but we hypothesized that continuous probability distributions (CPD) of decay rates can describe the data more parsimoniously and robustly. We tested this hypothesis on diverse human and animal data sets involving various radionuclides (including plutonium, strontium, caesium) measured in the laboratory and in regions contaminated by the Fukushima and Chernobyl nuclear accidents. We used four models on each data set: mono-exponential (ME) with one discrete decay rate, bi-exponential (BE) with two rates, gamma-exponential (GE) with a Gamma distribution of stretched-exponential rates, and power-decay (PD) with a Gamma distribution of power-decay rates. Information-theoretic model selection suggested that radionuclide biokinetics, e.g. for plutonium in humans, are often better described by CPD models like GE and PD, than by discrete rates (ME and BE). Extrapolation of models fitted to data at short times to longer times was frequently more robust for CPD formalisms. We suggest that using a set of several CPD and discrete-rate models, and comparing them by information-theoretic methods, is a promising strategy to enhance the analysis of radionuclide excretion and retention kinetics.

After a radiological incident, there is an urgent need for fast and reliable bioassays to identify radiation-exposed individuals within the first week post exposure. This study aimed to identify candidate radiation-responsive protein biomarkers in human lymphocytes in vivo using humanized NOD scid gamma (Hu-NSG) mouse model. Three days after X-irradiation (0-2 Gy, 88 cGy/min), human CD45+ lymphocytes were collected from the Hu-NSG mouse spleen and quantitative changes in the proteome of the human lymphocytes were analysed by mass spectrometry. Forty-six proteins were differentially expressed in response to radiation exposure. FDXR, BAX, DDB2 and ACTN1 proteins were shown to have dose-dependent response with a fold change greater than 2. When these proteins were used to estimate radiation dose by linear regression, the combination of FDXR, ACTN1 and DDB2 showed the lowest mean absolute errors (≤0.13 Gy) and highest coefficients of determination (R 2  = 0.96). Biomarker validation studies were performed in human lymphocytes 3 days after irradiation in vivo and in vitro. In conclusion, this is the first study to identify radiation-induced human protein signatures in vivo using the humanized mouse model and develop a protein panel which could be used for the rapid assessment of absorbed dose 3 days after radiation exposure.

Airborne-mediated microbial diseases such as influenza and tuberculosis represent major public health challenges. A direct approach to prevent airborne transmission is inactivation of airborne pathogens, and the airborne antimicrobial potential of UVC ultraviolet light has long been established; however, its widespread use in public settings is limited because conventional UVC light sources are both carcinogenic and cataractogenic. By contrast, we have previously shown that far-UVC light (207-222 nm) efficiently inactivates bacteria without harm to exposed mammalian skin. This is because, due to its strong absorbance in biological materials, far-UVC light cannot penetrate even the outer (non living) layers of human skin or eye; however, because bacteria and viruses are of micrometer or smaller dimensions, far-UVC can penetrate and inactivate them. We show for the first time that far-UVC efficiently inactivates airborne aerosolized viruses, with a very low dose of 2 mJ/cm 2 of 222-nm light inactivating >95% of aerosolized H1N1 influenza virus. Continuous very low dose-rate far-UVC light in indoor public locations is a promising, safe and inexpensive tool to reduce the spread of airborne-mediated microbial diseases.