Preparedness for a ‘no-notice’ mass-casualty incident: a nuclear detonation scenario

Coleman, C. Norman; Cliffer, Kenneth D.; DiCarlo, Andrea L.; Homer, Mary J; Moyer, Brian R.; Loelius, Shannon G.; Tewell, Adam; Bader, Judith L.; Koerner, John F.

doi:10.1080/09553002.2021.2013573

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Measurement of DNA damage is implemented from diagnostics to basic DNA repair research. Many of these fields necessitate quick assays that assess damage at scale [50]. Capturing immunofluorescent images of foci has been among the most accurate DNA damage-measuring assays, which can be productively automated to increase its capacity and speed [51].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Classification of 53BP1 Foci

Benítez-Jones,

Keegan,

Jamshahi

et al. 2024

Preprint

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Background53BP1 foci are reflective of DNA double-strand break formation and have been used as radiation markers. Manual focus counting, while prone to bias and time constraints, remains the most accurate mode of detecting 53BP1 foci. Several studies have pursued automated focus detection to replace manual methods. Deep learning, spatial 3D images, and segmentation techniques are main components of the highest performing automated methods. While these approaches have achieved promising results regarding accurate focus detection and cell classification, they are not compatible with time-sensitive large-scale applications due to their demand for long run times, advanced microscopy, and computational resources. Further, segmentation of overlapping foci in 2D images has the potential to represent focus morphologies inaccurately.ResultsTo overcome these limitations, we developed a novel method to classify 2D fluorescence microscopy images of 53BP1 foci. Our approach consisted of three key features: (1) general 53BP1 focus classes, (2) varied parameter space composed of properties from individual foci and their respective Fourier transform, and (3) widely-available machine learning classifiers. We identified four main focus classes, which consisted of blurred foci and three levels of overlapping foci. Our parameter space for the training focus library, composed of foci formed by fluorescently-tagged BP1-2, showed a wide correlation range between variables which was validated using a publicly-available library of immunostained 53BP1 foci. Random forest achieved one of the highest and most stable performances for binary and multiclass problems, followed by a support vector machine and k-nearest neighbors. Specific metrics impacted the classification of blurred and low overlap foci for both train and test sets.ConclusionsOur method classified 53BP1 foci across separate fluorescent markers, resolutions, and damage-inducing methods, using off-the-shelf machine learning classifiers, a diverse parameter space, and well-defined focus classes.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Classification of 53BP1 Foci

Benítez-Jones,

Keegan,

Jamshahi

et al. 2024

Preprint

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“…These lessons, of course, may be applicable to other disruptive events that potentially impact the health of large numbers of people (Coleman et al, 2021). Indeed, many of the lessons learned from these experiences could have importance for the near-and long-term management of the COVID-19 pandemic.…”

Section: Conclusion and Summarymentioning

confidence: 97%

Complementary lessons learned from the testing strategies used for radiation emergencies and COVID-19: A white paper from The International Association of Biological and Electron Paramagnetic Resonance (EPR) Radiation Dosimetry (IABERD)

et al. 2022

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As COVID-19 emerged, there are parallels between the responses needed for managing SARS-CoV-2 infections and radiation injuries. While some SARS-CoV-2-infected individuals present as asymptomatic, others exhibit a range of symptoms including severe and rapid onset of high-risk indicators of mortality. Similarly, a variety of responses are also observed after a radiological exposure depending on radiation dose, dose heterogeneity, and biological variability. The impact of acute radiation syndrome (ARS) has guided the identification of many biomarkers of radiation exposure, the establishment of medical management strategies, and development of medical countermeasures in the event of a radiation public health emergency. Biodosimetry has a prominent role for identifying exposed persons during a large scale radiological emergency situation. Identifying exposed individuals is also critical in the case of pandemics such as COVID-19, with the additional goal of controlling the spread of disease. Conclusions and significance: IABERD has taken advantage of its competences in biodosimetry to draw lessons from current practices of managing the testing strategy for nuclear accidents to improve responses to SARS-CoV-2. Conversely, lessons learned from managing SARS-CoV-2 can be used to inform best practices in managing radiological situations. Finally, the potential need to deal with testing modalities simultaneously and effectively in both situations is considered.

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“…Delivery of medical countermeasures within 24 to 48 h postirradiation may confer not only a survival advantage from ARS but may delay the onset of DEARE. However, in the event of widespread exposure from a radiological disaster (e.g., nuclear reactor accident, dirty bomb explosion or nuclear detonation), there will likely be scarce resources and insufficient countermeasures, necessitating a method to inform medical decision-making and efficiently distribute limited resources ( 6 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of miRNA Expression after Whole-Body Irradiation Across Three Strains of Mice

Martello,

Bylicky,

Shankavaram

et al. 2023

Radiation Research

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Whole- or partial-body exposure to ionizing radiation damages major organ systems, leading to dysfunction on both acute and chronic timescales. Radiation medical countermeasures can mitigate acute damages and may delay chronic effects when delivered within days after exposure. However, in the event of widespread radiation exposure, there will inevitably be scarce resources with limited countermeasures to distribute among the affected population. Radiation biodosimetry is necessary to separate exposed from unexposed victims and determine those who requires the most urgent care. Blood-based, microRNA signatures have great potential for biodosimetry, but the affected population in such a situation will be genetically heterogeneous and have varying miRNA responses to radiation. Thus, there is a need to understand differences in radiation-induced miRNA expression across different genetic backgrounds to develop a robust signature. We used inbred mouse strains C3H/HeJ and BALB/c mice to determine how accurate miRNA in blood would be in developing markers for radiation vs. no radiation, low dose (1 Gy, 2 Gy) vs. high dose (4 Gy, 8 Gy), and high risk (8 Gy) vs. low risk (1 Gy, 2 Gy, 4 Gy). Mice were exposed to whole-body doses of 0 Gy, 1 Gy, 2 Gy, 4 Gy, or 8 Gy of X rays. MiRNA expression changes were identified using NanoString nCounter panels on blood RNA collected 1, 2, 3 or 7 days postirradiation. Overall, C3H/HeJ mice had more differentially expressed miRNAs across all doses and timepoints than BALB/c mice. The highest amount of differential expression occurred at days 2 and 3 postirradiation for both strains. Comparison of C3H/HeJ and BALB/c expression profiles to those previously identified in C57BL/6 mice revealed 12 miRNAs that were commonly expressed across all three strains, only one of which, miR-340-5p, displayed a consistent regulation pattern in all three miRNA data. Notably multiple Let-7 family members predicted high-dose and high-risk radiation exposure (Let-7a, Let-7f, Let-7e, Let-7g, and Let-7d). KEGG pathway analysis demonstrated involvement of these predicted miRNAs in pathways related to: Fatty acid metabolism, Lysine degradation and FoxO signaling. These findings indicate differences in the miRNA response to radiation across various genetic backgrounds, and highlights key similarities, which we exploited to discover miRNAs that predict radiation exposure. Our study demonstrates the need and the utility of including multiple animal strains in developing and validating biodosimetry diagnostic signatures. From this data, we developed highly accurate miRNA signatures capable of predicting exposed and unexposed subjects within a genetically heterogeneous population as quickly as 24 h of exposure to radiation.

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Preparedness for a ‘no-notice’ mass-casualty incident: a nuclear detonation scenario

Cited by 8 publications

References 10 publications

Machine Learning Classification of 53BP1 Foci

Machine Learning Classification of 53BP1 Foci

Complementary lessons learned from the testing strategies used for radiation emergencies and COVID-19: A white paper from The International Association of Biological and Electron Paramagnetic Resonance (EPR) Radiation Dosimetry (IABERD)

Comparative Analysis of miRNA Expression after Whole-Body Irradiation Across Three Strains of Mice

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