ArticleScientific reports2023
Biomarker integration for improved biodosimetry of mixed neutron + photon exposures.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Gene expression as a biological dosimeter: effects of different neutron energies.Radiation and environmental biophysics · 2026Article
- Lipidomics of longer-term effects in the murine model after exposure to mixed field radiation and combined infection.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Radiation emergency medical countermeasures: Current formulary, identified gaps, and future approaches.Animal models and experimental medicine · 2025Review
- Effects of Combined Bacterial Infection and Radiation Injury on Biofluid Metabolite Profiles in the Murine Model.ACS omega · 2025Article
- Multicentered Biospecimen Analyses after 4 Gy Sublethal Total Body Irradiation in Rhesus Macaques.Radiation research · 2025Article
- Intracellular lymphocyte protein biomarkers for early radiological triage in the human population.PloS one · 2025Article
- Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice.Scientific reports · 2024Article
- Impact of Partial Body Shielding from Very High Dose Rates on Untargeted Metabolomics in Biodosimetry.ACS omega · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.