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Validation of a blood biomarker panel for machine learning-based radiation biodosimetry in juvenile and adult C57BL/6 mice. | LitMetric

AI Article Synopsis

  • A large radiological event may hinder quick sample collection, making rapid bioassays necessary for individual dose assessment days after exposure.
  • This study tested a biomarker panel of specific blood proteins and cell counts in irradiated mice to classify exposure and estimate radiation doses within a week.
  • The results indicated high accuracy in distinguishing exposure levels and reconstructing doses, suggesting that this biomarker assay could effectively assess radiation exposure in individuals.

Article Abstract

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 = 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11470949PMC
http://dx.doi.org/10.1038/s41598-024-74953-wDOI Listing

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