| Autor: |
Karthik Kanagaraj, Michelle A. Phillippi, Elizabeth H. Ober, Igor Shuryak, Norman J. Kleiman, John Olson, George Schaaf, J. Mark Cline, Helen C. Turner |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-024-69852-z |
| Popis: |
Abstract 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 (R2 = 0.79, RMSE = 0.80 Gy, and MAE = 0.63 Gy) and NHP (R2 = 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. |
| Databáze: |
Directory of Open Access Journals |
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