A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays

A machine learning method for improving the accuracy of radiation biodosimetry by combining data from the dicentric chromosomes and micronucleus assays

Abstract 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 radiatio...

Full description

Bibliographic Details
Main Authors: Igor Shuryak, Ekaterina Royba, Mikhail Repin, Helen C. Turner, Guy Garty, Naresh Deoli, David J. Brenner
Format: Article
Language:English
Published: Nature Portfolio 2022-12-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-25453-2

Internet

https://doi.org/10.1038/s41598-022-25453-2

Similar Items