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Biological Effects of Radiation

Biological Effects of Radiation

All radioactive nuclides emit high-energy particles or electromagnetic waves. When this radiation encounters living cells, it can cause heating, break chemical bonds, or ionize molecules. The most serious biological damage results when these radioactive emissions fragment or ionize molecules. For example, α and β particles emitted from nuclear decay reactions possess much higher energies than ordinary chemical bond energies. When these particles strike and penetrate matter, they produce ions...

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Related Experiment Video

Updated: May 19, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Radiation biomarker screening and dose reconstruction based on machine learning.

Yucheng Wang¹, Chenyang Huang¹, Yan Zhang¹

¹Sino-French Institute of Nuclear Engineering and Technology, Sun Yat-sen University, Zhuhai, China.

International Journal of Radiation Biology

|May 18, 2026

Summary

A new machine learning (ML) framework identifies radiation biomarkers and reconstructs radiation dose rapidly and accurately. This offers a vital tool for nuclear emergency medical rescue and biodosimetry.

Keywords:

Radiation biodosimetry biomarker screening dose reconstruction machine learning nuclear emergency

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Published on: March 11, 2021

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Last Updated: May 19, 2026

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

Published on: September 4, 2017

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Irradiator Commissioning and Dosimetry for Assessment of LQ α and β Parameters, Radiation Dosing Schema, and in vivo Dose Deposition

Published on: March 11, 2021

Area of Science:

Biomedical science
Genomics
Machine learning

Background:

Nuclear emergency medical rescue is crucial for public health.
Rapid and wide-range radiation biodosimetry is needed for nuclear emergencies.
Current biodosimetry methods require improvement for effective emergency response.

Purpose of the Study:

To develop a machine learning (ML) framework for radiation-responsive biomarker screening.
To establish a robust dose reconstruction model for a broad radiation dose range (0-12 Gy).
To lay a preclinical foundation for translational research in human biodosimetry.

Main Methods:

Utilized a female C57BL/6J mice model for preclinical study.
Employed transcriptomic sequencing to evaluate gene expression post-irradiation.
Applied differential expression analysis, Spearman's correlation filtering, and Boruta algorithm for biomarker screening.
Developed a stacked ensemble ML model for dose reconstruction.
Validated gene expression using quantitative real-time PCR (qRT-PCR).

Main Results:

Identified 172 robust radiation biomarkers from 25,654 genes.
Achieved high-accuracy dose reconstruction (R²=0.952, RMSE=0.938 Gy) across 0-12 Gy using a stacked ML model.
Reduced the biomarker panel to 15 key markers with comparable accuracy.
Confirmed biomarker reliability and framework potential through qRT-PCR validation.

Conclusions:

Established an ML framework with multi-stage biomarker screening and stacking ML for rapid, accurate dose reconstruction.
The methodology offers a novel technical solution for nuclear emergency medical response.
The developed framework shows potential for translation into a field-deployable diagnostic platform.