ArticlePloS one2018
Monte Carlo studies on photon interactions in radiobiological experiments.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 19 citations in OpenAlex.
- On the effectiveness of proton boron fusion therapy (PBFT) at cellular level.Scientific reports · 2022Article
- Chemical Overview of Gel Dosimetry Systems: A Comprehensive Review.Gels (Basel, Switzerland) · 2022Review
- Development of PHITS graphical user interface for simulation of positron emitting radioisotopes production in common biological materials during proton therapy.Journal of radiation research · 2022Article
- Article
- MCHP (Monte Carlo + Human Phantom): Platform to facilitate teaching nuclear radiation physics.PloS one · 2021Article
- Advances on inorganic scintillator-based optic fiber dosimeters.EJNMMI physics · 2020Review
- Radiation-induced rescue effect.Journal of radiation research · 2019Review
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
X-ray and γ-ray photons have been widely used for studying radiobiological effects of ionizing radiations. Photons are indirectly ionizing radiations so they need to set in motion electrons (which are a directly ionizing radiation) to perform the ionizations. When the photon dose decreases to below a certain limit, the number of electrons set in motion will become so small that not all cells in an "exposed" cell population can get at least one electron hit. When some cells in a cell population are not hit by a directly ionizing radiation (in other words not irradiated), there will be rescue effect between the irradiated cells and non-irradiated cells, and the resultant radiobiological effect observed for the "exposed" cell population will be different. In the present paper, the mechanisms underlying photon interactions in radiobiological experiments were studied using our developed NRUphoton computer code, which was benchmarked against the MCNP5 code by comparing the photon dose delivered to the cell layer underneath the water medium. The following conclusions were reached: (1) The interaction fractions decreased in the following order: 16O > 12C > 14N > 1H. Bulges in the interaction fractions (versus water medium thickness) were observed, which reflected changes in the energies of the propagating photons due to traversals of different amount of water medium as well as changes in the energy-dependent photon interaction cross-sections. (2) Photoelectric interaction and incoherent scattering dominated for lower-energy (10 keV) and high-energy (100 keV and 1 MeV) incident photons. (3) The fractions of electron ejection from different nuclei were mainly governed by the photoelectric effect cross-sections, and the fractions from the 1s subshell were the largest. (4) The penetration fractions in general decreased with increasing medium thickness, and increased with increasing incident photon energy, the latter being explained by the corresponding reduction in interaction cross-sections. (5) The areas under the angular distribution curves of photons exiting the medium layer and subsequently undergoing interactions within the cell layer became smaller for larger incident photon energies. (6) The number of cells suffering at least one electron hit increased with the administered dose. For larger incident photon energies, the numbers of cells suffering at least one electron hit became smaller, which was attributed to the reduction in the photon interaction cross-section. These results highlighted the importance of the administered dose in radiobiological experiments. In particular, the threshold administered doses at which all cells in the exposed cell array suffered at least one electron hit might provide hints on explaining the intriguing observation that radiation-induced cancers can be statistically detected only above the threshold value of ~100 mSv, and thus on reconciling controversies over the linear no-threshold model.
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