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Relationship: 1977


The title of the KER should clearly define the two KEs being considered and the sequential relationship between them (i.e., which is upstream and which is downstream). Consequently all KER titles take the form “upstream KE leads to downstream KE”.  More help

Energy Deposition leads to Increase, DNA strand breaks

Upstream event
Upstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help
Downstream event
Downstream event in the Key Event Relationship. On the KER page, clicking on the Event name under Upstream Relationship will bring the user to that individual KE page. More help

Key Event Relationship Overview

The utility of AOPs for regulatory application is defined, to a large extent, by the confidence and precision with which they facilitate extrapolation of data measured at low levels of biological organisation to predicted outcomes at higher levels of organisation and the extent to which they can link biological effect measurements to their specific causes. Within the AOP framework, the predictive relationships that facilitate extrapolation are represented by the KERs. Consequently, the overall WoE for an AOP is a reflection in part, of the level of confidence in the underlying series of KERs it encompasses. Therefore, describing the KERs in an AOP involves assembling and organising the types of information and evidence that defines the scientific basis for inferring the probable change in, or state of, a downstream KE from the known or measured state of an upstream KE. More help

AOPs Referencing Relationship

This table is automatically generated upon addition of a KER to an AOP. All of the AOPs that are linked to this KER will automatically be listed in this subsection. Clicking on the name of the AOP in the table will bring you to the individual page for that AOP. More help
AOP Name Adjacency Weight of Evidence Quantitative Understanding Point of Contact Author Status OECD Status
Direct deposition of ionizing energy leading to lung cancer adjacent High High Vinita Chauhan (send email) Under development: Not open for comment. Do not cite EAGMST Under Review

Taxonomic Applicability

Select one or more structured terms that help to define the biological applicability domain of the KER. In general, this will be dictated by the more restrictive of the two KEs being linked together by the KER. Authors can indicate the relevant taxa for this KER in this subsection. The process is similar to what is described for KEs (see pages 30-31 and 37-38 of User Handbook) More help
Term Scientific Term Evidence Link
mouse Mus musculus High NCBI
human Homo sapiens High NCBI
rat Rattus norvegicus High NCBI

Sex Applicability

Authors can indicate the relevant sex for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of the User Handbook). More help
Sex Evidence
Unspecific High

Life Stage Applicability

Authors can indicate the relevant life stage for this KER in this subsection. The process is similar to what is described for KEs (see pages 31-32 of User Handbook). More help
Term Evidence
All life stages High

Key Event Relationship Description

Provide a brief, descriptive summation of the KER. While the title itself is fairly descriptive, this section can provide details that aren’t inherent in the description of the KEs themselves (see page 39 of the User Handbook). This description section can be viewed as providing the increased specificity in the nature of upstream perturbation (KEupstream) that leads to a particular downstream perturbation (KEdownstream), while allowing the KE descriptions to remain generalised so they can be linked to different AOPs. The description is also intended to provide a concise overview for readers who may want a brief summation, without needing to read through the detailed support for the relationship (covered below). Careful attention should be taken to avoid reference to other KEs that are not part of this KER, other KERs or other AOPs. This will ensure that the KER is modular and can be used by other AOPs. More help

Direct deposition of ionizing energy refers to imparted energy interacting directly with the DNA double helix and producing randomized damage in the form of strand breaks. Among the different types of damage, the most detrimental type of DNA damage to a cell is the double-strand break (DSB). DSBs are caused by the breaking of the sugar-phosphate backbone on both strands of the DNA double helix molecule, either directly across from each other or several nucleotides apart (Ward, 1988; Iliakis et al., 2015). The number of DSBs produced and the complexity of the breaks is highly dependent on the amount of energy deposited on and absorbed by the cell. This can vary as a function of the dose-rate (Brooks et al., 2016) and the radiation quality which is a function of its linear energy transfer (LET) (Sutherland et al., 2000; Nikjoo et al., 2001; Jorge et al., 2012). LET describes the amount of energy that an ionizing particle transfers to media per unit distance (Smith et al., 2003; Okayasu, 2012a; Christensen et al., 2014).  High LET radiation, such as alpha particle radiation, can deposit larger quantities of energy within a single track than low LET radiation, such as gamma-ray radiation (Kadhim et al., 2006; Franken et al., 2012; Frankenberg et al., 1999; Rydberg et al., 2002; Belli et al., 2000; Antonelli et al., 2015). As such, radiation with higher LETs tends to produce more complex, dense structural damage, particularly in the form of clustered damage, in comparison to lower LET radiation (Nikjoo et al., 2001; Terato and Ide, 2005; Hada and Georgakilas, 2008; Okayasu, 2012a; Lorat et al., 2015; Nikitaki et al., 2016). Thus, the complexity and yield of clustered DNA damage increases with ionizing density (Ward, 1988; Goodhead, 2006).

However, clustered damage can also be induced even by a single radiation track through a cell.

Evidence Supporting this KER

Assembly and description of the scientific evidence supporting KERs in an AOP is an important step in the AOP development process that sets the stage for overall assessment of the AOP (see pages 49-56 of the User Handbook). To do this, biological plausibility, empirical support, and the current quantitative understanding of the KER are evaluated with regard to the predictive relationships/associations between defined pairs of KEs as a basis for considering WoE (page 55 of User Handbook). In addition, uncertainties and inconsistencies are considered. More help
Biological Plausibility
Define, in free text, the biological rationale for a connection between KEupstream and KEdownstream. What are the structural or functional relationships between the KEs? For example, there is a functional relationship between an enzyme’s activity and the product of a reaction it catalyses. Supporting references should be included. However, it is recognised that there may be cases where the biological relationship between two KEs is very well established, to the extent that it is widely accepted and consistently supported by so much literature that it is unnecessary and impractical to cite the relevant primary literature. Citation of review articles or other secondary sources, like text books, may be reasonable in such cases. The primary intent is to provide scientifically credible support for the structural and/or functional relationship between the pair of KEs if one is known. The description of biological plausibility can also incorporate additional mechanistic details that help inform the relationship between KEs, this is useful when it is not practical/pragmatic to represent these details as separate KEs due to the difficulty or relative infrequency with which it is likely to be measured (see page 40 of the User Handbook for further information).   More help

The biological rationale linking the direct deposition of energy on DNA with an increase in DSB formation is strongly supported by numerous literature reviews that are available on this topic (J .F. Ward, 1988; Terato & Ide, 2005; Goodhead, 2006; Asaithamby et al., 2008; Hada & Georgakilas, 2008; Okayasu, 2012b; M. E. Lomax et al., 2013; Moore et al., 2014; Desouky et al., 2015; Sage & Shikazono, 2017; Jeggo, 2009). Ionizing radiation can be in the form of high energy particles (such as alpha particles, beta particles, or charged ions) or high energy waves (such as gamma-rays or X-rays). Ionizing radiation can break the DNA within chromosomes both directly and indirectly, as shown through using velocity sedimentation of DNA through neutral and alkaline sucrose gradients. The most direct path entails a collision between a high-energy particle or photon and a strand of DNA. The high-energy subatomic particles can interact with the orbital electrons of the DNA causing ionization (where electrons are ejected from atoms) and excitation (where electrons are raised to higher energy levels) (Joiner, 2009). These processes ultimately break the phosphodiester backbone.

Additionally, excitation of secondary electrons in the DNA allows for a cascade of ionization events to occur, which can lead to the formation of multiple damage sites (Joiner, 2009). As an example, high-speed electrons will traverse a DNA molecule in a mammalian cell within 10-18 s and 10-14 s, resulting in 100,000 ionizing events per 1 Gy dose in a 10 μm cell (Joiner, 2009). The amount of damage can be influenced by factors such as the cell cycle stage and chromatin structure. It has been shown that in more condensed, packed chromatin structures such as those present in intact cells and heterochromatin, it is more difficult for the DNA to be damaged (Radulescu et al., 2006; Agrawala et al., 2008; Falk et al., 2008; Venkatesh et al., 2016). In contrast, DNA damage is more easily induced in lightly-packed chromatin such as euchromatin, nucleoids, and naked genome DNA (Radulescu et al., 2006; Falk et al., 2008; Venkatesh et al., 2016).

DNA damage can be in the form of DSBs, single-strand breaks, base damage, or the crosslinking of DNA to other molecules  (Smith et al., 2003; Joiner, 2009; Christensen, 2014; Sage and Shikazono, 2017). Of the possible radiation-induced DNA damage types, DSB is considered to be the most harmful to the cell, as there may be severe consequences if this damage is not adequately repaired (Khanna & Jackson, 2001; Smith et al., 2003; Okayasu, 2012a; M. E. Lomax et al., 2013; Rothkamm et al., 2015).

A considerable fraction of DSBs can also be formed in cells through indirect mechanisms.  In this case, deposited energy can split water molecules near DNA, which can generate a significant quantity of reactive oxygen species in the form of hydroxyl free radicals (Ward, 1988; Desouky et al., 2015; Maier et al., 2016). Estimates using models and experimental results suggest that hydroxyl radicals may be present within nanoseconds of energy deposition by radiation (Yamaguchi et al., 2005). These short-lived but highly reactive hydroxyl radicals may react with nearby DNA. This will produce DNA damage, including single-strand breaks and DSBs (Ward, 1988; Desouky et al., 2015; Maier et al., 2016). DNA breaks are especially likely to be produced if the sugar moiety is damaged, and DSBs occur when two single-strand breaks are in close proximity to each other  (Ward, 1988).

Uncertainties and Inconsistencies
In addition to outlining the evidence supporting a particular linkage, it is also important to identify inconsistencies or uncertainties in the relationship. Additionally, while there are expected patterns of concordance that support a causal linkage between the KEs in the pair, it is also helpful to identify experimental details that may explain apparent deviations from the expected patterns of concordance. Identification of uncertainties and inconsistencies contribute to evaluation of the overall WoE supporting the AOPs that contain a given KER and to the identification of research gaps that warrant investigation (seep pages 41-42 of the User Handbook).Given that AOPs are intended to support regulatory applications, AOP developers should focus on those inconsistencies or gaps that would have a direct bearing or impact on the confidence in the KER and its use as a basis for inference or extrapolation in a regulatory setting. Uncertainties that may be of academic interest but would have little impact on regulatory application don’t need to be described. In general, this section details evidence that may raise questions regarding the overall validity and predictive utility of the KER (including consideration of both biological plausibility and empirical support). It also contributes along with several other elements to the overall evaluation of the WoE for the KER (see Section 4 of the User Handbook).  More help

Uncertainties and inconsistencies in this KER are as follows:

  1. Studies have shown that dose-rates (Brooks et al., 2016) and radiation quality (Sutherland et al., 2000; Nikjoo et al., 2001; Jorge et al., 2012) are factors that can influence the dose-response relationship.  
  2. Low-dose radiation has been observed to have beneficial effects and may even invoke protection against spontaneous genomic damage (Feinendegen, 2005; Day et al., 2007; Feinendegen et al., 2007; Shah et al., 2012; Nenoi et al., 2015). This protective effect has been documented in in vivo and in vitro, as reviewed by ICRP (2007) and UNSCEAR (2008) and can vary depending on the cell type, the tissue, the organ, or the entire organism (Brooks et al., 2016).
  3. Depositing ionizing energy is a stochastic event; as such this can influence the location, degree and type of DNA damage imparted on a cell. As an example, studies have shown that mitochondrial DNA may also be an important target for genotoxic effects of ionizing radiation (Wu et al., 1999).
Response-response Relationship
This subsection should be used to define sources of data that define the response-response relationships between the KEs. In particular, information regarding the general form of the relationship (e.g., linear, exponential, sigmoidal, threshold, etc.) should be captured if possible. If there are specific mathematical functions or computational models relevant to the KER in question that have been defined, those should also be cited and/or described where possible, along with information concerning the approximate range of certainty with which the state of the KEdownstream can be predicted based on the measured state of the KEupstream (i.e., can it be predicted within a factor of two, or within three orders of magnitude?). For example, a regression equation may reasonably describe the response-response relationship between the two KERs, but that relationship may have only been validated/tested in a single species under steady state exposure conditions. Those types of details would be useful to capture.  More help

There is evidence of a response-response relationship between the deposition of energy and the frequency of DSBs. In studies encompassing a variety of biological models, radiation types and radiation doses, a positive, linear relationship was found between the radiation dose and the number of DSBs (Sutherland et al., 2000; de Lara et al., 2001; Rothkamm & Lo, 2003; Kuhne et al., 2005; Rube et al., 2008; Grudzenski et al., 2010; Shelke & Das, 2015; Antonelli et al., 2015; Frankenberg et al., 1999). There were, however, two exceptions reported. When human blood lymphocytes were irradiated with X-rays in vitro, a linear relationship was only found for doses ranging from 6 - 500 mGy; at low doses from 0 - 6 mGy, there was a quadratic relationship reported (Beels et al., 2009). Secondly, simulation studies predicted that there would be a non-linear increase in DSBs as energy deposition increased, with a saturation point at higher LETs (Charlton et al., 1989).

This sub-section should be used to provide information regarding the approximate time-scale of the changes in KEdownstream relative to changes in KEupstream (i.e., do effects on KEdownstream lag those on KEupstream by seconds, minutes, hours, or days?). This can be useful information both in terms of modelling the KER, as well as for analyzing the critical or dominant paths through an AOP network (e.g., identification of an AO that could kill an organism in a matter of hours will generally be of higher priority than other potential AOs that take weeks or months to develop). Identification of time-scale can also aid the assessment of temporal concordance. For example, for a KER that operates on a time-scale of days, measurement of both KEs after just hours of exposure in a short-term experiment could lead to incorrect conclusions regarding dose-response or temporal concordance if the time-scale of the upstream to downstream transition was not considered. More help

Data from temporal response studies suggests that DSBs likely occur within seconds to minutes of energy deposition by ionizing radiation. In a variety of biological models, the presence of DSBs has been well documented within 10 - 30 minutes of radiation exposure (Rogakou et al., 1999; Rube et al., 2008; Beels et al., 2009; Kuefner et al., 2009; Grudzenski et al., 2010; Antonelli et al., 2015); there is also evidence that DSBs may actually be present within 3 - 5 minutes of irradiation (Rogakou et al., 1999; Rothkamm & Lo, 2003; Rube et al., 2008; Grudzenski et al., 2010). Interestingly, one study that focussed on monitoring the cells before, during and after irradiation by taking photos every 5, 10 or 15 seconds found that foci indicative of DSBs were present 25 and 40 seconds after collision of the alpha particles and protons with the cell, respectively. The number of foci were found to increase over time until plateauing at approximately 200 seconds after alpha particle exposure and 800 seconds after proton exposure (Mosconi et al., 2011).

After the 30 minute mark, DSBs have been shown to rapidly decline in number. By 24 hours post-irradiation, DSB numbers had declined substantially in systems exposed to radiation doses between 40 mGy and 80 Gy (Rothkamm & Lo, 2003; Rube et al., 2008; Grudzenski et al., 2010; Russo et al., 2015; Antonelli et al., 2015), with the sharpest decrease documented within the first 5 hours (Rogakou et al., 1999; Rube et al., 2008; Kuefner et al., 2009; Grudzenski et al., 2010; Shelke and Das, 2015). Interestingly, DSBs were found to be more persistent when they were induced by higher LET radiation (Antonelli et al., 2015).

Known modulating factors
This sub-section presents information regarding modulating factors/variables known to alter the shape of the response-response function that describes the quantitative relationship between the two KEs (for example, an iodine deficient diet causes a significant increase in the slope of the relationship; a particular genotype doubles the sensitivity of KEdownstream to changes in KEupstream). Information on these known modulating factors should be listed in this subsection, along with relevant information regarding the manner in which the modulating factor can be expected to alter the relationship (if known). Note, this section should focus on those modulating factors for which solid evidence supported by relevant data and literature is available. It should NOT list all possible/plausible modulating factors. In this regard, it is useful to bear in mind that many risk assessments conducted through conventional apical guideline testing-based approaches generally consider few if any modulating factors. More help

Some common clinical radiation modifiers include cisplatin, 5-fluorouracil, thiols, and nitroxides (reviewed in (Citrin and Mitchel, 2014)). Clinical approaches have identified many modulating radiation factors, which are often categorized as either sensitizers or protectors. Sensitizers enhance radiation-induced tumour cell killing, and protectors protect normal tissues from the deleterious effects of ionizing radiation (Citrin & Mitchel, 2014).

Known Feedforward/Feedback loops influencing this KER
This subsection should define whether there are known positive or negative feedback mechanisms involved and what is understood about their time-course and homeostatic limits? In some cases where feedback processes are measurable and causally linked to the outcome, they should be represented as KEs. However, in most cases these features are expected to predominantly influence the shape of the response-response, time-course, behaviours between selected KEs. For example, if a feedback loop acts as compensatory mechanism that aims to restore homeostasis following initial perturbation of a KE, the feedback loop will directly shape the response-response relationship between the KERs. Given interest in formally identifying these positive or negative feedback, it is recommended that a graphical annotation (page 44) indicating a positive or negative feedback loop is involved in a particular upstream to downstream KE transition (KER) be added to the graphical representation, and that details be provided in this subsection of the KER description (see pages 44-45 of the User Handbook).  More help

Not identified.

Domain of Applicability

As for the KEs, there is also a free-text section of the KER description that the developer can use to explain his/her rationale for the structured terms selected with regard to taxonomic, life stage, or sex applicability, or provide a more generalizable or nuanced description of the applicability domain than may be feasible using standardized terms. More help

The domain of applicability relates to all eukaryotic species that contain genetic information in the form of a double strand helix of DNA (Parris et al., 2015; Cannan & Pederson, 2016).


List of the literature that was cited for this KER description using the appropriate format. Ideally, the list of references should conform, to the extent possible, with the OECD Style Guide (OECD, 2015). More help

Agrawala, P.K. et al. (2008), "Induction and repairability of DNA damage caused by ultrasoft X-rays: Role of core events.", Int. J. Radiat. Biol., 84(12):1093–1103. doi:10.1080/09553000802478083.

Antonelli, A.F. et al. (2015), "Induction and Repair of DNA DSB as Revealed by H2AX Phosphorylation Foci in Human Fibroblasts Exposed to Low- and High-LET Radiation: Relationship with Early and Delayed Reproductive Cell Death", Radiat. Res. 183(4):417-31, doi:10.1667/RR13855.1.

Asaithamby, A. et al. (2008), "Repair of HZE-Particle-Induced DNA Double-Strand Breaks in Normal Human Fibroblasts.", Radiat Res. 169(4):437–446. doi:10.1667/RR1165.1.

Beels, L. et al. (2009), "g-H2AX Foci as a Biomarker for Patient X-Ray Exposure in Pediatric Cardiac Catheterization", Are We Underestimating Radiation Risks?":1903–1909. doi:10.1161/CIRCULATIONAHA.109.880385.

Belli M, Cherunbini R, Vecchia MD, Dini V, Moschini G, Signoretti C, Simon G, Tabocchini MA, Tiveron P. 2000. DNA DSB induction and rejoining in V79 cells irradiated with light ions: a constant field gel electrophoresis study. Int J Radiat Biol. 76(8):1095-1104.

Brooks, A.L., D.G. Hoel & R.J. Preston (2016), "The role of dose rate in radiation cancer risk: evaluating the effect of dose rate at the molecular, cellular and tissue levels using key events in critical pathways following exposure to low LET radiation.", Int. J. Radiat. Biol. 92(8):405–426. doi:10.1080/09553002.2016.1186301.

Cannan, W.J. & D.S. Pederson (2016), "Mechanisms and Consequences of Double-Strand DNA Break Formation in Chromatin.", J. Cell Physiol. 231(1):3–14. doi:10.1002/jcp.25048.

Charlton, D.E., H. Nikjoo & J.L. Humm (1989), "Calculation of initial yields of single- and double-strand breaks in cell nuclei from electrons, protons and alpha particles.", Int. J. Rad. Biol., 53(3):353-365, DOI: 10.1080/09553008814552501

Christensen, D.M. (2014), "Management of Ionizing Radiation Injuries and Illnesses, Part 3: Radiobiology and Health Effects of Ionizing Radiation.", 114(7):556–565. doi:10.7556/jaoa.2014.109.

Citrin, D.E. & J.B. Mitchel (2014), "Public Access NIH Public Access.", 71(2):233–236. doi:10.1038/mp.2011.182.doi.

Day, T.K. et al. (2007), "Adaptive Response for Chromosomal Inversions in pKZ1 Mouse Prostate Induced by Low Doses of X Radiation Delivered after a High Dose.", Radiat Res. 167(6):682–692. doi:10.1667/rr0764.1.

Desouky, O., N. Ding & G. Zhou (2015), "ScienceDirect Targeted and non-targeted effects of ionizing radiation.", J. Radiat. Res. Appl. Sci. 8(2):247–254. doi:10.1016/j.jrras.2015.03.003.

Dubrova, Y.E. & M.A. Plumb (2002), "Ionising radiation and mutation induction at mouse minisatellite loci The story of the two generations", Mutat. Res. 499(2):143–150.

Falk, M., E. Lukášová & S. Kozubek (2008), "Chromatin structure influences the sensitivity of DNA to γ-radiation.", Biochim. Biophys. Acta. - Mol. Cell. Res. 1783(12):2398–2414. doi:10.1016/j.bbamcr.2008.07.010.

Feinendegen, L.E. (2005), "UKRC 2004 debate Evidence for beneficial low level radiation effects and radiation hormesis. Radiology.", 78:3–7. doi:10.1259/bjr/63353075.

Feinendegen, L.E., M. Pollycove & R.D. Neumann (2007), "Whole-body responses to low-level radiation exposure: New concepts in mammalian radiobiology.", Exp. Hematol. 35(4 SUPPL.):37–46. doi:10.1016/j.exphem.2007.01.011.

Flegal, M. et al. (2015), "Measuring DNA Damage and Repair in Mouse Splenocytes After Chronic In Vivo Exposure to Very Low Doses of Beta- and Gamma-Radiation.", (July):1–9. doi:10.3791/52912.

Franken NAP, Hovingh S, Cate RT, Krawczyk P, Stap J, Hoebe R, Aten J, Barendsen GW. 2012. Relative biological effectiveness of high linear energy transfer alpha-particles for the induction of DNA-double-strand breaks, chromosome aberrations and reproductive cell death in SW-1573 lung tumour cells. Oncol reports. 27:769-774.

Frankenberg D, Brede HJ, Schrewe UJ, Steinmetz C, Frankenberg-Scwager M, Kasten G, Pralle E. 1999. Induction of DNA Double-Strand Breaks by 1H and 4He Ions in Primary Human Skin Fibroblasts in the LET range of 8 to 124 keV/µm. Radiat Res. 151:540-549.

Goodhead, D.T. (2006), "Energy deposition stochastics and track structure: What about the target?", Radiat. Prot. Dosimetry. 122(1–4):3–15. doi:10.1093/rpd/ncl498.

Grudzenski, S. et al. (2010), "Inducible response required for repair of low-dose radiation damage in human fibroblasts.", Proc. Natl. Acad. Sci. USA. 107(32): 14205-14210, doi:10.1073/pnas.1002213107.

Hada, M. & A.G. Georgakilas (2008), "Formation of Clustered DNA Damage after High-LET Irradiation: A Review.", J. Radiat. Res., 49(3):203–210. doi:10.1269/jrr.07123.

Iliakis, G., T. Murmann & A. Soni (2015), "Alternative end-joining repair pathways are the ultimate backup for abrogated classical non-homologous end-joining and homologous recombination repair: Implications for the formation of chromosome translocations.", Mutat. Res. - Genet. Toxicol. Environ. Mutagen. 793:166–175. doi:10.1016/j.mrgentox.2015.07.001.

Joiner, M. (2009), "Basic Clinical Radiobiology", Edited by. [1] P.J. Sadler, Next-Generation Met Anticancer Complexes Multitargeting via Redox Modul Inorg Chem 52 21.:375. doi:10.1201/b13224.

Jorge, S.-G. et al. (2012), "Evidence of DNA double strand breaks formation in Escherichia coli bacteria exposed to alpha particles of different LET assessed by the SOS response.", Appl. Radiat. Isot. 71(SUPPL.):66–70. doi:10.1016/j.apradiso.2012.05.007.

Kadhim, M.A., M.A. Hill & S.R. Moore, (2006), "Genomic instability and the role of radiation quality.", Radiat. Prot. Dosimetry. 122(1–4):221–227. doi:10.1093/rpd/ncl445.

Khanna, K.K. & S.P. Jackson (2001), "DNA double-strand breaks: signaling, repair and the cancer connection.", Nature Genetics. 27(3):247-54. doi:10.1038/85798.

Kuefner, M.A. et al. (2009), "DNA Double-Strand Breaks and Their Repair in Blood Lymphocytes of Patients Undergoing Angiographic Procedures.", Investigative radiology. 44(8):440-6. doi:10.1097/RLI.0b013e3181a654a5.

Kuefner, M.A. et al. (2015), "Chemoprevention of Radiation-Induced DNA Double-Strand Breaks with Antioxidants.", Curr Radiol Rep (2015) 3: 81.

Kuhne, M., G. Urban & M. Lo, (2005), "DNA Double-Strand Break Misrejoining after Exposure of Primary Human Fibroblasts to C K Characteristic X Rays, 29 kVp X Rays and Co g-Rays.", Radiation Research. 164(5):669-676. doi:10.1667/RR3461.1.

de Lara, C.M. et al. (2001), "Dependence of the Yield of DNA Double-Strand Breaks in Chinese Hamster V79-4 Cells on the Photon Energy of Ultrasoft X Rays.", Radiation Research. 155(3):440-8. doi:10.1667/0033-7587(2001)155[0440:DOTYOD]2.0.CO;2.

Lomax, M.E., L.K. Folkes & P.O. Neill (2013). "Biological Consequences of Radiation-induced DNA Damage: Relevance to Radiotherapy", Statement of Search Strategies Used and Sources of Information Why Radiation Damage is More Effective than Endogenous Damage at Killing Cells Ionising Radiation-induced Do. 25:578–585. doi:10.1016/j.clon.2013.06.007.

Lorat, Y. et al. (2015), "Nanoscale analysis of clustered DNA damage after high-LET irradiation by quantitative electron microscopy – The heavy burden to repair.", DNA Repair (Amst). 28:93–106. doi:10.1016/j.dnarep.2015.01.007.

Maier, P. et al. (2016), "Cellular Pathways in Response to Ionizing Radiation and Their Targetability for Tumor Radiosensitization.", Int. J. Mol. Sci., 14;17(1), pii:E102, doi:10.3390/ijms17010102.

Moore, S., F.K.T. Stanley & A.A. Goodarzi (2014), "The repair of environmentally relevant DNA double strand breaks caused by high linear energy transfer irradiation – No simple task.", DNA repair (Amst), 17:64–73. doi: 10.1016/j.dnarep.2014.01.014.

Mosconi, M., U. Giesen & F. Langner (2011), "53BP1 and MDC1 foci formation in HT-1080 cells for low- and high-LET microbeam irradiations.", Radiat. Envrion. Biophys. 50(3):345–352. doi:10.1007/s00411-011-0366-9.

Nenoi, M., B. Wang & G. Vares (2015), "In vivo radioadaptive response: A review of studies relevant to radiation-induced cancer risk.", Hum. Exp. Toxicol. 34(3):272–283. doi:10.1177/0960327114537537.

Nikitaki, Z. et al. (2016), "Measurement of complex DNA damage induction and repair in human cellular systems after exposure to ionizing radiations of varying linear energy transfer (LET).", Free Radiac. Res. 50(sup1):S64-S78, doi:10.1080/10715762.2016.1232484.

Nikjoo, H. et al. (2001), "Computational approach for determining the spectrum of DNA damage induced by ionizing radiation.", Radiat. Res. 156(5 Pt 2):577–83.

Okayasu, R. (2012a), "Repair of DNA damage induced by accelerated heavy ions-A mini review.", Int. J. Cancer. 130(5):991–1000. doi:10.1002/ijc.26445.

Okayasu, R. (2012b), "Heavy ions — a mini review.", 1000:991–1000. doi:10.1002/ijc.26445.

Parris, C.N. et al. (2015), "Enhanced γ-H2AX DNA damage foci detection using multimagnification and extended depth of field in imaging flow cytometry.", Cytom. Part A. 87(8):717–723. doi:10.1002/cyto.a.22697.

Radulescu I., K. Elmroth & B. Stenerlöw (2006), "Chromatin Organization Contributes to Non-randomly Distributed Double-Strand Breaks after Exposure to High-LET Radiation.", Radiat. Res. 161(1):1–8. doi:10.1667/rr3094.

Rogakou, E.P. et al. (1999), "Megabase Chromatin Domains Involved in DNA Double-Strand Breaks In Vivo.", J. Cell Biol, 146(5):905-16. doi: 10.1083/jcb.146.5.905.

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