British Journal of Radiology (2005) Supplement_27, 139-145
© 2005 British Institute of Radiology
doi: 10.1259/bjr/78464782
British Journal of Radiology Supplement_27 (2005),139-145 © 2005 The British Institute of Radiology
From cell to organism: the need for multiparametric assessment of exposure and biological effects
A L Brooks, Prof.
Radiation Toxicology, Washington State University, TriCities, 2710 University Drive, Richland, WA 99352, USA
 |
Abstract
|
|---|
Radiation exposure produces a range of responses at every level of biological organisation. As new molecular, cellular, tissue, organ and organism responses are identified, it is essential to relate these responses to exposure parameters. This paper examines the use of different exposure parameters such as dose, fluence, local energy deposition, total energy deposited and hit number, and then relates them to biological responses at different levels of biological organisation. For many responses to high doses of radiation, the dose or energy concentration is the most appropriate and widely used parameter. The best predictor of response for cellular and molecular alterations following exposure to low radiation doses and for non-uniform radiation exposures appears to be "hit" number or the total energy deposited in the biological structure of interest. In many cases, this structure is the cell or the nucleus. To evaluate the probability of producing a cancer in an organ or tissue, the most appropriate measure of physical exposure is radiation dose. Estimation of population risks requires data on the distribution of exposures across that population, an estimate of the excess cancers in the population and a way to relate the exposure to response. The use of total energy in the system as one potential metric to establish this relationship is discussed. This paper provides an overview of the different exposure parameters that can be related to various biological responses, with examples of how these can be related at different levels of biological organisation.
|
Introduction
|
|---|
Radiation dose is a measure of energy concentration deposited following radiation exposure. It is used as a physical parameter to understand the relationships that exist between the energy concentration and the subsequent biological response induced by the exposure. In fact, dose multiplied by modifying factors for radiation and tissue types is often directly substituted for risk when evaluating individual and population cancer risks. In many cases, especially for individual risk estimates, this may not be appropriate. However, individual energy deposition events or interactions are randomly distributed in tissue and are thought to be responsible for the induction of biological changes. The physical measure that predicts biological effect needs to be carefully evaluated at each level of biological organisation. The proper unit of exposure is dependent both on the amount of radiation deposited and on the relevant end-point. The unit of exposure may change at different levels of biological organisation, for example evaluating molecular changes may require a different parameter than that used to characterise the total response in human populations. This paper will discuss the need to match the exposure parameters with the biological effects being evaluated. It will demonstrate that a single physical measurement or calculation may not apply to all different types of biological responses.
|
Radiation dose
|
|---|
Dose is well characterised and discussed by the International Commission on Radiation Units and Measurements (ICRU) [1]. Here, the ICRU defines dose as the energy imparted by ionising radiation to a mass as the mass approaches zero, so that the energy event is delivered to a point in space. This definition is useful for radiation therapy, where it is important to generate isodose contours to ensure optimal exposure to the tumour and minimal exposure or involvement of the surrounding normal tissue. Micro- and nano-dosimetry are other approaches suggested following low doses of radiation where few cells receive multiple interactions and only a fraction of the total cells have energy deposited in them. These approaches provide measures of exposure/dose to small volumes or masses of tissue. However, such definitions are difficult to apply in characterising exposure parameters for radiation protection or in describing parameters to reflect dose or exposure from internally deposited radioactive materials. Dose at a point can then be used to define dose in a finite volume such as an organ or tissue. Thus, for these applications the ICRU uses dose as an energy concentration or as the ratio of the amount of energy deposited in a specific organ or tissue divided by the mass of that organ or tissue. This redefinition of dose is called the average dose, or the tissue or organ dose. The average dose to organs seems to reflect the risk for development of late effects in each organ and is useful in radiation protection.
|
Relating physical parameters to biological end-points
|
|---|
To relate physical parameters to biological changes requires different parameters depending on the level of biological organisation. This is especially important at low radiation doses or in cases where the radiation energy is non-uniformly distributed in the cells or tissues. For most dose–response relationships, it is assumed that there is an exposure–response function that describes changes in biological response as dose increases. Table 1
illustrates some suggested parameters that may be used to generate exposure–response relationships when the responses are measured at different levels of organisation. The table first lists the level of organisation at which the biological changes are being evaluated. Next, it shows one example of the type of phenomena measured at this level of organisation. The table then illustrates the type of radiation used (high vs low-LET (linear energy transfer)). Finally, it suggests exposure parameters or metrics that may be used to best predict the induced biological response.
View this table:
[in this window]
[in a new window]
|
Table 1. Suggested parameters used to generate exposure–response relationships measured at different levels of organisation
|
|
This paper will discuss the responses induced at each level of biological organisation and will suggest why different exposure parameters describe and predict the observed changes. From these discussions it can be suggested that some methods of describing the exposure parameters match the observed biology better than others, and some exposure parameters (energy) can be summed for comparison of the biological responses that are summed in populations, whilst other parameters such as dose (energy/mass) cannot be summed. This is discussed and it is suggested that energy or fluence can be used to replace dose.
|
Exposure parameters for different levels of biological organisation
|
|---|
Molecular responses
Energy deposition events after exposure to ionising radiation are randomly distributed in the tissue so that the initial interaction of radiation with cells and molecules represents a random or stochastic process. The number of interactions increases linearly with increased dose. It is the total number of interactions, the distribution of these interactions and the time-related frequency of the energy-depositing events that are responsible for the biological changes observed. The challenge is to determine the exposure parameter that best reflects the observed biological change. This makes it possible to determine whether the change has a potential to impact cancer risk and whether changes in cancer frequency can be related to the exposure parameter.
|
DNA damage
|
|---|
Initial radiation-induced DNA damage following high doses of high-LET radiation was demonstrated to increase as a linear function of dose and energy deposition [2]. New techniques have demonstrated that even for lower doses, radiation-induced DNA damage appears to be directly related to dose [3]. In addition, it has been possible to visualise the induction of damage in individual cells using labelled protein foci that are postulated to be formed at the site of the DNA damage [3]. This use of 
H2AX protein has been related to the induction of DNA damage and has been shown to increase linearly with dose down to very low levels of exposure [4]. Such studies suggest that the use of dose is appropriate in production of DNA damage.
Studies with a microbeam have demonstrated that the number of H2AX sites increases directly as a function of the number of alpha "hits" in the nucleus. It has also been demonstrated that low-LET radiation exposure at localised sites in the nucleus produces H2AX sites. If these sites reflect DNA damage, perhaps the flux of particles, hit number or radiation concentration in the nucleus will provide a useful parameter for predicting DNA damage.
Doses to individual cells can be calculated based on the distribution of alpha particle passage through small masses such as the mass of the nucleus [5]. If only the cells that have energy deposited in them or that are "hit" respond to produce DNA damage from the exposure, then the mass of the cell may be appropriate for estimating cellular dose and predicting cellular response. However, it was observed that cells that do not receive alpha "hits" also respond with DNA damage. The number of nuclear alpha traversals or "hits" was validated as a useful parameter of exposure measuring DNA damage by the comet assay. The calculated number of cells traversed by an alpha particle and the number of traversals per cell could be related to the number of cells showing DNA damage. These observations agreed with the prediction that direct interactions with alpha particles produced the DNA damage recorded in the comet assay [6]. For this end-point, the number of cells traversed, the number of traversals per cell or the cellular dose can all be related directly to the amount of DNA damage induced in the cell population.
This would suggest that the distribution of cells hit with 0, 1, 2 etc. alpha particles is responsible for the non-random distribution of damage amongst the cells. Using a microbeam to hit each and every cell with known numbers of alpha particles should have decreased the range of distribution of damage among the cells. When this experiment was conducted it was found that the distribution of micronuclei following exposure to known numbers of alpha particles was the same as that observed when the cells received the same average number of hits [7]. As the cells process the initial damage to produce cellular alterations in the total cell population, the result is more dependent on the biology than on the distribution of energy between different cells. The biology thus dominated the cellular responses. The best an investigator can do under many of these situations is to describe the number of alpha traversals through individual cells.
|
Gene expression
|
|---|
Microchip technology has made it possible to measure changes in gene expression rapidly. With microchip methods, the changes in the level of gene expression in thousands of genes can be measured at one time. Studies have been conducted to determine the genes involved in the biological responses elicited by exposure to graded radiation doses from low-LET radiation [8, 9]. For certain genes, the dose–response was linear down to doses as low as 0.02 Gy [10, 11]. Research conducted at Lawrence Livermore National Laboratory using human cells determined that the spectrum of genes that respond by changing their level of expression following radiation exposure is dose dependent [9]. Thus, for low-LET radiation exposure, dose is a useful metric in describing the response of cells to changes in gene expression.
Using low doses of alpha particles, it was possible to demonstrate that when one cell is traversed by an alpha particle, neighbouring cells not traversed by the alpha particle can alter their gene expression [12]. This alteration in gene expression in cells without energy deposition raises a concern about using dose or average energy concentration to describe radiation-induced changes in gene expression induced by high-LET radiation. The number of cells traversed, the number of traversals and the amount of energy deposited in the cell appears to be a better predictor of this molecular response than dose.
Cellular responses
It is important to have useful definitions of exposure environments and dose to describe exposure–response relationships for new cellular phenomena such as "bystander effects" [13, 14], "adaptive responses" [15, 16] and "genomic instability" [17, 18]. The proper metric or parameter of exposure needs to be applied to predict the biological outcome. Since some of these biological phenomena appear to be triggered at the cellular level and are all or none responses, average dose (energy concentration) or dose to a tissue may not be applicable. Other means of expressing exposure that take into consideration the distribution of the energy, the number of energy events and the physical characteristics of the radiation must be considered.
Calculation of dose requires that the energy deposited in the critical volume associated with the response is determined. The appropriate volume or mass of the cells, tissue or organ should be used for dose calculation and is difficult to define. Both physical and biological problems exist in cellular dosimetry. The amount of energy deposited in a nucleus per particle traversal changes as a function of the shape of the nucleus, the angle that the radiation traverses this small volume, the LET of the particle and the distance that that particle travels in the nucleus. These are all physical variables that can be measured or calculated. The energy distribution in a cell and nucleus also is very important. The biological response to a single high-LET alpha particle has been shown to be different compared with depositing the same amount of energy in a cell nucleus using low-LET protons or electrons.
On the biological side, the amount of energy deposited in the critical target required to trigger a biological response in a cell may be different [12, 14, 17]. For the bystander effect [14, 19] or genomic instability [17, 18] to be triggered, a rather large amount of energy must be deposited in the cell nucleus. These responses can occur after the deposition of a single alpha particle, but are not triggered by a low level exposure to low-LET radiation. Addition of more energy or exposure of more cells may not alter some of these responses [14, 20], but for other responses there may be all or nothing responses that are not dose dependent. Such responses require special consideration when assigning an exposure parameter to describe the process. The proper metric may be energy deposited in a single cell in the system to trigger the bystander effect or genomic instability. If so, cellular or nuclear energy deposition may be the parameter of interest.
Several studies have been conducted suggesting that the bystander effect plays a very important role in the induction of biological changes in vitro at very low doses. Exposure of only a very few cells results in a marked increase in the frequency of micronuclei in the whole culture dish [19]. It was noted that exposing 10% of the cells in a culture dish to known numbers of alpha particles resulted in the same transformation frequency as exposing 100% of the cells to the same number of particles. This suggests that the bystander response is induced at different levels dependent on the number of particles and the nuclear dose to the cells, and that it is responsible for a substantial fraction of the total cell transformation response [20, 21]. If the biological response is related to dose or energy concentration, what is the appropriate mass or volume to use in the calculation of radiation dose from high-LET radiation exposures?
On the other hand, very low doses of low-LET radiation are capable of initiating the adaptive response both in cell cultures [15] and in animals [22]. Little evidence exists that the adaptive response can be initiated with high-LET alpha particle exposures. For adaptive responses to low doses of low-LET radiation, the average radiation dose seems to be appropriate. This response is triggered by very low doses, remains active over a narrow range of doses and has little impact on the response as the dose increases to levels where direct radiation effects dominate the response. These phenomena (bystander, adaptive response, genomic instability) are related and may impact our thinking on how radiation interacts with cells and tissues, especially after very low doses of radiation.
As these phenomena interact, they may influence the shape of dose–response relationships in ways not predicted in the past. For example, the frequency of chromosome aberrations has been well defined as a function of total radiation dose and dose rate, and is used extensively in biodosimetry. Exposure of cells to high-LET radiation, such as alpha particles from radon or 239Pu, results in linear relationships between dose and the frequency of aberrations per cell. It is also well established that there are non-linear dose–response relationships between the induction of chromosome aberrations and exposure to high dose rates from low-LET radiation [23]. Many examples of the dose–response relationship between the induction of chromosome aberrations and the type of radiation exposure have been published [23, 24]. Is it possible to examine the molecular mechanisms that result in these very different dose–response relationships as a function of exposure type? In the past, these curves have been explained based on the "hit" theory. This interpretation of dose–response relationships for the induction of chromosome aberrations has been supported by a large number of studies that evaluated the difference between high- and low-LET radiation and the ability of each of these to produce direct and indirect effects. These studies have been carefully reviewed [24].
However, with the advent of recent studies on radiation-induced chromosome damage associated with both the adaptive response and bystander effects, it is possible to develop new radiation paradigms to explain the shape of these dose–response curves. It can be postulated that the linear dose–response relationships observed for the induction of chromosome aberrations following exposure to high-LET radiation are a combination of bystander effects and direct effects. It has been shown that after traversal of a single alpha particle through a single cell, chromosome damage can be produced both in the cell that is "hit" by the alpha particle and in "bystander cells" that have no direct energy deposition [25]. This low-dose-induced bystander effect results in the induction of chromosome aberrations. As the dose increases, the frequency of bystander effects remains constant [25, 26] and the frequency of directly-induced aberrations continues to increase. The combination of these two processes, bystander and direct effects, could result in the apparent linear increase in chromosome aberrations even for very low doses from alpha particles.
The alternative paradigm for the non-linear dose–response observed following acute exposure to low-LET radiation could be that an adaptive response is induced at low doses, which decreases the number chromosome aberrations in the low dose region. Adaptive responses have been shown to decrease both spontaneous and radiation-induced aberration frequency. Such adaptive responses have been demonstrated following very low doses of low-LET radiation. As the dose increases, the number of aberrations produced by direct effects increases as well as the potential for deposition of enough energy in the nucleus to induce bystander effects and to increase aberrations. This is supported by the fact that bystander responses have not been demonstrated following low doses of low-LET radiation. The combination of adaptive response, bystander effects and direct effects of radiation could explain the non-linear dose–response relationship for chromosome aberrations induced by exposure to acute low-LET radiation.
Again, the differences in the shape of dose–response curves may be related to the fact that high-LET radiation is very effective at producing the bystander effects and not effective at producing adaptive responses. This bystander effect could result in linear dose–response relationships at low total doses of high-LET radiation. On the other hand, low doses of low-LET radiation are very effective in producing adaptive responses and are not capable of producing bystander effects. This combination results in non-linear dose–response relationships at low-LET radiation exposures.
As research is conducted to define the genes and signals involved both in adaptive responses and in bystander effects, this hypothesis can be tested directly. Such research may help to explain the shape of the dose–response relationships for this cellular end-point following exposure to very low doses of ionising radiation. It is of interest to determine whether such responses at the cellular level can play a role in providing an explanation for mechanisms involved in radiation-induced cancer.
Tissue and organ changes
Calculation of average dose to the tissue is useful when dealing with the organ or tissue level of biological organisation. The dose estimate from both external exposure and from internally deposited radioactive material is a useful metric for risk estimates. For internally deposited radioactive material, the amount of activity, the distribution of the activity, the energy per disintegration, the time of exposure and the mass of the organ where the activity is deposited can all be measured and used to calculate radiation dose and dose rate. Dose and dose rate from such exposures can be related to biological change and used to estimate cancer risk to a tissue.
When the microscopic distribution of internally deposited radioactivity is very non-uniform in a tissue there remains a question as to what physical parameter is appropriate to predict biological changes and risk. There are many examples of such non-uniform distribution of energy in cells and tissues, especially for high-LET radiation such as alpha particles from radon [27], internally deposited 239PuO2 particles [28], patients treated with Thorotrast [29] and high Z-particles found in space [30]. In several of these cases there were calculations of dose to individual cells [28, 31] from these very non-uniform radiation fields.
The most notable example of the importance of the argument associated with determining the appropriate mass of tissue to be used for estimating radiation risk for the induction of cancer was the "hot particle hypothesis" raised by Tamplin and Cochran [32]. In this hypothesis it was suggested that, since the radiation dose to a small number of cells was very high, the cancer risk would also be very high and had been underestimated by a factor of 115 000. If cells that were traversed by alpha particles were considered as the only cells at risk, then a sphere of tissue with a radius equal to the range of the alpha particle would be the appropriate mass to use in the dose calculation. This assumption resulted in very high local doses being calculated for cells next to the plutonium particles. Using a linear extrapolation from dose to risk, very high risk numbers resulted, suggesting that radiation protection standards were not appropriate. Each of these target assumptions results in a very different tissue mass and volume to be considered in the dose calculations. To test this hypothesis, experimental animal studies were conducted by injecting 239Pu oxide particles into Chinese hamsters [28, 33]. Chinese hamsters were injected with three different sizes of 239Pu oxide particles, which lodged in the liver and remained there for the life of the animal. The classic radiobiology or "hit" theory predicted that there would be a very large response following exposure to the alpha particles from the small particles and a small response following local dose from the large particles. If the "hot particle" hypothesis was correct, the animals injected with the large particles were predicted to develop large numbers of liver cancers. In this study, the chromosome aberration frequency [28] and the number of liver cancers [33] increased as a function of total dose to the liver, and not as a function of local dose to hit cells, number of alpha traversals per cell or number of cells traversed by alpha particles. These data suggest that the liver was responding as an organ to the insult from the plutonium. The cells hit with large amounts of energy are capable of signalling to the non-hit cells, to result in the same amount of damage per unit of energy deposited in the organ. This study, along with other data, refuted the "hot particle hypothesis" [34]. Bystander effects are thus demonstrated for alpha particle exposure both in tissue culture and in experimental animals.
Individuals
Following high doses of radiation, the metric of exposure remains dose. This is useful both for whole body radiation and for defining the field used in radiation therapy. The end-points of interest for high doses are the ability to kill the cancer cells and the response of the normal tissue that has been excluded from the radiation field. However, for low doses of radiation, which is the primary subject of this paper, the end-point of interest is the induction of cancer. Extrapolating the dose to the organ to a risk measurement requires additional biological information on the sensitivity of the tissue. These tissue differences have been accounted for by the use of tissue weighting factors [35]. Using such tissue weighting factors makes it possible to compare risks for different organs given the same radiation exposure. When dose in Gy is multiplied by tissue and radiation weighting factors it becomes a Sv, which is used in radiation protection as a surrogate for risk. Using Sv, the risks from different organs are often added to get the risk to the individual. These calculated individual risks are in reality a reflection of a population risk and not individual risks. Without extensive biological data about the individual's genetic background, sensitivity to exposure, DNA repair capability etc., risk cannot be accurately estimated for any individual.
Populations
The radiation response of interest in a population is excess cancer, which can be measured using standard epidemiology following high doses of radiation. The between-individual biological variation in any population makes it necessary to have a metric for exposure that can be related to the sum or net numbers of excess responses. In other words, both the X- and Y-axis need to be additive in an exposure–response plot for a population. Since dose is a ratio of energy/unit mass, it is not additive. Thus, dose cannot be used in epidemiological studies. The Man-Sv unit has been used [36] but is not readily applicable to risk. An alternative approach has been to take the average dose to the population and to multiply it by the total number of people in the population [37]. This provides an estimate of total energy deposited in the population. Since energy is additive and since excess numbers of cancer in a population are also additive, energy is a useful physical metric for evaluating population risk. These dosimetric approaches have been carefully reviewed [38]. By relating total energy in the population to excess cancers, it is possible to make a more accurate estimate of population risk. Such an approach can be readily used to estimate risk using excess numbers of cancers derived from standard epidemiological studies. This approach also helps to demonstrate that large amounts of energy are required to induce cancer above the background level, and that when additional individuals are added to a study population, additional energy has been added to the population. This property is not apparent when energy concentration or dose is used as the physical metric. Using dose as the metric, more individuals can be added to any population without changing the location of the dose on the X-axis. Thus, dose tends to mask the fact that excess energy in the population is the parameter that is producing the excess cancers. It is suggested that in population studies the total energy in the population is the physical measure to be related to the induction of excess cancers.
|
Summary
|
|---|
This brief review suggests that for each level of biological organisation, it may be necessary to use a different parameter to characterise physical exposure. Ultimately, dose is focused on predicting the outcome of therapy from radiation exposure. This paper is designed to outline what appears to be the appropriate physical parameter to relate to biological effects at different levels of biological organisation. It is difficult to relate biological changes at various levels of organisation to radiation dose. However, the responses can be measured at multiple levels of biological organisation and need to be matched to the appropriate metric of exposure. This paper has provided a table with suggestions for relating radiation exposure to the biological end-points of interest. It illustrates that for many levels of organisation, dose is the appropriate exposure parameter. However, for the molecular, cellular and whole population studies, other parameters must be considered.
For low-LET radiation exposure and the measurement of molecular end-points associated with these exposures, radiation dose still seems to be the appropriate exposure metric. However, high-LET radiation can result in all or nothing responses depending on whether the cell is traversed by the particle or not. The induction of bystander effects and genomic instability suggest that at the cellular level the number of cells traversed and the energy deposited in those cells are important. Since it seems that organs and tissues respond as a unit, the total dose to the organ is more appropriate than any measures of individual cellular doses. Finally, at the human population level, it is essential to compare the excess responses, namely cancers, to the excess exposure. Since the excess responses can be added, it is important to use a parameter that can also be added. It is suggested that this parameter is the total energy deposited in the population being studied.
|
Footnotes
|
|---|
This research was supported by the Office of Biological and Environmental Research (OBER), US Department of Energy, through a Grant No. DE-FG03-99ER62787 to Washington State University. 
|
References
|
|---|
- International Commission on Radiation Units and Measurements. Quantities and units in radiation protection dosimetry, ICRU Publication 51. Bethesda, MD, ICRU, 1991.
- Rydberg B, Lobrich M, Cooper PK. DNA double-strand breaks induced by high-energy neon and iron ions in human fibroblasts. I. Pulsed-field gel electrophoresis method. Radiat Res 1994;139:133–41.[Medline]
- Burma S, Chen BP, Murphy M, Kurimasa A, Chen DJ. ATM phosphorylates histone H2AX in response to DNA double-strand breaks. J Biol Chem 2001;276:42462–7.[Abstract/Free Full Text]
- Rothkamm K, Lobrich M. Evidence for a lack of DNA double-strand break repair in human cells exposed to very low x-ray doses. Proc Natl Acad Sci USA 2203;100:5057–62.
- Hui TE, James AC, Jostes RF, Schwartz JL, Swinth KL, Cross FT. Evaluation of an alpha probe detector for in vitro cellular dosimetry. Health Phys 1993;64:647–52.[Medline]
- Jostes RF, Hui TE, Cross FT. Single-cell gel technique supports hit probability calculations. Health Phys 1993;64:675–9.[Medline]
- Nelson JM, Brooks AL, Metting NF, Khan MA, Buschbom RL, Miick R, et al. Clastogenic effects of defined numbers of 3.2 MeV alpha particles on individual CHO-K1 cells. Radiat Res 1996;145:568–74.[Medline]
- Amundson SA, Do KT, Fornace AJ Jr. Induction of stress genes by low doses of gamma rays. Radiat Res 1999;152:225–31.[Medline]
- Mascio-Kegelmeyer L, Tomascik-Cheeseman L, Burnett MS, van Hummelen P, Wyrobek AJ. A ground truth approach to accurate quantitation of fluorescence microarrays. SPIE Proceedings 2001;4266:35–45.
- Amundson SA, Fornace AJ Jr. Gene expression profiles for monitoring radiation exposure. Radiat Prot Dosimetry 2001;97:11–6.[Abstract]
- Fornace AJ Jr, Amundson SA, Bittner M, Myers TC, Meltzer P, Weinsten JN, et al. The complexity of radiation stress responses: analysis by informatics and functional genomics approaches. Gene Expr 1999;7:387–400.[Medline]
- Azzam EI, de Toledo SM, Gooding T, Little JB. Intercellular communication is involved in the bystander regulation of gene expression in human cells exposed to very low fluences of alpha particles. Radiat Res 1998;150:497–504.[Medline]
- Zhou H, Randers-Pehrson G, Waldren CA, Vannais D, Hall EJ, Hei TK. Induction of a bystander mutagenic effect of alpha particles in mammalian cells. Proc Natl Acad Sci USA 2000;97:2099–104.[Abstract/Free Full Text]
- Hall EJ. The bystander effect. Health Phys 2003;85:31–5.[Medline]
- Wolff S. The adaptive response in radiobiology: evolving insights and implications. Environ Health Perspect 1998;106:277–83.
- Redpath JL, Lu Q, Lao X, Molloi S, Elmore E. Low doses of diagnostic energy X-rays protect against neoplastic transformation in vitro. Int J Radiat Biol 2003;79:235–40.[Medline]
- Kadhim MA, Lorimore SA, Hepburn MD, Goodhead DT. Alpha-particle-induced chromosomal instability in human bone marrow cells. Lancet 1994;344:987–8.[Medline]
- Ponnaiya B, Cornforth MN, Ullrich RL. Radiation-induced chromosomal instability in BALB/c and C57BL/6 mice: the difference is as clear as black and white. Radiat Res 1997;147:121–5.[Medline]
- Prise KM, Belyakov OV, Folkard M, Michael BD. Studies of bystander effects in human fibroblasts using a charged particle microbeam. Int J Radiat Biol 1998;74:793–8.[Medline]
- Sawant SG, Randers-Pehrson G, Metting NF, Hall EJ. Adaptive response and the bystander effect induced by radiation in C3H 10T (1/2) cells in culture. Radiat Res 2001;156:177–80.[Medline]
- Zhao YL, Piao CQ, Hall EJ, Hei TK. Mechanisms of radiation-induced neoplastic transformation of human bronchial epithelial cells. Radiat Res 2001;155:230–34.[Medline]
- Mitchel RE, Jackson JS, Morrison DP. Low doses of radiation increase the latency of spontaneous lymphomas and spinal osteosarcomas in cancer-prone, radiation-sensitive Trp53 heterozygous mice. Radiat Res 2003;159:320–7.[Medline]
- Brooks AL. Chromosome damage in liver cells from low dose rate alpha, beta and gamma irradiation: derivation of RBE. Science 1975;190:1090–2.[Abstract/Free Full Text]
- Bender MA, Awa AA, Brooks AL, Evans HJ, Groer PG, Littlefield LG, et al. Current status of cytogenetic procedures to detect and quantify previous exposures to radiation. Mutat Res 1988;196:103–59.[Medline]
- Geard CR, Jenkins-Baker G, Marino SA, Ponnaiya B. Novel approaches with track segment alpha particles and cell co-cultures in studies of bystander effects. Radiat Prot Dosimetry 2002;99:233–6.[Abstract]
- Belyakov OV, Malcolmson AM, Folkard M, Prise KM, Michael BD. Direct evidence for a bystander effect of ionizing radiation in primary human fibroblasts. Br J Cancer 2001;84:674–9.[Medline]
- Samet JM, Brenner D, Brooks AL, Ellett WH, Gilbert ES, Goodhead DT, et al. Health effects of exposure to radon. BEIR VI, Board on Radiation Effects Research, National Research Council. Washington D.C.: National Academy Press, 1999.
- Brooks AL, Retherford JC, McClellan RO. Effect of 239PuO2, particle number and size on the frequency and distribution of chromosome aberrations in the liver of the Chinese hamster. Radiat Res 1974;59:693–709.[Medline]
- Brooks AL, Guilmette RA, Evans MJ, Diel JH. The induction of chromosome aberrations in the liver of Chinese hamsters by injected thorotrast. In: Gossner W, Gerber GB, Hagen U, Luz A, eds. The radiobiology of radium and Thorotrast. Munchen, Germany: Urban and Scwarzemberg, 1986:197–201.
- Brooks AL, Bao S, Rithidech K, Couch LA, Braby LA. Relative effectiveness of HZE iron-56 particles for the induction of cytogenetic damage in vivo. Radiat Res 2001;155:353–9.[Medline]
- Kaul A, Foll U, Haase VA, Palme G, Riedel W, Stolpmann HJ. Microdistribution of Thorotrast and dose to cellular structures. Environ Res 1979;18:13–22.[Medline]
- Tamplin AR, Cochran TB. Radiation standards for hot particles. A report on the inadequacy of existing radiation protection standards related to internal exposures of man to insoluble particles of plutonium and other alpha-emitting hot particles. Washington D.C.: Natural Resources Defense Council, 1974.
- Brooks AL, Benjamin SA, Hahn FF, Brownstein DG, Griffith MW, McClellan RO. The induction of liver tumors by 239Pu citrate or 239PuO2 particles in the Chinese hamster. Radiat Res 1983;96:135–51.[Medline]
- Bair WJ, Richmond CR, Wachholz BW. A radiobiological assessment of the spatial distribution of radiation dose from inhaled plutonium. WASH 1320. Washington D.C.: U. S. Atomic Energy Commission, 1974.
- National Council on Radiation Protection and Measurements. Limitation of exposure to ionizing radiation, NCRP Report No. 116. Besthesda, MD: NCRP, 1993.
- International Commission on Radiation Units and Measurements. 1990 Recommendations of the International Commission on Radiological Protection, Publication 60. Annals of the ICRP 1991;21(1–3). Oxford, New York: Pergamon Press, 1990.
- Kaul A, Aurand K, Bonka H, Gumprecht D, Hardt HJ, Jacobi W, et al. Possibilities and limits for applying the concept of collective dose. Health Phys 1987;53:9–10.[Medline]
- Kellerer AM. Risk estimates for radiation-induced cancer – the epidemiological evidence. Radiat Environ Biophys 2000;39:17–24.[Medline]
Top
Abstract
Introduction
