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Journal articles on the topic 'Biological dose'

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Author: Grafiati
Published: 23 September 2022

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1

Oftedal, Per. "Biological low-dose radiation effects." Mutation Research/Reviews in Genetic Toxicology 258, no. 2 (September 1991): 191–205. http://dx.doi.org/10.1016/0165-1110(91)90009-k.

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2

Alber, Markus. "Normal tissue dose-effect models in biological dose optimisation." Zeitschrift für Medizinische Physik 18, no. 2 (June 2008): 102–10. http://dx.doi.org/10.1016/j.zemedi.2007.08.002.

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3

Jaikuna, Tanwiwat, Phatchareewan Khadsiri, Nisa Chawapun, Suwit Saekho, and Ekkasit Tharavichitkul. "Isobio software: biological dose distribution and biological dose volume histogram from physical dose conversion using linear-quadratic-linear model." Journal of Contemporary Brachytherapy 1 (2017): 44–51. http://dx.doi.org/10.5114/jcb.2017.66082.

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4

IWASAKI, Toshiyasu, and Masanori TOMITA. "Biological Effects of Low dose/Low dose-rate Ionizing Radiation." Journal of the Atomic Energy Society of Japan 51, no. 9 (2009): 668–73. http://dx.doi.org/10.3327/jaesjb.51.9_668.

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5

Pinto, M., and A. Amaral. "Biological dose assessment after low-dose overexposures in nuclear medicine." Radiation Protection Dosimetry 162, no. 3 (November 13, 2013): 254–59. http://dx.doi.org/10.1093/rpd/nct285.

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6

Koteles, G. J. "Biological responses in low-dose range." International Journal of Low Radiation 2, no. 1/2 (2006): 97. http://dx.doi.org/10.1504/ijlr.2006.007900.

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7

Joiner, M. C., S. A. Krueger, G. D. Wilson, and B. Marples. "41 Low-dose hypersensitivity: Biological mechanism." Radiotherapy and Oncology 78 (March 2006): S15. http://dx.doi.org/10.1016/s0167-8140(06)80535-9.

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8

Nenot, J. C. "Biological Indicators for Radiation Dose Assessment." International Journal of Radiation Biology and Related Studies in Physics, Chemistry and Medicine 52, no. 1 (January 1987): 177. http://dx.doi.org/10.1080/09553008714551601.

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9

Chen, Y., X. K. Yan, J. Du, Z. D. Wang, X. Q. Zhang, F. G. Zeng, and P. K. Zhou. "Biological dose estimation for accidental supra-high dose gamma-ray exposure." Radiation Measurements 46, no. 9 (September 2011): 837–41. http://dx.doi.org/10.1016/j.radmeas.2011.04.001.

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10

Zhang, Qinghui, Suqing Tian, and Giovanni Borasi. "A new definition of biological effective dose: The dose distribution effects." Physica Medica 31, no. 8 (December 2015): 1060–64. http://dx.doi.org/10.1016/j.ejmp.2015.07.145.

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11

Kim, Yusung, and Wolfgang A. Tomé. "Dose-painting IMRT optimization using biological parameters." Acta Oncologica 49, no. 8 (April 29, 2010): 1374–84. http://dx.doi.org/10.3109/02841861003767539.

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12

Siu, L. "16 Is There an Optimal Biological Dose?" European Journal of Cancer 48 (November 2012): 8–9. http://dx.doi.org/10.1016/s0959-8049(12)71815-4.

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13

ZAIDER, M. "Definitions of physical and biological low dose." International Journal of Radiation Biology 74, no. 5 (January 1998): 633–37. http://dx.doi.org/10.1080/095530098141212.

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14

Siegel, Jeffry A., Michael G. Stabin, and Robert M. Sharkey. "Renal Dosimetry: Ready for Biological Effective Dose?" Cancer Biotherapy and Radiopharmaceuticals 25, no. 5 (October 2010): 589–91. http://dx.doi.org/10.1089/cbr.2010.0851.

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15

Gemmel, A., B. Hasch, M. Ellerbrock, W. K. Weyrather, and M. Krämer. "Biological dose optimization with multiple ion fields." Physics in Medicine and Biology 53, no. 23 (November 12, 2008): 6991–7012. http://dx.doi.org/10.1088/0031-9155/53/23/022.

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16

Chen, Mingli, Weiguo Lu, Quan Chen, Kenneth Ruchala, and Gustavo Olivera. "Adaptive fractionation therapy: II. Biological effective dose." Physics in Medicine and Biology 53, no. 19 (September 9, 2008): 5513–25. http://dx.doi.org/10.1088/0031-9155/53/19/016.

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17

Rontó, Gy, S. Gáspár, and A. Bérces. "Phages T7 in biological UV dose measurements." Journal of Photochemistry and Photobiology B: Biology 12, no. 3 (February 1992): 285–94. http://dx.doi.org/10.1016/1011-1344(92)85030-x.

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18

Khayitov, J. B., G. I. Shaikhova, D. D. Achilov, and M. J. Allaeva. "Nutritional and biological value of natural-bio shoots mung bean “Mungoltin”. Food and biological values." CARDIOMETRY, no. 21 (February 23, 2022): 78–84. http://dx.doi.org/10.18137/cardiometry.2022.21.7884.

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Based on the results of our own research, examination of scientific dossier materials and reference literature data, it was established that dry powder Mungoltin made from the shoots of mung beans produced by Oriona-Scorpion LLC (Uzbekistan) contains a sufficient amount of protein, minerals, vitamins and dietary fiber, does not have a negative impact on the health status of experimental animals and does not result in functional and material cumulation. Acute systemic toxicity testing with intragastric administration of Mungoltin was carried out in 18 adult white male rats. Animals were divided into 4 groups. The animals of the first group received a nutrition dose of 5000 mg/ kg; the white rats of the second group were administered with a dose of 7500 mg/kg, and the rodents in the third group were given a dose of 10000 mg/kg, respectively. The animals in group 4 (the reference group) received distilled water. Upon a prolonged intragastric exposure to Mungoltin, no changes in biochemical parameters were detected. The activity indicators of alkaline phosphatase, trans-aminase enzymes and total protein in the blood serum did not differ significantly from those found in the reference group. Therefore, using Mungoltin will not cause a cytotoxic effect in relation to normal highly proliferating cells in an organism. The results of histomorphological studies of tissues of internal organs upon intragastric administration of Mungoltin within 30 days confirm the absence of toxic effects. According to toxicity parameters under the conditions of the above acute experiments, Mungoltin can be attributed to class 5 practically as a non-toxic substance.
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19

Krämer, Michael, and Oliver Jäkel. "Biological dose optimization using ramp-like dose gradients in ion irradiation fields." Physica Medica 21, no. 3 (July 2005): 107–11. http://dx.doi.org/10.1016/s1120-1797(05)80011-0.

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20

Hobbs, Robert F., and George Sgouros. "Calculation of the biological effective dose for piecewise defined dose-rate fits." Medical Physics 36, no. 3 (February 20, 2009): 904–7. http://dx.doi.org/10.1118/1.3070587.

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21

Singh, Rachana, Hania Al-Hallaq, Charles A. Pelizzari, Gregory P. Zagaja, Andrew Chen, and Ashesh B. Jani. "Dosimetric quality endpoints for low-dose-rate prostate brachytherapy using biological effective dose (bed) vs. conventional dose." Medical Dosimetry 28, no. 4 (December 2003): 255–59. http://dx.doi.org/10.1016/j.meddos.2003.04.001.

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22

Hansen, Joyce M., Niki Fidopiastis, Trabue Bryans, Michelle Luebke, and Terri Rymer. "Radiation Sterilization: Dose Is Dose." Biomedical Instrumentation & Technology 54, s1 (June 1, 2020): 45–52. http://dx.doi.org/10.2345/0899-8205-54.s3.45.

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Abstract In the radiation sterilization arena, the question often arises as to whether radiation resistance of microorganisms might be affected by the energy level of the radiation source and the rate of the dose delivered (kGy/time). The basis for the question is if the microbial lethality is affected by the radiation energy level and/or the rate the dose is delivered, then the ability to transfer dose among different radiation sources could be challenged. This study addressed that question by performing a microbial inactivation study using two radiation sources (gamma and electron beam [E-beam]), two microbial challenges (natural product bioburden and biological indicators), and four dose rates delivered by three energy levels (1.17 MeV [gamma], 1.33 MeV [gamma], and 10 MeV [high-energy E-beam]). Based on analysis of the data, no significant differences were seen in the rate of microbial lethality across the range of radiation energies evaluated. In summary, as long as proof exists that the specified dose is delivered, dose is dose.
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23

Anferov, Vladimir, and Indra J. Das. "Biological Dose Estimation Model for Proton Beam Therapy." International Journal of Medical Physics, Clinical Engineering and Radiation Oncology 04, no. 02 (2015): 149–61. http://dx.doi.org/10.4236/ijmpcero.2015.42019.

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24

Wilson, James D. "Biological Bases for Cancer Dose-Response Extrapolation Procedures." Environmental Health Perspectives 90 (January 1991): 293. http://dx.doi.org/10.2307/3430881.

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25

Zhang, Xiao-Hong, Xiao-Dan Hu, Su-Ying Zhao, Li-Hua Xie, Yu-Ji Miao, Qun Li, Rui Min, Pei-Dang Liu, and Hai-Qian Zhang. "Methemoglobin-Based Biological Dose Assessment for Human Blood." Health Physics 111, no. 1 (July 2016): 30–36. http://dx.doi.org/10.1097/hp.0000000000000522.

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26

Zhou, Liqi, Jingdong Song, Judy Kim, Xudong Pei, Chen Huang, Xiaoqing Pan, Peter Nellist, Peijun Zhang, Angus Kirkland, and Peng Wang. "Low Dose Electron Ptychography for Cryo-biological Imaging." Microscopy and Microanalysis 26, S2 (July 30, 2020): 1488–90. http://dx.doi.org/10.1017/s1431927620018292.

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27

Wilson, J. D. "Biological bases for cancer dose-response extrapolation procedures." Environmental Health Perspectives 90 (January 1991): 293–96. http://dx.doi.org/10.1289/ehp.90-1519475.

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28

Wambersie, A., H. G. Menzel, R. A. Gahbauer, D. T. L. Jones, B. D. Michael, and H. Paretzke. "Biological Weighting of Absorbed Dose in Radiation Therapy." Radiation Protection Dosimetry 99, no. 1 (June 1, 2002): 445–52. http://dx.doi.org/10.1093/oxfordjournals.rpd.a006829.

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29

Yordanov, Pencho, and Jörg Stelling. "Steady-State Differential Dose Response in Biological Systems." Biophysical Journal 114, no. 3 (February 2018): 723–36. http://dx.doi.org/10.1016/j.bpj.2017.11.3780.

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30

Sharma, B. Arunkumar, Th Tomcha Singh, L. Jaichand Singh, Y. Indibor Singh, and Y. Sobita Devi. "Biological effective doses in the intracavitary high dose rate brachytherapy of cervical cancer." Journal of Contemporary Brachytherapy 4 (2011): 188–92. http://dx.doi.org/10.5114/jcb.2011.26469.

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31

Ruas, D. B., A. H. Mounteer, A. C. Lopes, B. L. Gomes, F. D. Brandão, and L. M. Girondoli. "Combined chemical biological treatment of bleached eucalypt kraft pulp mill effluent." Water Science and Technology 55, no. 6 (March 1, 2007): 143–50. http://dx.doi.org/10.2166/wst.2007.222.

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Effectiveness of ozonation before and after biological treatment for removal of recalcitrant organic matter in bleached kraft pulp effluents was compared. Two industrial ECF bleached eucalypt kraft pulp effluents (E1 and E2) were pretreated with 100 mg O3/L. Raw and pretreated effluents were treated biologically in bench-scale sequencing batch reactors, under constant conditions. Following biological treatment, effluents were post-treated with 100 and 200 mg O3/L. Effluent pretreatment increased effluent biodegradability by 10% in E1 and 24% in E2. Combined O3-biological treated led to small but significant increases in COD, BOD and lignin removal over biological treatment alone, but pretreatment had no significant effect on effluent colour and carbohydrate removal. Ozone pretreatment did not affect biological activity during treatment of effluent E1 but resulted in a 38% lower specific oxygen uptake rate in effluent E2. At an equivalent dose of 100 mg/L, pre-ozonation produced better quality effluent than post-ozonation, especially with regard to COD and colour. Likewise, when an equivalent dose of 200 mg/L was applied, splitting the dose equally between pre- and post-treatments was more efficient than applying the entire dose in the post-treatment. The potential for combined chemical–biological treatment to improve effluent quality has been confirmed in this study.
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32

Pulido Valente, F. "Evaluation of biological effect in radiotherapy biological equivalent dose versus surviving fraction criteria." Radiotherapy and Oncology 25, no. 3 (November 1992): 221. http://dx.doi.org/10.1016/0167-8140(92)90277-2.

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33

Merens, Wendelien, and Willem van der Does. "Low-Dose Tryptophan Depletion." Biological Psychiatry 62, no. 5 (September 2007): 542–43. http://dx.doi.org/10.1016/j.biopsych.2006.09.024.

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34

Dörr, W., P. Wust, J. Kotzerke, and L. Oehme. "Influence of time-dose-relationships in therapeutic nuclear medicine applications on biological effectiveness of irradiation." Nuklearmedizin 47, no. 05 (2008): 205–9. http://dx.doi.org/10.3413/nukmed-0151.

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Summary Aim: The biological effectiveness of irradiation is influenced not only by the total dose but also the rate at which this dose is administered. Tolerance dose estimates from external radiation therapy with a conventional fractionation protocol require adaptation for application in targeted radionuclide therapy. Methods: The linear-quadratic model allows for calculation of the biologically effective dose (BED) and takes into consideration tissue specific factors (recovery capacity) as well as dose rate effects (recovery kinetics). It can be applied in radionuclide therapy as well. For relevant therapeutic radionuclides (e. g. 188Re, 90Y, 177Lu, and 131I), the effect of different physical decay times and variable biological half-lives on BED was calculated for several organs. Results: BED is markedly increased using 188Re compared to longer-lived radionuclides. The effect is dose-dependent and tissue-specific, resulting, for example, in higher effects on the kidneys compared to bone marrow. Therefore, in unfavourable conditions (e. g. reduced recovery capacity due to concomitant diseases or previous therapy), the BED may exceed organ dose tolerance. Conclusion: Time-dose-relationships have to be taken into consideration by the calculation of BED for internal radionuclide therapy. The biological effectiveness depends on dose- and tissue-specific factors and is much more pronounced in 188Re than in 90Y and other longer living radionuclides. Determination of organ tolerance dose values should take into account these radiobiological differences, since it is currently not considered in dosimetry programs.
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35

Zavgorodni, S. "The impact of inter-fraction dose variations on biological equivalent dose (BED): the concept of equivalent constant dose." Physics in Medicine and Biology 49, no. 23 (November 20, 2004): 5333–45. http://dx.doi.org/10.1088/0031-9155/49/23/010.

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36

Ferre, M., A. Courdi, and J. Hannoun. "Dose Gradient Impact on Biological Equivalent Dose for Accelerated Partial Breast Irradiation using High Dose Rate Interstitial Brachytherapy." International Journal of Radiation Oncology*Biology*Physics 72, no. 1 (September 2008): S526. http://dx.doi.org/10.1016/j.ijrobp.2008.06.052.

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37

Oghiso, Yoichi, Satoshi Tanaka, Ignacia B. Tanaka III, and Fumiaki Sato. "Experimental studies on the biological effects of low-dose-rate and low-dose radiation." International Journal of Low Radiation 5, no. 1 (2008): 55. http://dx.doi.org/10.1504/ijlr.2008.018818.

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38

Amaral, Ademir. "Trends in biological dosimetry: an overview." Brazilian Archives of Biology and Technology 45, spe (September 2002): 119–24. http://dx.doi.org/10.1590/s1516-89132002000500017.

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Biological dosimetry (biodosimetry) is based on investigations of induced biological effects (biomarkers) in order to correlate them with radiation dose. Among the indicators employed in biodosimetry, scoring of chromosome aberrations is the most reliable method to quantify individual exposure to ionizing radiation. The technique, applied to circulating lymphocytes, has been developed into a routine procedure to evaluate the dose in the case of real or suspected accidental exposure. Considering the radiosensitivity of lymphocytes in vitro and in vivo as being the same, the dose effect relationship obtained after in vitro irradiation of blood has been widely used, with medico-legal value, for evaluating individual radiation exposure. This report presents an overview of strengths, limitations and perspectives on biodosimetry.
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39

Dai, Hong, Junchao Feng, Huahui Bian, Weibo Chen, Youyou Wang, Yulong Liu, and Wentao Hu. "Complete Technical Scheme for Automatic Biological Dose Estimation Platform." Dose-Response 16, no. 4 (October 1, 2018): 155932581879995. http://dx.doi.org/10.1177/1559325818799951.

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To establish a complete technical solution for the automatic radiation biological dose estimation platform for biological dose estimation and classification of the wounded in large-scale radiation accidents, the “dose–effect curve by dicentric chromosome (DIC) automatic analysis” was established and its accuracy was verified. The effects of analyzed cell number and the special treatment of the culture on dose estimation by DIC automatic analysis were studied. Besides, sample processing capabilities of the special equipments were tested. The fitted “dose–effect curve by DIC automatic analysis” was presented as follows: Y = (0.01806 ± 0.00032) D2 + (0.01279 ± 0.00084) D + (0.0004891 ± 0.0001358) ( R2 = 0.961). Three-gradient scanning method, culture refrigeration method, and interprofessional collaboration under extreme conditions were proposed to improve the detection speed, prolong the sample processing time window, and reduce the equipment investment. In addition, the optimized device allocation ratio for the automatic biological dose estimation laboratory was proposed to eliminate the efficiency bottleneck. The complete set of technical solutions for the high-throughput automatic biological dose estimation laboratory proposed in this study can meet the requirements of early classification and rapid biological dose assessment of the wounded during the large-scale nuclear radiation events, and it is worthy of further promotion.
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40

Sonin, Dmitry, Evgeniia Pochkaeva, Sergei Zhuravskii, Viktor Postnov, Dmitry Korolev, Lyubov Vasina, Daria Kostina, et al. "Biological Safety and Biodistribution of Chitosan Nanoparticles." Nanomaterials 10, no. 4 (April 23, 2020): 810. http://dx.doi.org/10.3390/nano10040810.

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The effect of unmodified chitosan nanoparticles with a size of ~100 nm and a weakly positive charge on blood coagulation, metabolic activity of cultured cardiomyocytes, general toxicity, biodistribution, and reactive changes in rat organs in response to their single intravenous administration at doses of 1, 2, and 4 mg/kg was studied. Chitosan nanoparticles (CNPs) have a small cytotoxic effect and have a weak antiplatelet and anticoagulant effect. Intravenous administration of CNPs does not cause significant hemodynamic changes, and 30 min after the CNPs administration, they mainly accumulate in the liver and lungs, without causing hemolysis and leukocytosis. The toxicity of chitosan nanoparticles was manifested in a dose-dependent short-term delay in weight gain with subsequent recovery, while in the 2-week observation period no signs of pain and distress were observed in rats. Granulomas found in the lungs and liver indicate slow biodegradation of chitosan nanoparticles. In general, the obtained results indicate a good tolerance of intravenous administration of an unmodified chitosan suspension in the studied dose range.
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41

Bhattacharjee, Atanu, Vijay M. Patil, Vanita Noronha, Amit Joshi, and Kumar Prabhash. "An Algorithm to Define the Optimum Biological Dose of Metronomic Chemotherapy." Biometrical Letters 55, no. 1 (June 1, 2018): 1–15. http://dx.doi.org/10.2478/bile-2018-0001.

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Summary The best effective dose of a chemotherapy is defined using the maximum tolerated dose (MTD) of toxicity. It is possible that the toxicity of a dose may increase when the dose-response curve is not monotonic. In the case of metronomic chemotherapy (MC) a 1/10th level of MC dose is considered as a targeted dose of therapy and is safer in terms of toxicity levels. The objective of this study is to develop an algorithm based on the dose response model of MC to evaluate the best effective dose based on the molecular target agent. The molecular target agent is defined as the optimal biological dose achieved by the best effective dose, as the lowest dose with the highest rate of safety and efficacy. The first proposed design is parametric and assumes a logistic dose-efficacy curve for dose determination, and the second design uses quadratic regression to identify the optimal biological dose. We conducted extensive simulation studies to investigate the operating characteristics of the proposed designs. Simulation studies provide a possible way to decide on the best effective dose of MC to be considered in further phases through the finding of the optimal biological dose. The proposed design is assumed, with the threshold value of optimum biological dose (OBD), to detect the best dose of MC. This consistent design with specific dose response models can be recommended for practice.
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42

Ali, Yasmine, Caterina Monini, Etienne Russeil, Jean Michel Létang, Etienne Testa, Lydia Maigne, and Michael Beuve. "Estimate of the Biological Dose in Hadrontherapy Using GATE." Cancers 14, no. 7 (March 25, 2022): 1667. http://dx.doi.org/10.3390/cancers14071667.

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For the evaluation of the biological effects, Monte Carlo toolkits were used to provide an RBE-weighted dose using databases of survival fraction coefficients predicted through biophysical models. Biophysics models, such as the mMKM and NanOx models, have previously been developed to estimate a biological dose. Using the mMKM model, we calculated the saturation corrected dose mean specific energy z1D* (Gy) and the dose at 10% D10 for human salivary gland (HSG) cells using Monte Carlo Track Structure codes LPCHEM and Geant4-DNA, and compared these with data from the literature for monoenergetic ions. These two models were used to create databases of survival fraction coefficients for several ion types (hydrogen, carbon, helium and oxygen) and for energies ranging from 0.1 to 400 MeV/n. We calculated α values as a function of LET with the mMKM and the NanOx models, and compared these with the literature. In order to estimate the biological dose for SOBPs, these databases were used with a Monte Carlo toolkit. We considered GATE, an open-source software based on the GEANT4 Monte Carlo toolkit. We implemented a tool, the BioDoseActor, in GATE, using the mMKM and NanOx databases of cell survival predictions as input, to estimate, at a voxel scale, biological outcomes when treating a patient. We modeled the HIBMC 320 MeV/u carbon-ion beam line. We then tested the BioDoseActor for the estimation of biological dose, the relative biological effectiveness (RBE) and the cell survival fraction for the irradiation of the HSG cell line. We then tested the implementation for the prediction of cell survival fraction, RBE and biological dose for the HIBMC 320 MeV/u carbon-ion beamline. For the cell survival fraction, we obtained satisfying results. Concerning the prediction of the biological dose, a 10% relative difference between mMKM and NanOx was reported.
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43

Ando, Koichi, Yukari Yoshida, Ryoichi Hirayama, Sachiko Koike, and Naruhiro Matsufuji. "Dose- and LET-dependent changes in mouse skin contracture up to a year after either single dose or fractionated doses of carbon ion or gamma rays." Journal of Radiation Research 63, no. 2 (January 10, 2022): 221–29. http://dx.doi.org/10.1093/jrr/rrab123.

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Abstract Time dependence of relative biological effectiveness (RBE) of carbon ions for skin damage was investigated to answer the question of whether the flat distribution of biological doses within a Spread-Out Bragg peak (SOBP) which is designed based on in vitro cell kill could also be flat for in vivo late responding tissue. Two spots of Indian ink intracutaneously injected into the legs of C3H mice were measured by calipers. An equieffective dose to produce 30% skin contraction was calculated from a dose–response curve and used to calculate the RBE of carbon ion beams. We discovered skin contraction progressed after irradiation and then reached a stable/slow progression phase. Equieffective doses decreased with time and the decrease was most prominent for gamma rays and least prominent for 100 keV/μm carbon ions. Survival parameter of alpha but not beta in the linear-quadratic model is closely related to the RBE of carbon ions. Biological doses within the SOBP increased with time but their distribution was still flat up to 1 year after irradiation. The outcomes of skin contraction studies suggest that (i) despite the higher RBE for skin contracture after carbon ions compared to gamma rays, gamma rays can result in a more severe late effect of skin contracture. This is due to the carbon effect saturating at a lower dose than gamma rays, and (ii) the biological dose distribution throughout the SOBP remains approximately the same even one year after exposure.
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44

BESSHO, Yuko. "The Biological Basis for Dose Limitation in the Skin." Japanese Journal of Health Physics 27, no. 4 (1992): 317–23. http://dx.doi.org/10.5453/jhps.27.317.

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45

Paris, F. "SP-0508 BIOLOGICAL CONSIDERATION OF HIGH DOSE PER FRACTION." Radiotherapy and Oncology 103 (May 2012): S204—S205. http://dx.doi.org/10.1016/s0167-8140(12)70847-2.

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46

Khadsiri, Patchareewan, Ekkasit Tharavichitkul, Suwit Saekho, and Nisa Chawapun. "Development of 3D biological effective dose distribution software program." Journal of Radiotherapy in Practice 16, no. 4 (September 9, 2016): 383–90. http://dx.doi.org/10.1017/s1460396916000339.

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AbstractPurposeTo develop a software program to convert physical dose distribution into biological effective dose (BED).MethodsThe MATLAB-based BED distribution software program was designed to import the radiotherapy treatment plan from the computer treatment planning system and to convert the physical dose distribution into the BED distribution. The BED calculation was based on the linear-quadratic-linear model (LQ-L model). Besides radiobiological parameters, other specific data could be fed in through the panel. The accuracy of the program was verified by comparing the BED distribution with manual calculation.ResultsThis software program was able to import the radiotherapy treatment plans and pull out pixel-wised physical dose for BED calculation, and display the isoBED lines on the computed tomographic (CT) image. The verification of BED dose distribution was performed in both phantom and clinical cases. It revealed that there were no differences between the program and manual BED calculations.ConclusionIt is feasible and practical to use this in-house BED distribution software program in clinical practices and research work. However, it should be used with caution as the validity of the program depends on the accuracy of the published biological parameters.
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47

Nakadaira, Hiroto, and Shinichi Nishi. "Effects of low-dose cadmium exposure on biological examinations." Science of The Total Environment 308, no. 1-3 (June 2003): 49–62. http://dx.doi.org/10.1016/s0048-9697(02)00646-0.

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48

Andersen, Melvin E., Raymond S. H. Yang, C. Tenley French, Laura S. Chubb, and James E. Dennison. "Molecular circuits, biological switches, and nonlinear dose-response relationships." Environmental Health Perspectives 110, suppl 6 (December 2002): 971–78. http://dx.doi.org/10.1289/ehp.02110s6971.

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49

Orton, Colin. "WE-B-304-00: Point/Counterpoint: Biological Dose Optimization." Medical Physics 42, no. 6Part37 (June 2015): 3663–64. http://dx.doi.org/10.1118/1.4925899.

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50

Balderson, Michael, Brandon Koger, and Charles Kirkby. "The relative biological effectiveness of out-of-field dose." Physics in Medicine and Biology 61, no. 1 (November 27, 2015): 114–30. http://dx.doi.org/10.1088/0031-9155/61/1/114.

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