{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:09:23Z","timestamp":1789711763509,"version":"4.0.1"},"reference-count":31,"publisher":"IOP Publishing","issue":"3","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":17,"URL":"https:\/\/publishingsupport.iopscience.iop.org\/iop-standard\/v1"},{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"tdm","delay-in-days":17,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["J. Radiol. Prot."],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"Abstract<\/jats:title>\n \n Uncertainty surrounding the biological effects of chronic low-dose radiation remains an important challenge in radiation risk assessment, particularly for long-duration space missions. Although adaptive response has been proposed as a potential modifier of radiation-induced cancer risk, its impact on mission-level risk predictions has not been quantitatively evaluated. We developed an exploratory Monte Carlo framework that extends a conventional linear no-threshold-based cancer risk model by incorporating an adaptive response factor (RAF) parameterised by a maximum protection fraction (\n \n \n \n <\/jats:tex-math>\n \n \n \n \n RAF<\/mml:mi>\n <\/mml:mrow>\n \n max<\/mml:mo>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), a half-saturation priming dose (\n \n \n \n <\/jats:tex-math>\n \n \n \n d<\/mml:mi>\n \n 50<\/mml:mn>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ), and a persistence time constant (\n \n \n \n <\/jats:tex-math>\n \n \n \u03c4<\/mml:mi>\n <\/mml:mrow>\n <\/mml:math>\n <\/jats:inline-formula>\n ). Organ-specific equivalent doses and model parameters were sampled for representative International Space Station (ISS), lunar, and Mars mission scenarios over 100\u2009000 iterations per organ\u2013mission pair. Under the implemented single-priming with exponential decay formulation, adaptive response produced only modest changes in projected cancer risk. Median reductions were negligible for the 1000\u2009d Mars mission and remained below 1% for ISS scenarios, while larger reductions occurred only in a small fraction of simulations associated with favourable adaptive-response parameters. Within the assumptions of the current model, adaptive response is unlikely to substantially alter mission-level cancer risk estimates. Nevertheless, the proposed framework provides a transparent method for evaluating alternative biological hypotheses and quantifying their potential influence on radiation risk assessment as new experimental evidence becomes available.\n <\/jats:p>","DOI":"10.1088\/1361-6498\/ae9ed3","type":"journal-article","created":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T22:48:33Z","timestamp":1787784513000},"page":"031534","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Modelling radiation adaptive response in space radiation risk assessment using Monte Carlo simulation"],"prefix":"10.1088","volume":"46","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6790-4245","authenticated-orcid":true,"given":"Omid","family":"Zare","sequence":"first","affiliation":[{"name":"Shiraz University of Medical Sciences"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"266","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"jrpae9ed3bib1","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0168-583X(01)00748-0","type":"journal-article","article-title":"Space radiation dosimetry in low-earth orbit and beyond","volume":"184","author":"Benton","year":"2001","journal-title":"Nucl. 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Published on behalf of SRP by IOP Publishing Limited. All rights, including for text and data mining, AI training, and similar technologies, are reserved.","name":"copyright_information","label":"Copyright Information"},{"value":"2026-06-29","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-08-26","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2026-09-18","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}