Literatura académica sobre el tema "Radiological co-Exposures"
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Artículos de revistas sobre el tema "Radiological co-Exposures"
Gao, Fawei, Xucheng Yang, Chenggong Wang, Shilong Su, Jun Qi, Zhigang Li, Juehao Chen y Da Zhong. "Comparison of Clinical and Radiological Outcomes between Calibratable Patient-Specific Instrumentation and Conventional Operation for Medial Open-Wedge High Tibial Osteotomy: A Randomized Controlled Trial". BioMed Research International 2022 (23 de noviembre de 2022): 1–11. http://dx.doi.org/10.1155/2022/1378042.
Texto completoCross, F. T. "Radon Inhalation Studies in Animals". Radiation Protection Dosimetry 24, n.º 1-4 (1 de agosto de 1988): 463–66. http://dx.doi.org/10.1093/oxfordjournals.rpd.a080324.
Texto completoKorir, Geoffrey K., Jeska S. Wambani y Ian K. Korir. "Estimation of annual occupational effective doses from external ionising radiation at medical institutions in Kenya". South African Journal of Radiology 15, n.º 4 (7 de diciembre de 2011): 116. http://dx.doi.org/10.4102/sajr.v15i4.353.
Texto completoRaju, Uma, Glenice J. Gumin y Philip J. Tofilon. "NF?B activity and target gene expression in the rat brain after one and two exposures to ionizing radiation". Radiation Oncology Investigations 7, n.º 3 (1999): 145–52. http://dx.doi.org/10.1002/(sici)1520-6823(1999)7:3<145::aid-roi2>3.0.co;2-r.
Texto completoC. Kingsley, Azionu, Avwiri O. Gregory y Ononugbo, P. Chinyere. "Occupational Hazards from BIR in Selected Crude Oil Production Pipes Storage Locations in Niger Delta Region of Nigeria". Current Journal of Applied Science and Technology, 22 de agosto de 2019, 1–12. http://dx.doi.org/10.9734/cjast/2019/v37i230277.
Texto completoTesis sobre el tema "Radiological co-Exposures"
Fendler, Julie. "Approches hiérarchiques bayésiennes pour l'estimation d'un risque sanitaire induit par l'exposome professionnel (co-expositions radiologiques à faibles doses sujettes à des erreurs de mesure) : Application à la cohorte française des mineurs d'uranium". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASR019.
Texto completoThe population of uranium miners is a reference population for studying the health effects of chronic exposure to various sources of ionising radiation (IR): radon, gamma rays and uranium dust. However, two statistical problems arise in these studies: 1) the miners' exposures measures are error-prone; 2) the exposures to the three sources of IR are highly correlated. In radiation epidemiology, measurement errors in exposures are often ignored and health risks are estimated source by source, ignoring the synergistic or antagonistic effects of simultaneous exposures. The aim of this work, which is divided into two parts, is to promote the use of hierarchical Bayesian models to address the two problems raised above. All the statistical methods proposed in this work are applied to estimate a health risk from survival data in the French cohort of uranium miners.A model is proposed for estimating a health risk while considering complex measurement errors on radon exposures. These measurement errors depend on the miner's workplace and its work habits which change little over time. These errors are therefore spatially and temporally correlated. They are also heteroscedastic: their variances decrease over time as methods for assessing radon exposure improve. The proposed models are used account for measurement errors in radon exposure when estimating the risk of death by lung cancer, kidney cancer, brain and central nervous system cancer and leukaemia. The correction of the measurement errors and the estimation of the health risk are carried out simultaneously so that the estimation of the risk coefficient account for the uncertainty in the exposures. An MCMC algorithm was implemented in Python 3.8 to infer the model within a Bayesian framework. A simulations study is then carried out to estimate the impact of model misspecification on risk estimates.The three exposures to IRs are considered simultaneously when assessing a health risk by using profil regressions mixture (PRM) models. These models are used to create groups of miners with similar exposure profiles and similar health risks. As before, the inference of groups and the estimation of health risk are carried out simultaneously so that the uncertainty in the grouping is accounted in the estimation of risk. The number of groups in the model is infinite, but only a finite number of groups are non-empty. This assumption, which implies that the number of model parameters is infinite, introduces a difficulty in inferring the model. In addition, the output of the inference algorithm cannot be interpreted directly: post-processing must be carried out in order to form the different groups of individuals. While the choice of post-processing used has an impact on the grouping of individuals, there are only few guidelines on this in the scientific literature. This work proposes a Python implementation of a time-efficient MCMC algorithm for inferring PRM models. This algorithm is used to estimate the risk of death from lung cancer in the French cohort of uranium miners associated with simultaneous exposure to radon, gamma rays and uranium dust. Finally, a simulation study is carried out to compare different post-treatment procedures and provide guidelines on their use
Actas de conferencias sobre el tema "Radiological co-Exposures"
Wilkins, R. C. "Case study on occupational exposures to radiation with possible co-exposure to heavy metals". En The Sixth International Symposium on the System of Radiological Protection. SAGE Publications Ltd., 2023. http://dx.doi.org/10.54320/ppqs8013.
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