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Thematische Bibliographien / Dose-Response modeling

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Auswahl der wissenschaftlichen Literatur zum Thema „Dose-Response modeling“

Autor: Grafiati

Veröffentlicht am 15. Februar 2025

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Zeitschriftenartikel zum Thema "Dose-Response modeling"

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May, Susanne, und Carol Bigelow. „Modeling Nonlinear Dose-Response Relationships in Epidemiologic Studies: Statistical Approaches and Practical Challenges“. Dose-Response 3, Nr. 4 (01.10.2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.004.

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Non-linear dose response relationships pose statistical challenges for their discovery. Even when an initial linear approximation is followed by other approaches, the results may be misleading and, possibly, preclude altogether the discovery of the nonlinear relationship under investigation. We review a variety of straightforward statistical approaches for detecting nonlinear relationships and discuss several factors that hinder their detection. Our specific context is that of epidemiologic studies of exposure-outcome associations and we focus on threshold and J-effect dose response relationships. The examples presented reveal that no single approach is universally appropriate; rather, these (and possibly other) nonlinearities require for their discovery a variety of both graphical and numeric techniques.

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Hunt, Daniel L., Shesh N. Rai und Chin-Shang Li. „Summary of Dose-Response Modeling for Developmental Toxicity Studies“. Dose-Response 6, Nr. 4 (01.10.2008): dose—response.0. http://dx.doi.org/10.2203/dose-response.08-007.hunt.

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Developmental toxicity studies are an important area in the field of toxicology. Endpoints measured on fetuses include weight and indicators of death and malformation. Binary indicator measures are typically summed over the litter and a discrete distribution is assumed to model the number of adversely affected fetuses. Additionally, there is noticeable variation in the litter responses within dose groups that should be taken into account when modeling. Finally, the dose-response pattern in these studies exhibits a threshold effect. The threshold dose-response model is the default model for non-carcinogenic risk assessment, according to the USEPA, and is encouraged by the agency for the use in the risk assessment process. Two statistical models are proposed to estimate dose-response pattern of data from the developmental toxicity study: the threshold model and the spline model. The models were applied to two data sets. The advantages and disadvantages of these models, potential other models, and future research possibilities will be summarized.

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COLEMAN, MARGARET, und HARRY MARKS. „Topics in Dose-Response Modeling“. Journal of Food Protection 61, Nr. 11 (01.11.1998): 1550–59. http://dx.doi.org/10.4315/0362-028x-61.11.1550.

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Great uncertainty exists in conducting dose-response assessment for microbial pathogens. The data to support quantitative modeling of dose-response relationships are meager. Our philosophy in developing methodology to conduct microbial risk assessments has been to rely on data analysis and formal inferencing from the available data in constructing dose-response and exposure models. The probability of illness is a complex function of factors associated with the disease triangle: the host, the pathogen, and the environment including the food vehicle and indigenous microbial competitors. The epidemiological triangle and interactions between the components of the triangle are used to illustrate key issues in dose-response modeling that impact the estimation of risk and attendant uncertainty. Distinguishing between uncertainty (what is unknown) and variability (heterogeneity) is crucial in risk assessment. Uncertainty includes components that are associated with (i) parameter estimation for a given assumed model, and (ii) the unknown “true” model form among many plausible alterative such as the exponential, Beta-Poisson, profit, logistic, and Gompertz. Uncertainty may be grossly understated if plausible alterative models are not tested in the analysis. Examples are presented of the impact of variability and uncertainty on species, strain, or serotype of microbial pathogens; variability in human response to administered doses of pathogens; and effects of threshold and nonthreshold models. Some discussion of the usefulness and limitations of epidemiological data is presented. Criteria for development of surrogate dose-response models are proposed for pathogens for which human data are lacking. Alterative dose-response models which consider biological plausibility are presented for predicting the probability of illness.

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Zhao, Yuchao, und Paolo F. Ricci. „Modeling dose-Response at Low dose: A Systems Biology Approach for Ionization Radiation“. Dose-Response 8, Nr. 4 (19.03.2010): dose—response.0. http://dx.doi.org/10.2203/dose-response.09-054.zhao.

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Slob, W. „Dose-Response Modeling of Continuous Endpoints“. Toxicological Sciences 66, Nr. 2 (01.04.2002): 298–312. http://dx.doi.org/10.1093/toxsci/66.2.298.

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Feinendegen, Ludwig E., Myron Pollycove und Ronald D. Neumann. „Low-Dose Cancer Risk Modeling Must Recognize Up-Regulation of Protection“. Dose-Response 8, Nr. 2 (10.12.2009): dose—response.0. http://dx.doi.org/10.2203/dose-response.09-035.feinendegen.

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Cox, Louis Anthony (Tony). „A Model of Cytotoxic Dose-Response Nonlinearities Arising from Adaptive Cell Inventory Management in Tissues“. Dose-Response 3, Nr. 4 (01.10.2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.003.04.005.

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Why do low-level exposures to environmental toxins often elicit over-compensating responses that reduce risk to an organism? Conversely, if these responses improve health, why wait for an environmental challenge to trigger them? This paper presents a mathematical modeling framework that addresses both questions using the principle that evolution favors tissues that hedge their bets against uncertain environmental challenges. We consider a tissue composed of differentiated cells performing essential functions (e.g., lung tissue, bone marrow, etc.). The tissue seeks to maintain adequate supplies of these cells, but many of them may occasionally be killed relatively quickly by cytotoxic challenges. The tissue can “order replacements” (e.g., via cytokine network signaling) from a deeper compartment of proliferative stem cells, but there is a delivery lag because these cells must undergo maturation, amplification via successive divisions, and terminal differentiation before they can replace the killed functional cells. Therefore, a “rational” tissue maintains an inventory of relatively mature cells (e.g., the bone marrow reserve for blood cells) for quick release when needed. This reservoir is replenished by stimulating proliferation in the stem cell compartment. Normally, stem cells have a very low risk of unrepaired carcinogenic (or other) damage, due to extensive checking and repair. But when production is rushed to meet extreme demands, error rates increase. We use a mathematical model of cell inventory management to show that decision rules that effectively manage the inventory of mature cells to maintain tissue function across a wide range of unpredictable cytotoxic challenges imply that increases in average levels of cytotoxic challenges can increase average inventory levels and reduce the average error rate in stem cell production. Thus, hormesis and related nonlinearities can emerge as a natural result of cell-inventory risk management by tissues.

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Li, Zhenhong, Bin Sun, Rebecca A. Clewell, Yeyejide Adeleye, Melvin E. Andersen und Qiang Zhang. „Dose-Response Modeling of Etoposide-Induced DNA Damage Response“. Toxicological Sciences 137, Nr. 2 (16.11.2013): 371–84. http://dx.doi.org/10.1093/toxsci/kft259.

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Cox, Louis Anthony (Tony). „Universality of J-Shaped and U-Shaped Dose-Response Relations as Emergent Properties of Stochastic Transition Systems“. Dose-Response 3, Nr. 3 (01.05.2005): dose—response.0. http://dx.doi.org/10.2203/dose-response.0003.03.006.

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Dose-response data for many chemical carcinogens exhibit multiple apparent concentration thresholds. A relatively small increase in exposure concentration near such a threshold disproportionately increases incidence of a specific tumor type. Yet, many common mathematical models of carcinogenesis do not predict such threshold-like behavior when model parameters (e.g., describing cell transition rates) increase smoothly with dose, as often seems biologically plausible. For example, commonly used forms of both the traditional Armitage-Doll and multistage (MS) models of carcinogenesis and the Moolgavkar-Venzon-Knudson (MVK) two-stage stochastic model of carcinogenesis typically yield smooth dose-response curves without sudden jumps or thresholds when exposure is assumed to increase cell transition rates in proportion to exposure concentration. This paper introduces a general mathematical modeling framework that includes the MVK and MS model families as special cases, but that shows how abrupt transitions in cancer hazard rates, considered as functions of exposure concentrations and durations, can emerge naturally in large cell populations even when the rates of cell-level events increase smoothly (e.g., proportionally) with concentration. In this framework, stochastic transitions of stem cells among successive events represent exposure-related damage. Cell proliferation, cell killing and apoptosis can occur at different stages. Key components include: An effective number of stem cells undergoing active cycling and hence vulnerable to stochastic transitions representing somatically heritable transformations. (These need not occur in any special linear order, as in the MS model.) A random time until the first malignant stem cell is formed. This is the first order-statistic, T = min{T1, T2, …, Tn} of n random variables, interpreted as the random times at which each of n initial stem cells or their progeny first become malignant. A random time for a normal stem cell to complete a full set of transformations converting it to a malignant one. This is interpreted very generally as the first passage time through a network of stochastic transitions, possibly with very many possible paths and unknown topology. In this very general family of models, threshold-like (J-shaped or multi-threshold) dose-response nonlinearities naturally emerge even without cytotoxicity, as consequences of stochastic phase transition laws for traversals of random transition networks. With cytotoxicity present, U-shaped as well as J-shaped dose-response curves can emerge. These results are universal, i.e., independent of specific biological details represented by the stochastic transition networks.

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Herbert, Donald E., und Colin G. Orton. „Dose/time/response modeling in radiation therapy“. International Journal of Radiation Oncology*Biology*Physics 19 (Januar 1990): 114–15. http://dx.doi.org/10.1016/0360-3016(90)90636-x.

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Dissertationen zum Thema "Dose-Response modeling"

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Leininger, Thomas J. „An Adaptive Bayesian Approach to Dose-Response Modeling“. Diss., CLICK HERE for online access, 2009. http://contentdm.lib.byu.edu/ETD/image/etd3325.pdf.

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Ås, Joel. „Active dose selection and dose-response modeling for quantitative high-throughput screening (qHTS)“. Thesis, Uppsala universitet, Cancerfarmakologi och beräkningsmedicin, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-300682.

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This master thesis studies the potential benefit of iterative selection of the concentrations evaluated when building mathematical dose-response curves (and response surfaces when there are two drugs) using experimental measurements. The reference alternative is to use a standard two-fold dilution series or ten-fold dilution series measured in replicates. The standard 4-parameter Hill dose-response model is used as a reference and for simulations. Models to screen for synergy between two different substances are also developed in this thesis.

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Adamus-Górka, Magdalena. „Improved dose response modeling for normal tissue damage and therapy optimization“. Doctoral thesis, Stockholm University, Medical Radiation Physics (together with KI), 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7433.

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The present thesis is focused on the development and application of dose response models for radiation therapy. Radiobiological models of tissue response to radiation are an integral part of the radiotherapeutic process and a powerful tool to optimize tumor control and minimize damage to healthy tissues for use in clinical trials. Ideally, the models could work as a historical control arm of a clinical trial eliminating the need to randomize patents to suboptimal therapies. In the thesis overview part, some of the basic properties of the dose response relation are reviewed and the most common radiobiological dose-response models are compared with regard to their ability to describe experimental dose response data for rat spinal cord using the maximum likelihood method. For vascular damage the relative seriality model was clearly superior to the other models, whereas for white matter necrosis all models were quite good except possibly the inverse tumor and critical element models. The radiation sensitivity, seriality and steepness of the dose-response relation of the spinal cord is found to vary considerably along its length. The cervical region is more radiation sensitive, more parallel, expressing much steeper dose-response relation and more volume dependent probability of inducing radiation myelitis than the thoracic part. The higher number of functional subunits (FSUs) consistent with a higher amount of white matter close to the brain may be responsible for these phenomena. With strongly heterogeneous dose delivery and due to the random location of FSUs, the effective size of the FSU and the mean dose deposited in it are of key importance and the radiation sensitivity distribution of the FSU may be an even better descriptor for the response of the organ. An individual optimization of a radiation treatment has the potential to increase the therapeutic window and improve cure for a subgroup of patients.

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Adamus-Górka, Magdalena. „Improved dose response modeling for normal tissue damage and therapy optimization /“. Stockholm ; Solna : Medical Radiation Physics, Stockholm University and Karolinska institutet, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7433.

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Eklund, Karin. „Modeling Silicon Diode Dose Response in Radiotherapy Fields using Fluence Pencil Kernels“. Doctoral thesis, Uppsala universitet, Avdelningen för sjukhusfysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-120581.

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In radiotherapy, cancer is treated with ionizing radiation, most commonly bremsstrahlung photons from electrons of several MeV. Secondary electrons produced in photon-interactions results in dose deposition. The treatment response is low for low doses, raises sharply for normal treatment doses and saturates at higher doses. This response pattern applies to both eradication of tumors and to complications in healthy tissues. Well controlled treatments require accurate dosimetry since the uncertainty in delivered dose will be magnified 1 to 5 times in treatment response variations. Techniques that superpose many small radiation fields to concentrate the dose to a localized target are becoming increasingly used. A detector with high spatial resolution suitable for such fields is a silicon diode. To maintain the current accuracy of the dosimetric calibration of 1.5%, diode measurements relative to this calibration should preferably be possible at 0.5% accuracy level. The main limitation of silicon diodes is their over-response to low-energy photons. This problem has been adressed with the insertion of a high atomic number filter in diodes. For modeling diode detector response one must quantify the spectral variations in the irradiated medium resulting from variations of the beam parameters. This requires understanding of the particle transport and can be achieved by Monte Carlo simulations. However, the small dimensions of the detector geometry compared to surrounding medium makes a direct application of Monte Carlo impractical due to the large amount of CPU time necessary to reach statistically satisfactory results. In this work a fast method for spectra calculations is used, based on superposition of mono-energetic fluence pencil kernels. Building on this base a general model for silicon response functions in photon fields is developed. The incident photons are bipartitioned into a low and a high energy component. The high energy part is treated with the Spencer-Attic cavity theory while the low energy part and scattered photons are treated with large cavity theory. The deviations from electron equilibrium are investigated and handled with correction factors. The result is used to correct unshielded diode measurements, with an overall uncertainty less than 0.5%, except for very small fields where the precision is around 1-2%, thus eliminating the need for less predictable shielded diodes for measurements in photon fields.

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Sand, Salomon. „Dose-response modeling : evaluation, application, and development of procedures for benchmark dose analysis in health risk assessment of chemical substances /“. Stockholm, 2005. http://diss.kib.ki.se/2005/91-7140-420-1/.

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Wessel, Michael Raymond. „Dose time response modeling of neurobehavioral screening data application of physiologically relevant parameters to describe dose dependent time of peak effects /“. [Tampa, Fla.] : University of South Florida, 2005. http://purl.fcla.edu/fcla/etd/SFE0001273.

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Toyinbo, Peter A. „On Effective and Efficient Experimental Designs for Neurobehavioral Screening Tests: The Choice of a Testing Time for Estimating the Time of Peak Effects“. [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000393.

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Wessel, Michael Raymond. „Dose Time Response Modeling of Neurobehavioral Screening Data: Application of Physiologically Relevant Parameters to Allow for Dose Dependent Time of Peak Effects“. Scholar Commons, 2005. https://scholarcommons.usf.edu/etd/911.

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In collaboration with the United States Environmental Protection Agency (USEPA), the University of South Florida Health Risk Methodology Group has developed dose-time-response models to characterize neurobehavioral response to chemical exposure. The application of dose-time-response models to neurobehavioral screening tests on laboratory animals allows for benchmark dose estimation to establish exposure limits in environmental risk assessment. This thesis has advanced dose-time-response modeling by generalizing a published toxico diffusion model to allow for dose dependent time of peak effects. To accomplish this, a biphasic model was developed which adopted the effect compartment model paradigm used in pharmacokinetics/pharmacodynamics to estimate a distributional rate constant to account for dose related variation in the time of peak effect. The biphasic model was able to describe dose-dependent time of peak effects as observed in the data on acute exposure to parathion and adequately predicted the observed response. However, the experimental design appeared insufficient in statistical power to confirm statistical significance for each parameter of interest. Motivated by the question of what design requirement might be necessary to validate the biphasic model, Monte Carlo simulation was adopted. Simulations were performed to assess the efficacy and efficiency of various experimental designs for detecting and evaluating some critical characteristics of the biphasic model, including the TOPE. The results of simulation suggest that the location of measurement times around the TOPE have important implications for assessing the statistical significance of the parameter that describes dose-dependent TOPE and that the mean squared error of the parameter estimator was improved most when testing times were chosen to bracket the TOPE. While dose dependent time of peak effects has underlying physiological mechanisms such as synergistic or capacity limited kinetics, the biphasic model estimates these physiological properties through a mathematical function which may be physiologically relevant but does not necessarily define physiological mechanisms underlying the response. However, if verified through further testing, the biphasic model may contribute to the USEPA’s aim of developing physiologically relevant dose-response models for assessing risk of neurotoxicity with repeated measurements of response.

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Davidson, Sarah E. „Alternative Approach to Dose-Response Modeling of Toxicogenomic Data with an Application in Risk Assessment of Engineered Nanomaterials“. University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666554729205.

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Bücher zum Thema "Dose-Response modeling"

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Cooke, Roger M., Hrsg. Uncertainty Modeling in Dose Response. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. http://dx.doi.org/10.1002/9780470481400.

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Cooke, Roger M. Uncertainty modeling in dose response: Bench testing environmental toxicity. Hoboken: Wiley, 2009.

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L, Sielken Robert, Hrsg. Quantitative cancer modeling and risk assessment. Englewood Cliffs, N.J: Prentice Hall, 1993.

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Lin, Dan, Ziv Shkedy, Daniel Yekutieli, Dhammika Amaratunga und Luc Bijnens, Hrsg. Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2.

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Hanford Life Sciences Symposium (26th 1987 Richland, Wash.). Modeling for scaling to man: Biology, dosimetry, and response, [proceedings of the] 26th Hanford Life Sciences Symposium. Herausgegeben von Mahaffey Judith A. New York: Pergamon Press, 1989.

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Cooke, Roger M. Uncertainty Modeling in Dose Response. Wiley & Sons, Incorporated, John, 2009.

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Cooke, Roger M. Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity. Wiley & Sons, Incorporated, John, 2009.

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Cooke, Roger M. Uncertainty Modeling in Dose Response: Bench Testing Environmental Toxicity. Wiley & Sons, Incorporated, John, 2008.

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Holland, Charles D. Quantitive cancer modeling and risk assessment. PTR Prentice Hall, 1993.

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Exposure-response modeling: Methods and practical implementation. Boca Raton: CRC Press, Taylor & Francis, 2016.

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Buchteile zum Thema "Dose-Response modeling"

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Dinse, Gregg E., und David M. Umbach. „Dose-Response Modeling“. In Chemical Mixtures and Combined Chemical and Nonchemical Stressors, 205–34. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-56234-6_8.

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Edler, Lutz, Annette Kopp-schneider und Harald Heinzl. „Dose-Response Modeling“. In Recent Advances in Quantitative Methods in Cancer and Human Health Risk Assessment, 211–37. Chichester, UK: John Wiley & Sons, Ltd, 2006. http://dx.doi.org/10.1002/0470857706.ch13.

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Crawford-Brown, Douglas J. „Modeling Dose-Response Relationships“. In Theoretical and Mathematical Foundations of Human Health Risk Analysis, 151–75. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6143-9_6.

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Straetemans, Roel. „Nonlinear Modeling of Dose-Response Data“. In Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R, 43–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2_4.

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Haas, Chuck. „Dose-Response Modeling for Microbial Risk“. In Food Safety Handbook, 47–57. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/047172159x.ch4.

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Bijnens, Luc, Hinrich W. H. Göhlmann, Dan Lin, Willem Talloen, Tim Perrera, Ilse Van Den Wyngaert, Filip De Ridder, An De Bondt und Pieter Peeters. „Functional Genomic Dose-Response Experiments“. In Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R, 69–80. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-24007-2_5.

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Charnley, Gail. „Cancer Dose-Response Modeling and Methylene Chloride“. In Oncogene and Transgenics Correlates of Cancer Risk Assessments, 231–40. Boston, MA: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3056-5_17.

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DeVito, Michael J., Amy Kim, Nigel J. Walker, Fred Parham und Christopher Portier. „Dose-Response Modeling for 2,3,7,8-Tetrachlorodibenzo-p-Dioxin“. In Dioxins and Health, 247–98. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471722014.ch7.

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Thakur, Ajit K. „Modeling and Risk Assessment of Carcinogenic Dose-Response“. In Pharmacokinetics, 227–44. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4684-5463-5_11.

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Sielken, R. L. „Quantitative Cancer Dose-Response Modeling for All Ages“. In Risk Analysis, 315–35. Boston, MA: Springer US, 1991. http://dx.doi.org/10.1007/978-1-4899-0730-1_32.

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Konferenzberichte zum Thema "Dose-Response modeling"

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Ewing, Lucas, Sebastian Ahn, Oliver Jonas und Nobuhiko Hata. „Pixelwise tissue segmentation for precise local in-vivo dose response assessment in patient-derived xenografts“. In Image-Guided Procedures, Robotic Interventions, and Modeling, herausgegeben von Baowei Fei und Cristian A. Linte. SPIE, 2019. http://dx.doi.org/10.1117/12.2513080.

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Witulski, A. F., M. B. Smith, N. Mahadevan, A. L. Sternberg, C. Barnes, D. Sheldon, R. D. Schrimpf, G. Karsai und M. W. McCurdy. „Bayesian Modeling of COTS Power MOSFET Ionizing Dose Impact on Circuit Response“. In 2017 17th European Conference on Radiation and Its Effects on Components and Systems (RADECS). IEEE, 2017. http://dx.doi.org/10.1109/radecs.2017.8696104.

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Bighamian, Ramin, Sadaf Soleymani, Andrew T. Reisner, Istvan Seri und Jin-Oh Hahn. „Modeling and System Identification of Hemodynamic Responses to Vasopressor-Inotropes“. In ASME 2013 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/dscc2013-3726.

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In an effort to establish an initial step towards the ultimate goal of developing an analytic tool to optimize the vasopressor-inotrope therapy through individualized dose-response relationships, we propose a phenomenological model intended to reproduce the hemodynamic response to vasopressor-inotropes. The proposed model consists of a cardiovascular model relating blood pressure to cardinal cardiovascular parameters (stroke volume and total peripheral resistance) and the phenomenological relationships between the cardinal cardiovascular parameters and the vasopressor-inotrope dose, in such a way that the model can be adapted to individual patient solely based upon blood pressure and heart rate responses to medication dosing. In this paper, the preliminary validity of the proposed model is shown using the experimental epinephrine dose versus blood pressure and heart rate response data collected from five newborn piglets. Its performance and potential usefulness are discussed. It is anticipated that, potentially, the proposed phenomenological model may offer a meaningful first step towards the automated control of vasopressor-inotrope therapy.

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Poorbahrami, K., K. J. Carey, A. Hahn, M. Schiebler, S. B. Fain, L. C. Denlinger und J. M. Oakes. „Modeling Bronchodilator Dose Response in the Central Airways of Asthmatic Lungs Using Computational Fluid Dynamics“. In American Thoracic Society 2020 International Conference, May 15-20, 2020 - Philadelphia, PA. American Thoracic Society, 2020. http://dx.doi.org/10.1164/ajrccm-conference.2020.201.1_meetingabstracts.a5692.

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Affan, Affan, Jacek M. Zurada, Michael E. Brier und Tamer Inanc. „Adaptive Individualized Drug-Dose Response Modeling from a Limited Clinical Data: Case of Warfarin Management“. In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630158.

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6

Schulmeister, Karl, Gerald Sonneck, Herbert Hoedlmoser, Frank Rattay, John Mellerio und David H. Sliney. „Modeling of uncertainty associated with dose-response curves as applied for probabilistic risk assessment in laser safety“. In BiOS 2001 The International Symposium on Biomedical Optics, herausgegeben von Bruce E. Stuck und Michael Belkin. SPIE, 2001. http://dx.doi.org/10.1117/12.426713.

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7

Williams, Katherine S., Ardith W. El-Kareh und Timothy W. Secomb. „Abstract 439: Mathematical modeling of cellular dose-response for radiation and radiation-drug combinations including cell cycle effects.“ In Proceedings: AACR 104th Annual Meeting 2013; Apr 6-10, 2013; Washington, DC. American Association for Cancer Research, 2013. http://dx.doi.org/10.1158/1538-7445.am2013-439.

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8

Zhuang, Shuhan, Sheng Fang und Xinwen Dong. „Local-Scale Atmospheric Dispersion Modelling of Radionuclides Following the Fukushima Daiichi Nuclear Accident Using SWIFT-RIMPUFF“. In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-90748.

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Abstract Air dispersion modeling is an important tool for emergency response following a nuclear accident, such as the Fukushima accident. Current researches mainly focus on global- and regional-scale modeling with wind field data derived from different methodological models and observations. However, the capability of the local-scale atmospheric dispersion model hasn’t been discussed in detail for the Fukushima accident. In this paper, the local-scale modeling of radionuclides following the Fukushima accident was investigated with the combination of the wind diagnosed model SWIFT and the radionuclide transport model RIMPUFF. The coarse input wind field of SWIFT is prepared with WRF using the European Centre for Medium-Range Weather Forecasts (ECMWF) Meteorological data. The SWIFT-diagnosed wind field was used to drive RIMPUFF for calculating the dispersion of radionuclides and gamma dose rates around the Fukushima Daiichi Nuclear Power Plant (FDNPP) site. The diagnostic wind fields were validated to the on-site meteorological observations, whereas the dispersion and gamma dose rates were validated the onsite gamma dose rate monitoring data and the observations of suspended particulate m? near the FDNPP. The plume patterns were also analyzed to help understand the transport behaviour of the radionuclides. The validation demonstrates that, with the 1km-resolved ECMWF meteorological data, SWIFT fairly reproduces the wind field. The speed is slightly overestimated, with a Normalized Mean Squared Error (NMSE) below 6. The wind direction is well simulated at some specific moments, which is critical for reproducing some peaks of the dose rates. However, RIMPUFF underestimates the dose rates around the FDNPP, partly because of the overestimated wind speed. The concentration simulations better agree with observations in the Naraha station than Futaba station, with the Fractional Bias (FB) under 0.3 and NMSE under 6 at the Naraha station.

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Dong, Xinwen, Sheng Fang und Shuhan Zhuang. „Interpolation Influence on the Fast Fourier Transform Based Calculation of Three-Dimensional Dose Rate Field“. In 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-89244.

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Abstract The calculation of three-dimensional dose rate fields plays a key role in radiation dose rate estimation and the service for the nuclear emergency. The recent fast calculation method based on the Fast Fourier Transform (FFT) method can greatly speed up the calculation without losing accuracy, which is promising for operational usage in nuclear emergency response systems. But it can only be used for a uniform grid. Unfortunately, most atmospheric dispersion models use a non-uniform grid, which prevents the direct application of FFT-based calculation. Therefore, interpolation is required beforehand to use the Fourier transform, which may introduce errors and affect computing efficiency. In this paper, an atmospheric dispersion modeling case of a typical nuclear power plant (NPP) is used to investigate the efficiency of different interpolation methods, which are based on a non-uniform grid. These methods are linear interpolation and nearest-neighbor interpolation. The sensitive analysis of grid resolution is investigated in the slices of x, y, and z at typical positions, which confirms the smooth-out and speed-up effects in rough grids. A grid size over 10 m at any slice commonly causes losses of change details of dose rate fields. Given the same resolution of 50 m × 50 m × 50 m, the nearest neighbor performs a 717 times calculation faster than the linear method, which preserves more change details of dose rate fields as well. For complex calculation tasks, e.,g., non-uniform NPP buildings, the nearest neighbor interpolation method is recommended with a resolution of 10 m × 10 m × 10 m to make a good balance between accuracy and speed.

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Patel, Krishna, Michael Stevens, Suyash Adhikari, Greg Book, Muhammad Mubeen und Godfrey Pearlson. „Acute cannabis-related alterations in an fMRI time estimation task“. In 2022 Annual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.02.000.26.

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Introduction: Cannabis is widely popular recreational drug of choice in the US. The drug is known to alter the subjective experience of time. However, its effects on time estimation at a brain level are still largely unexplored. Our goal was to investigate acute effects of cannabis on an fMRI time estimation task by evaluating brain activation differences between cannabis and placebo conditions. We hypothesized that participants’ time estimation accuracy and corresponding BOLD response would be altered during the cannabis condition in a dose-related manner, compared to placebo. Methods: In this placebo-controlled, double-blind randomized trial, a total of N=44 participants had 3 dose visits, at each of which they received either high-dose cannabis (0.5 gm of ~12.5% THC flower), low dose cannabis (0.5 gm of ~5.7% flower) or 0.5 gm placebo, using paced inhalation from a volcano via vaporizer. Drug material was supplied by NIDA/RTI. For the current study we analyzed fMRI data from the first of placebo and high dose fMRI sessions throughout each dosing day in which participants performed a time estimation task. Participants were asked to respond with a mouse click as to which box of two boxes displayed for different intervals was displayed on the screen longer. Both sub-second and supra-second temporal intervals were tested, with a range of easy to hard discriminations. We used the Human Connectome Project processing pipeline to prepare fMRI data for GLM modeling of activation using the FSL FEAT toolbox. This model estimated the unique effect sub-second (short) and supra-second (long) interval discrimination, their average effect, and their difference. From these contrasts, the mean activation amplitudes within 387 brain parcels from the Human Connectome cortical atlas were extracted. Robust statistics in R software estimated a paired t test equivalent using the bootdpci function to assess the difference between placebo and the high dose drug conditions for each contrast. Results: Only premotor cortex survived False Discovery Rate corrections for searching all 387 parcels across the entire brain for the average of short and long temporal estimation conditions. Numerous other brain regions differed between placebo and high doses at p<.05 uncorrected for various task contrasts: Short duration stimuli: Premotor cortex, posterior cingulate cortex, medial temporal cortex, visual area, somatosensory cortex, anterior cingulate and medial prefrontal cortex, paracentral and mid-cingular cortex, inferior frontal cortex. Long duration stimuli: Premotor cortex, visual areas, somatosensory motor cortex, paracentral and mid- cingulate cortex, the tempo-parieto-occipital junction, dorsolateral-prefrontal cortex, posterior opercular cortex, medial temporal cortex, posterior cingulate cortex, orbito-frontal cortex. Average of short and long duration stimuli: Premotor cortex, somatosensory and motor cortex, posterior cingulate cortex, visual are, medial temporal cortex, paracentral and midcingulate cortex, anterior cingulate and medial prefrontal cortex, inferior frontal cortex, tempo-parieto-occipital junction, premotor cortex, somatosensory motor cortex, posterior cingulate cortex, medial temporal cortex, orbital and polar frontal cortex, hippocampus. Difference of short and long duration stimuli: Anterior cingulate and medial prefrontal cortex, ventral stream visual cortex, dorsal stream visual cortex, early visual cortex. Conclusions: The current study elicited multiple brain activation differences for the initial, acute high-dose cannabis vs. placebo condition, but only premotor cortex region survived as significant following multiple comparison correction for short and long duration stimuli contrast. A post hoc power analysis showed that adding 10 additional subjects to this sample would achieve significance with multiple comparison correction for medium effect sizes at alpha=0.05. Future studies on a larger sample can help identify such significant activation differences, and examining all doses and tasks would elucidate unfolding of effects longitudinally post-dose, and dose-dependence of effects.

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Berichte der Organisationen zum Thema "Dose-Response modeling"

1

Nottingham, Quinton J., Jeffrey B. Birch und Barry A. Bodt. Modeling Nonmonotonic Dose-Response Curves. Fort Belvoir, VA: Defense Technical Information Center, Januar 2001. http://dx.doi.org/10.21236/ada391664.

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2

NTP Research Report on National Toxicology Program Approach to Genomic Dose-Response Modeling. NIEHS, April 2018. http://dx.doi.org/10.22427/ntp-rr-5.

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