Uncertainty of Estimates of Cancer Risks Derived by Extrapolation from High to Low Doses and from Animals to Humans

The uncertainties associated with extrapolating model-based cancer risks from high to low doses and animal-based cancer risks to humans are examined. It is argued that low-dose linear extrapolation based on statistical confidence limits calculated from animal data is designed to account for data uncertainty, model-selection uncertainty, and model-fitting instability. The intent is to err on the side of safety, that is, overstating rather than understating the true risk. The tendency toward conservatism in predicting human cancer risks from animal data based on linear extrapolation is confirmed by a real-data analysis of the various sources of uncertainty involved in extrapolating from animals to humans. Along with the tendency toward conservatism, a high degree of overall uncertainty in the interspecies extrapolation process is demonstrated. It is concluded that human cancer risk estimates based on animal data may underestimate the true risk by a factor of 10 or may overestimate that risk by a factor of 1,000.

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Exploring Geostatistical Modeling and VisualizationTechniques of Uncertainties for Categorical Spatial Data

Journal of Information and Data Management ◽

10.5753/jidm.2021.1786 ◽

2021 ◽

Vol 12 (4) ◽

Author(s):

Carlos A. Felgueiras ◽

Jussara O. Ortiz ◽

Eduardo C. G. Camargo ◽

Laércio M. Namikawa ◽

Thales S. Körting

Keyword(s):

Spatial Data ◽

Three Dimensional ◽

Real Data ◽

Geographic Region ◽

Geostatistical Modeling ◽

Probability Maps ◽

Uncertainty Model ◽

Probability Criterion ◽

Sample Points

This article presents and analyzes the indicator geostatistical modeling and some visualization techniques of uncertainty models for categorical spatial attributes. A set of sample points of some categorical attribute is used as input information. The indicator approach requires a transformation of sample points on fields of indicator samples according to the classes of interest. Experimental and theoretical semivariograms of the indicator fields are defined representing the spatial variation of the indicator information. The indicator fields, along with their semivariograms, are used to determine the uncertainty model, the conditioned probability distribution function, of the attribute at any location inside the geographic region delimited by the samples. The probability functions are considered for producing prediction and probability maps based on the maximum class probability criterion. These maps can be visualized using different techniques. In this work, it is considered individual visualization of the predicted and probability maps and a combination of them. The predicted maps can also be visualized with or without constraints related to the uncertainty probabilities. The combined visualizations are based on three-dimensional (3D) planar projection and on the Red-Green-Blue to Intensity-Hue-Saturation (RGB-IHS) fusion transformation techniques. The methodology of this article is illustrated by a case study with real data, a sample set of soil textures observed in an experimental farm located in the region of São Carlos city in São Paulo State, Brazil. The resulting maps of this case study are presented and the advantages and the drawbacks of the visualization options are analyzed and discussed.

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USE OF MICRONUCLEUS EXPERIMENTS FOR THE DETECTION OF HUMAN CANCER RISKS: A BRIEF OVERVIEW

Proceedings of the Shevchenko Scientific Society. Medical Sciences ◽

10.25040/ntsh2021.02.05 ◽

2021 ◽

Vol 65 (2) ◽

Author(s):

Armen Nersesyan ◽

◽

Miroslav Mišík ◽

Andriy Cherkas ◽

Viktoria Serhiyenko ◽

...

Keyword(s):

Blood Cells ◽

Environmental Control ◽

Human Cancer ◽

Occupational Exposures ◽

Human Biomonitoring ◽

Target Cells ◽

Cancer Risks ◽

Wide Range

Introduction. Micronuclei (MN) are small extranuclear DNA-containing structures that are formed as a consequence of structural and numerical chromosomal aberrations. The advantage of MN experiments compared to conventional chromosomal analyses in metaphase cells is that the scoring is by far less time consuming and laborious. MN experiments are currently widely used for the routine screening of chemicals in vitro and in vivo but also for environmental control and human biomonitoring Objectives. The purpose of this review was to collect data on the use of MN experiments for the detection of increased cancer risks as a consequence of environmental, lifestyle and occupational exposures and the detection/diagnosis of different forms of cancer. Methods. Analysis of the literature on methods for MN experiments with humans; as well as the use of this technique in different areas of research. Results. To date, a wide range of protocols for human biomonitoring studies has been developed for the measurement of MN formation in peripheral blood cells and in epithelial from different organs (buccal and nasal cavity, cervix and bladder). In addition to MN, other nuclear anomalies can be scored which reflect genetic instability as well as acute toxicity and the division of target cells. Conclusions. The evidence is accumulating that MN can be used as a diagnostic tool for the detection of increased cancer risks as well as for the early diagnosis of cervical and bladder cancer

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Spatiotemporal multi-disease transmission dynamic measure for emerging diseases: an application to dengue and zika integrated surveillance in Thailand

BMC Medical Research Methodology ◽

10.1186/s12874-019-0833-6 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 2

Author(s):

Chawarat Rotejanaprasert ◽

Andrew B. Lawson ◽

Sopon Iamsirithaworn

Keyword(s):

Disease Transmission ◽

Hierarchical Modeling ◽

Model Fitting ◽

Real Data ◽

Emerging Diseases ◽

Integrated Analysis ◽

Alternative Measure ◽

Surveillance Systems ◽

Modeling Framework ◽

Important Challenge

Abstract Background New emerging diseases are public health concerns in which policy makers have to make decisions in the presence of enormous uncertainty. This is an important challenge in terms of emergency preparation requiring the operation of effective surveillance systems. A key concept to investigate the dynamic of infectious diseases is the basic reproduction number. However it is difficult to be applicable in real situations due to the underlying theoretical assumptions. Methods In this paper we propose a robust and flexible methodology for estimating disease strength varying in space and time using an alternative measure of disease transmission within the hierarchical modeling framework. The proposed measure is also extended to allow for incorporating knowledge from related diseases to enhance performance of surveillance system. Results A simulation was conducted to examine robustness of the proposed methodology and the simulation results demonstrate that the proposed method allows robust estimation of the disease strength across simulation scenarios. A real data example is provided of an integrative application of Dengue and Zika surveillance in Thailand. The real data example also shows that combining both diseases in an integrated analysis essentially decreases variability of model fitting. Conclusions The proposed methodology is robust in several simulated scenarios of spatiotemporal transmission force with computing flexibility and practical benefits. This development has potential for broad applicability as an alternative tool for integrated surveillance of emerging diseases such as Zika.

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EXTRAPOLATING RADIATION-INDUCED CANCER RISKS FROM LOW DOSES TO VERY LOW DOSES

Health Physics ◽

10.1097/hp.0b013e3181ad7f04 ◽

2009 ◽

Vol 97 (5) ◽

pp. 505-509 ◽

Cited By ~ 37

Author(s):

David J. Brenner

Keyword(s):

Low Doses ◽

Cancer Risks ◽

Radiation Induced Cancer ◽

Radiation Induced

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Peak fitting and integration uncertainties for the Aerodyne Aerosol Mass Spectrometer

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-3471-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3471-3523 ◽

Cited By ~ 2

Author(s):

J. C. Corbin ◽

A. Othman ◽

J. D. Haskins ◽

J. D. Allan ◽

B. Sierau ◽

...

Keyword(s):

Monte Carlo ◽

Monte Carlo Model ◽

Routine Data ◽

Real Data ◽

Peak Height ◽

Data Set ◽

Mass Spectrometers ◽

Aerosol Mass ◽

Peak Fitting ◽

Uncertainty Model

Abstract. The errors inherent in the fitting and integration of the pseudo-Gaussian ion peaks in Aerodyne High-Resolution Aerosol Mass Spectrometers (HR-AMS's) have not been previously addressed as a source of imprecision for these instruments. This manuscript evaluates the significance of these uncertainties and proposes a method for their estimation in routine data analysis. Peak-fitting uncertainties, the most complex source of integration uncertainties, are found to be dominated by errors in m/z calibration. These calibration errors comprise significant amounts of both imprecision and bias, and vary in magnitude from ion to ion. The magnitude of these m/z calibration errors is estimated for an exemplary data set, and used to construct a Monte Carlo model which reproduced well the observed trends in fits to the real data. The empirically-constrained model is used to show that the imprecision in the fitted height of isolated peaks scales linearly with the peak height (i.e., as n1), thus contributing a constant-relative-imprecision term to the overall uncertainty. This constant relative imprecision term dominates the Poisson counting imprecision term (which scales as n0.5) at high signals. The previous HR-AMS uncertainty model therefore underestimates the overall fitting imprecision. The constant relative imprecision in fitted peak height for isolated peaks in the exemplary data set was estimated as ~4% and the overall peak-integration imprecision was approximately 5%. We illustrate the importance of this constant relative imprecision term by performing Positive Matrix Factorization (PMF) on a~synthetic HR-AMS data set with and without its inclusion. Finally, the ability of an empirically-constrained Monte Carlo approach to estimate the fitting imprecision for an arbitrary number of known overlapping peaks is demonstrated. Software is available upon request to estimate these error terms in new data sets.

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The Predictive Value of Animal Data in Human Cancer Risk Assessment

Regulatory Toxicology and Pharmacology ◽

10.1006/rtph.1996.1080 ◽

1997 ◽

Vol 25 (2) ◽

pp. 94-102 ◽

Cited By ~ 10

Author(s):

Jan M.M Meijers ◽

Gerard M.H Swaen ◽

Louis J.N Bloemen

Keyword(s):

Risk Assessment ◽

Cancer Risk ◽

Predictive Value ◽

Human Cancer ◽

Cancer Risk Assessment ◽

Animal Data

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A novel bottom-up approach to bounding low-dose human cancer risks from chemical exposures

Regulatory Toxicology and Pharmacology ◽

10.1016/j.yrtph.2013.01.004 ◽

2013 ◽

Vol 65 (3) ◽

pp. 311-315 ◽

Cited By ~ 14

Author(s):

Thomas B. Starr ◽

James A. Swenberg

Keyword(s):

Low Dose ◽

Human Cancer ◽

Cancer Risks ◽

Bottom Up ◽

Chemical Exposures

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Time to repeal the Delaney Clause

Human & Experimental Toxicology ◽

10.1177/096032719401300903 ◽

1994 ◽

Vol 13 (9) ◽

pp. 602-603

Author(s):

H Elizabeth Driver

Keyword(s):

Current Knowledge ◽

Human Cancer ◽

Accurate Determination ◽

Cancer Risks ◽

Federal Food ◽

Genotoxic Carcinogens ◽

Mechanisms Of Carcinogenesis ◽

Williams Test ◽

Important Progress

The Delaney Clause of the Federal Food, Drug, and Cosmetic Act, enacted in 1958, prohibits the addition to the human food supply of any chemical that had caused cancer in humans or animals. The aim was to prevent cancer in humans. The scientific knowledge on causes of cancer and mechanisms of carcinogenesis in the 1950s can be rationalised to justify enactment of this Clause at that time. Since then, important progress in the fields of mechanism of carcinogenesis and cancer causation, and in analytical chemistry permitting accurate determination of trace amounts of chemicals, suggests that the Clause requires modification based on current knowledge. The documented human carcinogens are DNA reactive or genotoxic. Thus, the Clause should emphasise prohibition of the addition to human foods of proven genotoxins that are likely human cancer risks by contemporary standards. Such genotoxic carcinogens are those reliably positive in a battery of three tests: the Ames test in Salmonella typhimurium; the Williams test with evidence of DNA repair in hepatocytes; and direct documentation of DNA adduct formation in the 32P-postlabelling technique of Randerath.

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