Preliminary diagnostic reference levels for endoscopic retrograde cholangio-pancreatography in Greece.

The main objective of this study was to determine the preliminary Diagnostic Reference Levels (DRLs) in terms of Kerma Area Product (KAP) and fluoroscopy time (Tf) during Endoscopic Retrograde Cholangio-Pancreatography (ERCP) procedures. Additionally, an investigation was conducted to explore the statistical relation between KAP and Tf. Data from a set of 200 randomly selected patients treated in 4 large hospitals in Greece (50 patients per hospital) were analyzed in order to obtain preliminary DRLs for KAP and Tf during therapeutic ERCP procedures. Non-parametric statistic tests were performed in order to determine a statistically significant relation between KAP and Tf. The resulting third quartiles for KAP and Tf for hospitals (A, B, C and D) were found as followed: KAPA=10.7Gycm(2), TfA=4.9min; KAPB=7.5Gycm(2), TfB=5.0min; KAPC=19.0Gycm(2), TfC=7.3min; KAPD=52.4Gycm(2), TfD=15.8min. The third quartiles, calculated for the total 200 cases sample, are: KAP=18.8Gycm(2) and Tf=8.2min. For 3 out of 4 hospitals and for the total sample, p-values of statistical indices (correlation of KAP and Tf) are less than 0.001, while for the Hospital A p-values are ranging from 0.07 to 0.08. Using curve fitting, we finally determine that the relation of Tf and KAP is deriving from a power equation (KAP=Tf(1.282)) with R(2)=0.85. The suggested Preliminary DRLs (deriving from the third quartiles of the total sample) for Greece are: KAP=19Gycm(2) and Tf=8min, while the relation between KAP and Tf is efficiently described by a power equation.

THE IMPACT OF X-RAY UNIT TYPE USED FOR ENDOSCOPIC RETROGRADE CHOLANGIOPANCREATOGRAPHY PROCEDURES ON PATIENT DOSES.

To investigate whether the X-ray unit type used for interventional endoscopic retrograde cholangiopancreatography (ERCP) procedures may affect patient radiation doses. A total of 471 ERCP procedures performed in 4 hospitals with 4 types of X-ray units were studied. Kerma-area product (KAP), fluoroscopy time (T) and total number of radiographs acquired (F) were recorded. KAP, T and F values exhibited a great variation, ranging from 0.1 to 130.2 Gy cm2 (mean 16 Gy cm2), 0.13 to 33.7 min (mean 5.4 min) and 0 to 26 radiographs (mean 3.5), respectively. The respective mean values for the four types of X-ray units that were investigated were as follows: KAP: 17.4, 12.5, 5.6 and 36.3 Gy cm2, T: 4.7, 5.2, 3.8 and 11.5 min and F: 1.7, 7.4, 1.9 and 4.6 radiographs. The type of the X-ray unit seems to significantly affect patient radiation dose, with the C-arm delivering the lowest and the angiography unit the highest patient doses.

Mechanistic modeling of emergency events: assessing the impact of hypothetical releases of anthrax.

A modular system for source-to-dose-to-effect modeling analysis has been developed based on the modeling environment for total risk studies (MENTOR),((1)) and applied to study the impacts of hypothetical atmospheric releases of anthrax spores. The system, MENTOR-2E (MENTOR for Emergency Events), provides mechanistically consistent analysis of inhalation exposures for various release scenarios, while allowing consideration of specific susceptible subpopulations (such as the elderly) at the resolution of individual census tracts. The MENTOR-2E application presented here includes atmospheric dispersion modeling, statistically representative samples of individuals along with corresponding activity patterns, and population-based dosimetry modeling that accounts for activity and physiological variability. Two hypothetical release scenarios were simulated: a 100 g release of weaponized B. anthracis over a period of (a) one hour and (b) 10 hours, and the impact of these releases on population in the State of New Jersey was studied. Results were compared with those from simplified modeling of population dynamics (location, activities, etc.), and atmospheric dispersion of anthrax spores. The comparisons showed that in the two release scenarios simulated, each major approximation resulted in an overestimation of the number of probable infections by a factor of 5 to 10; these overestimations can have significant public health implications when preparing for and responding effectively to an actual release. This is in addition to uncertainties in dose-response modeling, which result in an additional factor of 5 to 10 variation in estimated casualties. The MENTOR-2E system has been developed in a modular fashion so that improvements in individual modules can be readily made without impacting the other modules, and provides a first step toward the development of models that can be used in supporting real-time decision making.

Environmental, dietary, demographic, and activity variables associated with biomarkers of exposure for benzene and lead.

Classification and regression tree methods represent a potentially powerful means of identifying patterns in exposure data that may otherwise be overlooked. Here, regression tree models are developed to identify associations between blood concentrations of benzene and lead and over 300 variables of disparate type (numerical and categorical), often with observations that are missing or below the quantitation limit. Benzene and lead are selected from among all the environmental agents measured in the NHEXAS Region V study because they are ubiquitous, and they serve as paradigms for volatile organic compounds (VOCs) and heavy metals, two classes of environmental agents that have very different properties. Two sets of regression models were developed. In the first set, only environmental and dietary measurements were employed as predictor variables, while in the second set these were supplemented with demographic and time-activity data. In both sets of regression models, the predictor variables were regressed on the blood concentrations of the environmental agents. Jack-knife cross-validation was employed to detect overfitting of the models to the data. Blood concentrations of benzene were found to be associated with: (a) indoor air concentrations of benzene; (b) the duration of time spent indoors with someone who was smoking; and (c) the number of cigarettes smoked by the subject. All these associations suggest that tobacco smoke is a major source of exposure to benzene. Blood concentrations of lead were found to be associated with: (a) house dust concentrations of lead; (b) the duration of time spent working in a closed workshop; and (c) the year in which the subject moved into the residence. An unexpected finding was that the regression trees identified time-activity data as better predictors of the blood concentrations than the measurements in environmental and dietary media.

Differential expression of glutamate dehydrogenase in cultured neurons and astrocytes from mouse cerebellum and cerebral cortex.

Glutamate dehydrogenase (GDH) specific activities, kinetic properties and allosteric regulation were studied in extracts from cultured neurons and astrocytes prepared from mouse cerebral cortex and cerebellum. Considerable differences were observed in the specific activity of the enzyme among the different cell types with astrocytes expressing the highest GDH activity. This may reflect the functional importance of these cells in glutamate uptake and metabolism. Among the neurons, the glutamatergic cerebellar granule cells showed a GDH specific activity that was 60% higher (P < 0.01) than that of the GABAergic cerebral cortical neurons. Also, the K(m) for ammonia was 1.7-fold higher in the cortical neurons than in the other cell types. These findings may reflect a particular need for the glutamatergic granule cells to synthesize glutamate via the GDH pathway. No differences were observed among the different cell types with regard to the allosteric properties of GDH expressed by these cells.

Environmental copper: its dynamics and human exposure issues.

This article provides an overview of the environmental patterns and dynamics of copper from the perspective of issues that affect our ability to examine current human exposures. It presents selected summary information on the levels of copper found in various media and exposure pathways from a variety of information sources, and discusses the breadth and the limitations of this information. The analysis presented focuses on the ability to provide quantitative values for both external metrics of exposures (microenvironmental levels) and internal biological markers of exposure. The status of the current information on environmental copper is placed within a conceptual framework that can be used to identify data gaps, assess the utility of current biological markers of exposure, and examine the need for systematic and consistent data-gathering studies to improve our ability to complete exposure assessments. A primary concern is the exposure to copper through potable water supplies; this is considered within a framework that examines copper levels and distribution in food, soil, air and sediments, as well as the levels found in biological media such as urine, blood, and hair. An existing water consumption model for copper and associated exposure factors is briefly discussed. This type of model will eventually be valuable within a total exposure analysis modeling framework that can consider and prioritize exposures from multiple routes and differentiate levels of concern for both excesses and deficiencies in exposure, an important issue, since copper is an essential nutrient. Finally, this review attempts to examine the needs for better information using as a basis the concerns briefly mentioned in the recent NRC report "Copper in Drinking Water" (National Research Council, 2000).

Translational invariance in nucleation theories: theoretical formulation.

The consequences of spontaneously broken translational invariance on the nucleation-rate statistical prefactor in theories of first-order phase transitions are analyzed. A hybrid, semiphenomenological approach based on field-theoretic analyses of condensation and modern density-functional theories of nucleation is adopted to provide a unified prescription for the incorporation of translational-invariance corrections to nucleation-rate predictions. A connection between these theories is obtained starting from a quantum-mechanical Hamiltonian and using methods developed in the context of studies on Bose-Einstein condensation. An extremum principle is used to derive an integro-differential equation for the spatially nonuniform mean-field order-parameter profile; the appropriate order parameter becomes the square root of the fluid density. The importance of the attractive intermolecular potential is emphasized, whereas the repulsive two-body potential is approximated by considering hard-sphere collisions. The functional form of the degenerate translational eigenmodes in three dimensions is related to the mean-field order parameter, and their contribution to the nucleation-rate prefactor is evaluated. The solution of the Euler-Lagrange variational equation is discussed in terms of either a proposed variational trial function or the complete numerical solution of the associated boundary-value integro-differential problem. Alternatively, if the attractive potential is not explicitly known, an approach that allows its formal determination from its moments is presented.

Integrated exposure and dose modeling and analysis system. 3. Deposition of inhaled particles in the human respiratory tract.

Detailed information on the composition-resolved size distribution of particulate matter deposited along the human respiratory tract can help linking epidemiological, toxicological, and pathological studies and thus potentially improve the understanding of the origin of pulmonary disorders induced by respirable pathogens. For this purpose, a new mechanistic dosimetry model describing the dynamics of respirable particles in the human airways was developed. Model predictions of transport and fate of inhaled aerosols are based on solutions of the aerosol general dynamic equation, which describes changes in particle size and mass distributions resulting from processes such as nucleation, condensation, coagulation, gas phase chemical reaction, and deposition. To compensate for approximating the three-dimensional problem by considering only axial variations along the airways, boundary layer effects are introduced via appropriate dimensionless transport parameters. The architecture of the human lung is described by Weibel's simple regular dichotomous model. An important advantage of the present approach is that it allows testing the significance of intersubject lung morphology and ventilation variability for particle deposition and dose calculations. The model predicts the evolution of size and composition distributions of inhaled particles and the deposition profile along the human lower respiratory tract: in general, model predictions are in qualitative and quantitative agreement with tracheobronchial and alveolar deposition data.

Efficient sensitivity/uncertainty analysis using the combined stochastic response surface method and automated differentiation: application to environmental and biological systems.

Estimation of uncertainties associated with model predictions is an important component of the application of environmental and biological models. "Traditional" methods for propagating uncertainty, such as standard Monte Carlo and Latin Hypercube Sampling, however, often require performing a prohibitive number of model simulations, especially for complex, computationally intensive models. Here, a computationally efficient method for uncertainty propagation, the Stochastic Response Surface Method (SRSM) is coupled with another method, the Automatic Differentiation of FORTRAN (ADIFOR). The SRSM is based on series expansions of model inputs and outputs in terms of a set of "well-behaved" standard random variables. The ADIFOR method is used to transform the model code into one that calculates the derivatives of the model outputs with respect to inputs or transformed inputs. The calculated model outputs and the derivatives at a set of sample points are used to approximate the unknown coefficients in the series expansions of outputs. A framework for the coupling of the SRSM and ADIFOR is developed and presented here. Two case studies are presented, involving (1) a physiologically based pharmacokinetic model for perchloroethylene for humans, and (2) an atmospheric photochemical model, the Reactive Plume Model. The results obtained agree closely with those of traditional Monte Carlo and Latin hypercube sampling methods, while reducing the required number of model simulations by about two orders of magnitude.

Stochastic response surface methods (SRSMs) for uncertainty propagation: application to environmental and biological systems.

Comprehensive uncertainty analyses of complex models of environmental and biological systems are essential but often not feasible due to the computational resources they require. "Traditional" methods, such as standard Monte Carlo and Latin Hypercube Sampling, for propagating uncertainty and developing probability densities of model outputs, may in fact require performing a prohibitive number of model simulations. An alternative is offered, for a wide range of problems, by the computationally efficient "Stochastic Response Surface Methods (SRSMs)" for uncertainty propagation. These methods extend the classical response surface methodology to systems with stochastic inputs and outputs. This is accomplished by approximating both inputs and outputs of the uncertain system through stochastic series of "well behaved" standard random variables; the series expansions of the outputs contain unknown coefficients which are calculated by a method that uses the results of a limited number of model simulations. Two case studies are presented here involving (a) a physiologically-based pharmacokinetic (PBPK) model for perchloroethylene (PERC) for humans, and (b) an atmospheric photochemical model, the Reactive Plume Model (RPM-IV). The results obtained agree closely with those of traditional Monte Carlo and Latin Hypercube Sampling methods, while significantly reducing the required number of model simulations.

Reconstructing week-long exposures to volatile organic compounds using physiologically based pharmacokinetic models.

Reconstruction of human exposure to toxic chemicals using physiologically based pharmacokinetic (PBPK) models and biomarkers is an attractive prospect, because biomarker measurements generally provide the most direct evidence of dose. Previously it has been shown that it is possible to reconstruct short-term (30 minute) exposure to chloroform, and that it is possible in some cases to resolve the total dose between two routes of uptake (Georgopoulos et al., 1994). In this paper it is shown that it is mathematically feasible to reconstruct longer term exposures to volatile organic compounds (VOCs), using benzene as a paradigm for other VOCs, and exhaled breath concentration as a biomarker of exposure. First, it is shown that exhaled breath concentration is an appropriate biomarker for long-term exposure to benzene, since benzene accumulates in fat and is eliminated in exhaled breath. Application of a benzene PBPK model (Travis et al., 1990) showed that benzene continues to accumulate in the fat compartment for over 10 days, and consequently fat acts as an integrator of dose during this period. Second, the benzene PBPK model is used to reconstruct exposure using the maximum likelihood approach. Since no data were available for long-term exposures of this duration, "data" with a normally distributed random error and 30% coefficient of variation were generated by the PBPK model for a variety of daily exposures. It was shown that in most cases it is possible to estimate cumulative exposure within 40% of the actual values, even when the exposure concentration-time profile is unknown. The estimated exposure is found to always be an underestimate of the true exposure when the exposure concentration is assumed to be constant.

Comparative evaluation of methods for estimating potential human exposure to ozone: photochemical modeling and ambient monitoring.

Photochemical modeling and ambient monitoring of ground-level ozone concentrations provide two alternative/complementary methods for calculating potential population exposure estimates. A comparative evaluation of these methods was undertaken over a study area comprised of the entire state of New Jersey and neighboring parts of Delaware, Maryland, Pennsylvania, and New York. Kriging, a geostatistical interpolation technique, was used for the interpolation of hourly ozone data from 38 air quality monitoring stations operating within the study area, to derive concentration fields for the entire domain. The Urban Airshed Model (UAM-IV), a comprehensive photochemical grid-based model, was then used to calculate the same concentrations from emissions and meteorology inputs. Concentration fields, thus developed, were linked with corresponding population data to calculate potential population exposure estimates to outdoor ozone (Ep.o). The adequacy of kriging as an interpolation technique was evaluated by comparing Ep.o estimates derived via photochemical UAM modeling with those calculated by using concentrations obtained from kriging UAM-calculated values at the locations of the monitoring stations. In general, UAM was found to predict higher Ep.o compared to those derived by kriging observations. In order to test the robustness of the interpolation methodology with respect to assumptions of statistical correlation, two different semivariogram models, spherical and exponential, were used for kriging. Application of the different semivariograms yielded almost identical Ep.o patterns.

Alternative Metrics for Assessing the Relative Effectiveness of NOx and VOC Emission Reductions in Controlling Ground-Level Ozone.

This study presents a new set of metrics quantifying the response of photochemical air pollution systems to changes in 03 precursor levels. Extending the traditional approach of using domain-wide maximum ozone values as the metric for guiding the development of emission control strategies, the new metrics incorporate attributes of the spatial and temporal pervasiveness and the severity of the ozone episodes considered for strategy development, as well as the impact on potential exposures to ozone. The usefulness of using various alternative criteria to better understand the directionality of the impact of emission controls is demonstrated via a set of 26 simulations of a three-day period of the severe July 1988 episode over the New Jersey-Philadelphia-Delaware Valley area. These simulations model the effect of across-the-board reductions of VOC and NO by 25, 50, 75, and 100% of the base case. The impact of these reductions is found to be dependent on the control objective (e.g., reduction of exposure vs. reduction of the maximum) as well as on the targeted level of the control objective (e.g., reduction of exposures below 120 ppb vs. reduction of exposures below 80 ppb).

Biomarkers of environmental benzene exposure.

Environmental exposures to benzene result in increases in body burden that are reflected in various biomarkers of exposure, including benzene in exhaled breath, benzene in blood and urinary trans-trans-muconic acid and S-phenylmercapturic acid. A review of the literature indicates that these biomarkers can be used to distinguish populations with different levels of exposure (such as smokers from nonsmokers and occupationally exposed from environmentally exposed populations) and to determine differences in metabolism. Biomarkers in humans have shown that the percentage of benzene metabolized by the ring-opening pathway is greater at environmental exposures than that at higher occupational exposures, a trend similar to that found in animal studies. This suggests that the dose-response curve is nonlinear; that potential different metabolic mechanisms exist at high and low doses; and that the validity of a linear extrapolation of adverse effects measured at high doses to a population exposed to lower, environmental levels of benzene is uncertain. Time-series measurements of the biomarker, exhaled breath, were used to evaluate a physiologically based pharmacokinetic (PBPK) model. Biases were identified between the PBPK model predictions and experimental data that were adequately described using an empirical compartmental model. It is suggested that a mapping of the PBPK model to a compartmental model can be done to optimize the parameters in the PBPK model to provide a future framework for developing a population physiologically based pharmacokinetic model.

Exposure measurement needs for hazardous waste sites: two case studies.

The science of exposure assessment has been expanding both its theoretical and experimental bases over the past two years. Recent theoretical work published by the authors in the Journal of Exposure Analysis and Environmental Epidemiology (Volume 4, Number 3, 1994) has defined a multistep process to couple measurement data with mathematical models of exposure and dose. The present manuscript discusses the need for improving the measurement of exposure in order to reduce uncertainties in the potential risk and, eventually, the occurrences of health outcomes in the community environment. The discussion focuses on hazardous waste sites and how improving or routinely introducing exposure measurements to the remedial investigation can lead to a better understanding of how the potential population can get exposed via single or multiple activities. This information can help to better understand the need for specific remediation actions and selection of the types of models that can be used to predict exposure for a large population and to estimate the reduction in postremediation exposure for a local population (National Research Council, 1991).

Application of a biologically-based RFD estimation method to tetrachlorodibenzo-p-dioxin (TCDD) mediated immune suppression and enzyme induction.

The current methods for a reference dose (RfD) determination can be enhanced through the use of biologically-based dose response analysis. Methods developed here utilizes information from tetrachlorodibenzo-p-dioxin (TCDD) to focus on noncancer endpoints, specifically TCDD mediated immune system alterations and enzyme induction. Dose-response analysis, using the Sigmoid-Emax (EMAX) function, is applied to multiple studies to determine consistency of response. Through the use of multiple studies and statistical comparison of parameter estimates, it was demonstrated that the slope estimates across studies were very consistent. This adds confidence to the subsequent effect dose estimates. This study also compares traditional methods of risk assessment such as the NOAEL/safety factor to a modified benchmark dose approach which is introduced here. Confidence in the estimation of an effect dose (ED10) was improved through the use of multiple datasets. This is key to adding confidence to the benchmark dose estimates. In addition, the Sigmoid-Emax function when applied to dose-response data using nonlinear regression analysis provides a significantly improved fit to data increasing confidence in parameter estimates which subsequently improve effect dose estimates.

A distributed parameter physiologically-based pharmacokinetic model for dermal and inhalation exposure to volatile organic compounds.

Estimates of dermal dose from exposures to toxic chemicals are typically derived using models that assume instantaneous establishment of steady-state dermal mass flux. However, dermal absorption theory indicates that this assumption is invalid for short-term exposures to volatile organic chemicals (VOCs). A generalized distributed parameter physiologically-based pharmacokinetic model (DP-PBPK), which describes unsteady state dermal mass flux via a partial differential equation (Fickian diffusion), has been developed for inhalation and dermal absorption of VOCs. In the present study, the DP-PBPK model has been parameterized for chloroform, and compared with two simpler PBPK models of chloroform. The latter are lumped parameter models, employing ordinary differential equations, that do not account for the dermal absorption time lag associated with the accumulation of permeant chemical in tissue represented by permeability coefficients. All three models were evaluated by comparing simulated post-exposure exhaled breath concentration profiles with measured concentrations following environmental chloroform exposures. The DP-PBPK model predicted a time-lag in the exhaled breath concentration profile, consistent with the experimental data. The DP-PBPK model also predicted significant volatilization of chloroform, for a simulated dermal exposure scenario. The end-exposure dermal dose predicted by the DP-PBPK model is similar to that predicted by the EPA recommended method for short-term exposures, and is significantly greater than the end-exposure dose predicted by the lumped parameter models. However, the net dermal dose predicted by the DP-PBPK model is substantially less than that predicted by the EPA method, due to the post-exposure volatilization predicted by the DP-PBPK model. Moreover, the net dermal dose of chloroform predicted by all three models was nearly the same, even though the lumped parameter models did not predict substantial volatilization.

Alternative models for low dose-response analysis of biochemical and immunological endpoints for tetrachlorodibenzo-p-dioxin.

As part of the current reassessment of dioxin, the empirical relationships between administered tetrachlorodibenzo-p-dioxin and selected immune and biochemical endpoints were investigated. A dose-response analysis from the published literature was performed using Linear, Sigmoid-Emax and Power Law functions. The results of this analysis indicate that the use of a wide dose range may bias the interpretation of low-dose phenomenon. The Power Law function was applied exclusively to low-dose subsets enabling estimation of dose response in the low-dose range. Subsequently, high-dose data were fit using Power Law subset analysis. This approach resulted in a change in slope value from low- to high-dose subsets for thymic atrophy, immune suppression, benzo(a)pyrene hydroxylase activity, and ethoxyresorufin-o-deethylase activity. This change suggests that there is a high probability that there is a tissue concentration along the dose-response continuum which results in biological activity from low to high dose. This analysis also demonstrates that the Power Law functional fit to the low-dose data differs quantitatively from the fit to the high-dose data for several noncancer endpoints. Therefore, by using the Power Law function a more accurate and biologically relevant assessment of risk can be produced for non-cancer endpoints.

Regulatory ozone modeling: status, directions, and research needs.

The Clean Air Act Amendments (CAAA) of 1990 have established selected comprehensive, three-dimensional, Photochemical Air Quality Simulation Models (PAQSMs) as the required regulatory tools for analyzing the urban and regional problem of high ambient ozone levels across the United States. These models are currently applied to study and establish strategies for meeting the National Ambient Air Quality Standard (NAAQS) for ozone in nonattainment areas; State Implementation Plans (SIPs) resulting from these efforts must be submitted to the U.S. Environmental Protection Agency (U.S. EPA) in November 1994. The following presentation provides an overview and discussion of the regulatory ozone modeling process and its implications. First, the PAQSM-based ozone attainment demonstration process is summarized in the framework of the 1994 SIPs. Then, following a brief overview of the representation of physical and chemical processes in PAQSMs, the essential attributes of standard modeling systems currently in regulatory use are presented in a nonmathematical, self-contained format, intended to provide a basic understanding of both model capabilities and limitations. The types of air quality, emission, and meteorological data needed for applying and evaluating PAQSMs are discussed, as well as the sources, availability, and limitations of existing databases. The issue of evaluating a model's performance in order to accept it as a tool for policy making is discussed, and various methodologies for implementing this objective are summarized. Selected interim results from diagnostic analyses, which are performed as a component of the regulatory ozone modeling process for the Philadelphia-New Jersey region, are also presented to provide some specific examples related to the general issues discussed in this work. Finally, research needs related to a) the evaluation and refinement of regulatory ozone modeling, b) the characterization of uncertainty in photochemical modeling, and c) the improvement of the model-based ozone-attainment demonstration process are presented to identify future directions in this area.