Trichloroethene levels in human blood and exhaled breath from controlled inhalation exposure

The organic constituents of exhaled human breath are representative of bloodborne concentrations through gas exchange in the blood/breath interface in the lungs. The presence of specific compounds can be an indicator of recent exposure or represent a biological response of the subject. For volatile organic compounds, sampling and analysis of breath is preferred to direct measurement from blood samples because breath collection is noninvasive, potentially infectious waste is avoided, the sample supply is essentially limitless, and the measurement of gas-phase analytes is much simpler in a gas matrix rather than in a complex biological tissue such as blood. However, to assess the distribution of a contaminant in the body requires a reasonable estimate of the blood level. We have investigated the use of noninvasive breath measurements as a surrogate for blood measurements for (high) occupational levels of trichloroethene in a controlled exposure experiment. Subjects were placed in an exposure chamber for 24 hr; they were exposed to 100 parts per million by volume trichloroethene for the initial 4 hr and to purified air for the remaining 20 hr. Matched breath and blood samples were collected periodically during the experiment. We modeled the resulting concentration data with respect to their time course and assessed the blood/breath relationship during the exposure (uptake) period and during the postexposure (elimination) period. Estimates for peak blood levels, compartmental distribution, and time constants were calculated from breath data and compared to direct blood measurements to assess the validity of the breath measurement methodology. Blood/breath partition coefficients were studied during both uptake and elimination. At equilibrium conditions at the end of the exposure, we could predict actual blood levels using breath elimination curve calculations and a literature value partition coefficient with a mean ratio of calculated:measured of 0.98 and standard error (SE) = 0.12 across all subjects. blood/breath comparisons at equilibrium resulted in calculated in vivo partition coefficients with a mean of 10.8 and SE = 0.60 across all subjects and experiments and 9.69 with SE = 0.93 for elimination-only experiments. We found that about 78% of trichloroethene entering the body during inhalation exposure is metabolized, stored, or excreted through routes other than exhalation.     

Correcting for measurement error in the analysis of case-control data with repeated measurements of exposure

The authors present a technique for correcting for exposure measurement error in the analysis of case-control data when subjects have a variable number of repeated measurements, and the average is used as the subject's measure of exposure. The true exposure as well as the measurement error are assumed to be normally distributed. The method transforms each subject's observed average by a factor which is a function of the measurement error parameters, prior to fitting the logistic regression model. The resulting logistic regression coefficient estimate based on the transformed average is corrected for error. A bootstrap method for obtaining confidence intervals for the true regression coefficient, which takes into account the variability due to estimation of the measurement error parameters, is also described. The method is applied to data from a nested case-control study of hormones and breast cancer.     

Noradrenaline modulates the electrical activity of white adipocytes by a cAMP-dependent mechanism

The two-compartment open model is currently used to assess the glomerular filtration rate (GFR) after a single intravenous injection or a constant rate intravenous infusion. This model needs multiple blood samples from a patient, thus numerous limited sampling models have been so far developed to reduce the number of blood samples. In the present study, the three-, four- and n-compartment closed models have been developed to assess GFR after and during a constant rate intravenous infusion, which include the renal and all possible non-renal elimination pathways. Although more non-renal elimination compartments were included in the modelling, the results show it only leads to the increase in the similar analytical solutions for these compartments and the analytical solution for the blood compartment is the same as that in the two-compartment open model. Theoretically, the developed models can be used to assess GFR with a single blood sample at any sampling time with several urine samples.     

Assay of aldehydes from lipid peroxidation: gas chromatography-mass spectrometry compared to thiobarbituric acid

Measurements of neurotransmitters in conscious animals have been restrictive in real-time. The use of specific enzymes within an amperometric probe, based upon a microdialysis membrane, may overcome some of these problems. I report the use of such a probe, with different perfusions of enzymes, to allow real-time measurement of glutamate, catecholamines and indoleamines, in conscious animals. At an adjacent site microdialysis sample collections were made concurrently and neurotransmitters measured in the dialysate. Both probes were positioned within the somatosensory cortex. Values obtained by amperometric probes were similar to those in dialysate samples for glutamate, catecholamines and indoleamines during basal and stimuli related collections. Amperometric measurements showed higher peak concentrations and better time resolution than dialysate sampling. This reflects sampling differences. Application of external stimuli increased extracellular concentrations of glutamate, catecholamines and indoleamines, in both forms of sampling. Dopamine measurements did not correlate well between the two forms of sampling. This may reflect the non-specificity of the enzyme (dopamine-beta-hydroxylase) used in the amperometric probe. This combination, of microdialysis and amperometry, offers a useful tool for real-time neurotransmitter studies in vivo.     

Uptake and decay of volatile organic compounds at environmental concentrations: application of a four-compartment model to a chamber study of five human subjects

Five subjects were exposed to nine volatile organic compounds (VOCs) at concentrations that can be encountered in everyday life. Breath samples were collected during a 10-h uptake phase and a 24-h decay phase. It was possible to determine four distinct slopes in the decay curve for each chemical. The distribution in the body and residence times in different tissues were calculated using a linear four-compartment mass-balance model. The model was used to predict breath concentrations for two subjects in a second chamber experiment including the same nine VOCs, representing three chemical classes (aromatic, aliphatic, and chlorinated compounds). Predicted values were generally within 25% of those observed, suggesting that the model parameters calculated here could be useful in estimating exposure and body burden to other VOCs in these three classes. Median residence times for the nine VOCs ranged from 3-12 min for compartment 1 (metabolizing); 0.3-2 h for compartment 2; 2-5 h for compartment 3; and 1-4 d for compartment 4. The fraction of the parent compound exhaled at equilibrium was estimated to range from 0.06-0.16 for four aromatic compounds and decane; 0.22-0.23 for trichloroethylene and dichloromethane; 0.35 for hexane; and 0.88 for 1,1,1-trichloroethane. Limited blood measurements were obtained for six of the nine VOCs in two subjects simultaneously with the breath samples over four-hour decay periods. Blood/breath ratios agreed well between the two subjects, but were higher than human blood/air partition coefficients reported in subjects exposed to high concentrations. This observation is consistent with results from other studies at relatively low concentrations.     

Sampling Issues in Urban Runoff Monitoring Programs: Composite versus Grab

Storm-water monitoring generally uses flow-weighted automatic composite samplers to collect a representative sample of an entire storm event. Automatic samplers are convenient but unfortunately they can be expensive, especially for temporary sampling needs or for short-term research projects. An alternative method is to use a series of grab samples. This paper examines the accuracy of event mean concentrations (EMCs) and mass first flush ratios calculated from a finite number of grab samples, and compares them to results from flow-weighted automatic samples. Both sampling techniques were evaluated using data collected from a three-year investigation of three highway sites. A large number of grab samples is needed to approach the accuracy and precision of flow weighted composite samples, and 30 grab samples per storm event generally estimated the EMCs within 20% average error. To detect a first flush, it is necessary to take even more grab samples or to adjust the timing of the sample collection toward the beginning of the storm. The superiority of automatic sampling for estimating EMCs for constituents compatible with automatic sampling is demonstrated.     

On-line determination of pulmonary blood flow using respiratory inert gas analysis

An inert gas analysis method has been developed to perform on-line real time determination of pulmonary blood flow using a nonrebreathing approach. This technique is based on a mathematical model describing mass balance of two inert gases which are breathed using an open gas circuit. The measurements using this method are noninvasive, easy to peform, and do not disturb normal physiological processes. As well, since data are collected on a breath-by-breath basis, it is possible to estimate other respiratory, cardiopulmonary, and metabolic parameters simultaneously in a breath-by-breath manner. Special consideration was given to developing effective data processing algorithms to minimize the influence of measurement noise and respiratory variations. Experimental studies to compare this method with other accepted techniques were conducted to validate the present technique.     

Uncertainty analysis methods for comparing predictive models and biomarkers: A case study of dietary methyl mercury exposure

Biologically based markers (biomarkers) are currently used to provide information on exposure, health effects, and individual susceptibility to chemical and radiological wastes. However, the development and validation of biomarkers are expensive and time consuming. To determine whether biomarker development and use offer potential improvements to risk models based on predictive relationships or assumed values, we explore the use of uncertainty analysis applied to exposure models for dietary methyl mercury intake. We compare exposure estimates based on self-reported fish intake and measured fish mercury concentrations with biomarker-based exposure estimates (i.e., hair or blood mercury concentrations) using a published data set covering 1 month of exposure. Such a comparison of exposure model predictions allowed estimation of bias and random error associated with each exposure model. From these analyses, both bias and random error were found to be important components of uncertainty regarding biomarker-based exposure estimates, while the diary-based exposure estimate was susceptible to bias. Application of the proposed methods to a simple case study demonstrates their utility in estimating the contribution of population variability and measurement error in specific applications of biomarkers to environmental exposure and risk assessment. Such analyses can guide risk analysts and managers in the appropriate validation, use, and interpretation of exposure biomarker information.     

Divergent effects of different antihypertensive drugs on endothelium-dependent vasodilation in the human forearm

OBJECTIVES: This paper describes 2 statistical methods designed to correct for bias from exposure measurement error in point and interval estimates of relative risk. METHODS: The first method takes the usual point and interval estimates of the log relative risk obtained from logistic regression and corrects them for nondifferential measurement error using an exposure measurement error model estimated from validation data. The second, likelihood-based method fits an arbitrary measurement error model suitable for the data at hand and then derives the model for the outcome of interest. RESULTS: Data from Valanis and colleagues' study of the health effects of antineoplastics exposure among hospital pharmacists were used to estimate the prevalence ratio of fever in the previous 3 months from this exposure. For an interdecile increase in weekly number of drugs mixed, the prevalence ratio, adjusted for confounding, changed from 1.06 to 1.17 (95% confidence interval [CI] = 1.04, 1.26) after correction for exposure measurement error. CONCLUSIONS: Exposure measurement error is often an important source of bias in public health research. Methods are available to correct such biases.     

基于厚度增强因子的X射线厚度测量模型

通过对X射线测厚仪测量机理的研究,剖析了其机理模型黑匣子。同时根据现场情况,分析射线衰减系数、散射效应以及样板材质等对X射线测厚仪测量精度的影响,提出了一种带厚度增强因子的非线性模型。试验结果表明,厚度在0.1～2.5 mm范围内,该模型只需要测量5个厚度点即完成基准曲线制作,测量精度可达到0.1%以内,简化了操作工序。     

Sampling variance in attenuated correlation coefficients: A Monte Carlo study.

Research in validity generalization has generated renewed interest in the sampling error of the Pearson correlation coefficient. The standard estimator for the sampling variance of the correlation was derived under assumptions that do not consider the presence of measurement error or range restriction in the data. The accuracy of the estimator in attenuated or restricted data has not been studied. This article presented the results of computer simulations that examined the accuracy of the sampling variance estimator in data containing measurement error. Sample sizes of n?=?25, n?=?60, and n?=?100 are used, with the reliability ranging from .10 to 1.00, and the population correlation ranging from .10 to 0.90. Results demonstrated that the estimator has a slight negative bias, but may be sufficiently accurate for practical applications if the sample size is at least 60. In samples of this size, the presence of measurement error does not add greatly to the inaccuracy of the estimator. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Abbreviated kinetic profiles in area-under-the-curve monitoring of cyclosporine therapy. Technical note

BACKGROUND: The new microemulsion formulation of cyclosporine (CsA-ME) displays more consistent pharmacokinetic properties than the original formulation and may allow successful implementation of an abbreviated area-under-the-curve (AUC) strategy. METHODS: Here we compared two limited sampling strategies in order to define the one that best predicts AUC after CsA-ME in 51 renal transplant recipients with stable renal function. Pharmacokinetics were based on analysis of blood samples collected over 12 hours after drug administration by high-performance liquid chromatography (HPLC). Predicted AUC was estimated by using a three-point (0, 1 and 3 hr) or a two-point (2 and 6 hr or 0 and 2 hr) sampling strategy. RESULTS: A simplified strategy with three time points of blood collection at 0, 1, and 3 hours after CsA-ME allowed adequate and accurate prediction of the daily exposure to CsA. AUC prediction with two-point sampling at 2 and 6 hours was less good with a very large error in prediction (only 59% of the estimated AUC were within the accepted range). This limitation was even more evident when the 0 and 2 hour time points were examined, in which only 51% of AUC estimates were included in the accepted range of variation (-10 to 10%). CONCLUSIONS: A limited strategy of three-point sampling taken early after dosing allows an excellent and perfectly reliable prediction of the actual AUC.     

Effect of measurement error on epidemiological studies of environmental and occupational exposures

Random error (misclassification) in exposure measurements usually biases a relative risk, regression coefficient, or other effect measure towards the null value (no association). The most important exception is Berkson type error, which causes little or no bias. Berkson type error arises, in particular, due to use of group average exposure in place of individual values. Random error in exposure measurements, Berkson or otherwise, reduces the power of a study, making it more likely that real associations are not detected. Random error in confounding variables compromises the control of their effect, leaving residual confounding. Random error in a variable that modifies the effect of exposure on health--for example, an indicator of susceptibility--tends to diminish the observed modification of effect, but error in the exposure can create a supurious appearance of modification. Methods are available to correct for bias (but not generally power loss) due to measurement error, if information on the magnitude and type of error is available. These methods can be complicated to use, however, and should be used cautiously as "correction" can magnify confounding if it is present.     

Exposure estimates to disinfection by-products of chlorinated drinking water

Exposure to disinfection by-products (DBPs) of drinking water is multiroute and occurs in households serviced by municipal water treatment facilities that disinfect the water as a necessary step to halt the spread of waterborne infectious diseases. Biomarkers of the two most abundant groups of DBPs of chlorination, exhaled breath levels of trihalomethanes (THMs) and urinary levels of two haloacetic acids, were compared to exposure estimates calculated from in-home tap water concentrations and responses to a questionnaire related to water usage. Background THM breath concentrations were uniformly low. Strong relationships were identified between the THM breath concentrations collected after a shower and both the THM water concentration and the THM exposure from a shower, after adjusting for the postshower delay time in collecting the breath sample. Urinary haloacetic acid excretion rates were not correlated to water concentrations. Urinary trichloroacetic acid excretion rates were correlated with ingestion exposure, and that correlation was stronger in a subset of individuals who consumed beverages primarily within their home where the concentration measurements were made. No correlation was observed between an average 48-hr exposure estimate and the urinary dichloroacetic acid excretion rate, presumably because of its short biological half-life. Valid biomarkers were identified for DBP exposures, but the time between the exposure and sample collection should be considered to account for different metabolic rates among the DBPs. Further, using water concentration as an exposure estimate can introduce misclassification of exposure for DBPs whose primary route is ingestion due to the great variability in the amount of water ingested across a population.     

蒙特卡洛法在多点校准线性测量系统不确定度评定中的应用

多点校准线性测量系统相比单点或两点校准具有更宽的测定范围和更高的准确度、可靠性,在化学分析、物理测试等多个领域都有着广泛的应用。实验提出了蒙特卡洛法(MCM)评定多点校准线性测量系统不确定度的数学模型和仿真模拟方法,并以电感耦合等离子体原子发射光谱法(ICP-AES)测量低合金钢中锰含量为例,介绍了MCM评定测量不确定度的详细步骤和应用方法。首先采用MCM法,根据输入量的概率分布类型,通过模拟抽样分析进行测量不确定度评定,再采用GUM法进行评定。实验结果显示,试样中锰含量的平均测量结果为(0.919±0.012)%,k=1.96,与GUM法计算得到的扩展不确定度一致。给出的方法实现了MCM法在多点校准线性测量系统不确定度评估中的应用,一定程度上解决了标准样品参考值与线性校准波动性引入不确定度量值的导入问题,有助于进一步推动不确定度评估与应用的创新与发展。     

Assessment of the influence of hydrogen nonexcretion on the usefulness of the hydrogen breath test and lactose tolerance test

The recognition of hydrogen nonexcretion in up to 20% of tested subjects and the large ethnic differences in the prevalence of lactose malabsorption make it necessary to reassess the diagnostic usefulness of the lactose tolerance test and the hydrogen breath test. Both tests were performed in 83 consecutive patients with suspected lactose malabsorption who ingested 50 g lactose. On a separate day a hydrogen breath test was performed after 25 g lactulose. The prevalence of hydrogen nonexcretion was 18%. The diagnostic usefulness of hydrogen breath test was influenced both by the individual threshold for hydrogen excretion and the amount of malabsorbed lactose. In addition to baseline values, breath samples for hydrogen measurements have to be taken at 30, 60, 90, 180, and 240 minutes after ingestion of lactose. For the lactose tolerance test only one measurement of serum glucose at 30 minutes is needed in addition to the baseline measurement. The combination of both tests excludes the influence of hydrogen nonexcretion, but even if a combined diagnostic approach utilizing the lactose hydrogen breath test and lactose tolerance test is used, 6% of patients presenting with symptoms suggestive of lactose intolerance cannot be classified.     

Reliability assessment of isometric knee extension measurements with a computer-assisted hand-held dynamometer

OBJECTIVE: To assess clinically relevant indexes of measurement error of hand-held dynamometer measurements using a computer-assisted hand-held dynamometer (CAHN-DY). DESIGN: In separate sessions, different physical therapists performed repeated measurements of maximal isometric knee extension. SETTING: Four physical therapy practices and outpatient departments. PATIENTS: Consecutive samples of 30 male and 28 female patients aged 13 to 77 years, with isolated orthopedic knee disorders participated in this study. MAIN OUTCOME MEASURES: For intrarater and interrater applications, the standard error of measurement (SEM) and related 95% confidence intervals and minimal detectable changes were assessed. RESULTS: Sixty percent of the patients performed within the therapist's upper force limit. Variance between sessions performed by the same or different therapists accounted for a large percentage of the measurement error (70% to 81%). For single measurements, the standard error of measurement was assessed not to exceed 10N x m. Minimal detectable changes for different designs varied from 13.8 to 28.2N x m. CONCLUSIONS: The CAHN-DY facilitated standardization of test performance in a satisfactory manner, resulting in less measurement error compared with simple hand-held dynamometry.     

Assessment of ecologic regression in the study of lung cancer and indoor radon

Ecologic regression studies conducted to assess the cancer risk of indoor radon to the general population are subject to methodological limitations, and they have given seemingly contradictory results. The authors use simulations to examine the effects of two major methodological problems that affect these studies: measurement error and misspecification of the risk model. In a simulation study of the effect of measurement error caused by the sampling process used to estimate radon exposure for a geographic unit, both the effect of radon and the standard error of the effect estimate were underestimated, with greater bias for smaller sample sizes. In another simulation study, which addressed the consequences of uncontrolled confounding by cigarette smoking, even small negative correlations between county geometric mean annual radon exposure and the proportion of smokers resulted in negative average estimates of the radon effect. A third study considered consequences of using simple linear ecologic models when the true underlying model relation between lung cancer and radon exposure is nonlinear. These examples quantify potential biases and demonstrate the limitations of estimating risks from ecologic studies of lung cancer and indoor radon.     

Clinical expression of myotonic dystrophy: the predictive role of DNA diagnosis

The two-compartment open model is currently used to assess glomerular filtration rate (GFR) with multiple blood samples. Numerous limited sampling models have been developed to reduce the number of blood samples. In the present study, the three-compartment closed model was used to assess GFR after a single intravenous injection. The three-compartment closed model includes blood, peripheral and urine compartments, thus the samples in urine can be used to estimate parts of pharmacokinetic parameters, thus to reduce the number of blood samples. Theoretically, the three-compartment closed model can assess GFR with one blood sample at any sampling time and three urine samples at any three sampling times.     

Issues in intraindividual variability: Individual differences in equilibria and dynamics over multiple time scales.

This article addresses three issues germane to experimental design and statistical analysis of intraindividual variability such as the articles contained within this special section. First, the time scale of the measurement of a process can have profound effects on the outcome of analyses of the resulting time series. Measurement in time poses special problems in the design of experiments: the time scale of the measurements must be appropriate for the time scale of the process. Second, deterministic and stochastic models should be fit at the individual level and only at a second level should individual differences in parameters be modeled. Third, one must consider the possibility that nomothetic relations may be exposed by the invariance of covariance between latent variables rather than within a factor analytic measurement model. (PsycINFO Database Record (c) 2010 APA, all rights reserved)