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Photon attenuation can reduce the diagnostic accuracy of cardiac SPECT imaging. Bellini et al have previously derived a mathematically exact method to compensate for attenuation in a uniform attenuator. Since the human thorax contains structures with differing attenuation properties, non-uniform attenuation compensation is required in cardiac SPECT. Given an estimate of the patient attenuation map, we show that the Bellini attenuation compensation method can be used in cardiac SPECT to provide a quantitatively accurate reconstruction of a central region in the image which includes the heart and surrounding soft tissue. Simulations using a mathematical cardiac-torso phantom were conducted to evaluate the Bellini method and to compare its performance to the ML-EM iterative algorithm, and to 180 degrees and 360 degrees filtered backprojection (FBP) with no attenuation compensation. 'Bulls-eye' polar maps and circumferential profiles showed that both the Bellini method and the ML-EM algorithm provided quantitatively accurate reconstructions of the myocardium, with a substantial reduction in attenuation-induced artifacts that were observed in the FBP images. The computational load required to implement the Bellini method is approximately equivalent to that required for one iteration of the ML-EM algorithm, thus it is suitable for routine clinical use.
While many studies have quantified the sources of variation in exposure to workplace contaminants for individual groups of workers, patterns of exposure variability have not been investigated since a comprehensive evaluation was carried out over 10 years ago. Therefore, a systematic review of the literature was conducted to identify studies that applied the one-way random-effects model to describe exposure profiles of groups of workers classified on the basis of the kind of work performed and where it was performed. Quantitative estimates of the sources of variation in exposure along with information related to the workplace, contaminant and sampling strategy were compiled. For subsets of the data, based upon the classification scheme used to group workers, weighted empirical cumulative distribution functions were constructed and compared using the non-parametric Kolomogorov-Smirnov two-sample test. Further stratifications evaluated differences by industry, agent and characteristics of the sampling strategy. The review identified nearly 60 studies that examined the within-worker and between-worker sources of variation in exposure to workplace contaminants. In pooling results across studies, the between-worker variability increased as workers were aggregated across jobs and locations. The within-worker variability for an occupational group of workers was generally larger than the between-worker variability, although the differences in the variation in exposures across work shifts relative to the variation among workers' mean exposure levels diminished as groups were combined across jobs and locations. On average, gaseous exposures were more homogeneous than exposures to aerosols or dermal agents as were exposures in the chemical industry compared with the non-chemical industry. The design of sampling strategies also plays an important role with greater variability among groups of workers who were sampled randomly rather than systematically; in addition, differences were detected on the basis of the study period and sample size. In evaluating key features of the design and methods of the studies identified in the review, several methodological issues emerged given the heterogeneity in terms of how censored data were handled, which estimation method was applied and whether underlying assumptions of the models were met. Notwithstanding the utility of quantifying sources of variation in exposure, several challenges lie ahead with regard to ensuring quality in the collection, analysis and reporting of exposure monitoring data that would enhance efforts to accurately assess exposure.
The classical twin study is the most popular design in behavioural genetics. It has strong roots in biometrical genetic theory, which allows predictions to be made about the correlations between observed traits of identical and fraternal twins in terms of underlying genetic and environmental components. One can infer the relative importance of these 'latent' factors (model parameters) by structural equation modelling (SEM) of observed covariances of both twin types. SEM programs estimate model parameters by minimising a goodness-of-fit function between observed and predicted covariance matrices, usually by the maximum-likelihood criterion. Likelihood ratio statistics also allow the comparison of fit of different competing models. The program Mx, specifically developed to model genetically sensitive data, is now widely used in twin analyses. The flexibility of Mx allows the modelling of multivariate data to examine the genetic and environmental relations between two or more phenotypes and the modelling to categorical traits under liability-threshold models.
A surrogate endpoint is an endpoint that is obtained sooner, at lower cost, or less invasively than the true endpoint for a health outcome and is used to make conclusions about the effect of intervention on the true endpoint. In this approach, each previous trial with surrogate and true endpoints contributes an estimated predicted effect of intervention on true endpoint in the trial of interest based on the surrogate endpoint in the trial of interest. These predicted quantities are combined in a simple random-effects meta-analysis to estimate the predicted effect of intervention on true endpoint in the trial of interest. Validation involves comparing the average prediction error of the aforementioned approach with (i) the average prediction error of a standard meta-analysis using only true endpoints in the other trials and (ii) the average clinically meaningful difference in true endpoints implicit in the trials. Validation is illustrated using data from multiple randomized trials of patients with advanced colorectal cancer in which the surrogate endpoint was tumor response and the true endpoint was median survival time.
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Although many HPLC methods are available in the literature only a fraction of these are applicable to the analysis of known drugs in human biological fluids. This paper presents the favoured approach of a laboratory involved in the quantitative assay of drugs in man for the subsequent study of pharmacokinetics and bioavailability.
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The objective of this study was to develop a screening process for the analysis of sexual assault samples. Recently, the Society of Forensic Toxicologists created a committee to address the issue of drug-facilitated sexual assault (DFSA) in the toxicology field. This committee prepared a list of drugs that could be, or have been, used in DFSAs. The list comprises about 50 compounds, including illicit, prescription, and over-the-counter drugs. Using this list, our laboratory wanted an easy, fast, and sensitive method to analyze a urine sample for all 50 of these drugs. We screened and confirmed for 20 compounds, including cocaine, amphetamines, benzodiazepines, barbiturates, opiates, methadone, alcohol, and PCP. A gas chromatographic-mass spectrometric screening method that was able to detect the remaining 30 compounds following 1 extraction and using only 2 mL of urine was developed. The process is inexpensive and uses equipment available in most forensic toxicology laboratories. This method is recommended for any laboratory that commonly receives specimens collected from sexual assault victims and is interested in a more thorough analysis.
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Past literature suggests that schizophrenic men and women may be at different risks for developing different subtypes of schizophrenia. This hypothesis was tested using data from the well-known retrospective cohort family studies, the Iowa 500 and the Iowa non-500. The sample consisted of 171 male and 161 female DSM-III schizophrenic patients and 713 of their first-degree relatives. First, bivariate tests for gender differences were conducted regarding family morbidity, age of onset, premorbid history, season of birth, and expression of deficit and affective symptoms. Restricted maximum likelihood latent class analysis was then used to test whether there was a subgroup of schizophrenic men who were more likely to have a low familial risk for schizophrenia or schizophrenia spectrum disorders, deficit symptoms, poor premorbid history, and birth in the winter months, suggesting possible early environmental insults, compared to schizophrenic women. Results showed that although men were more likely to meet these criteria, women also met them, thus suggesting gender differences in the prevalence of the subtype. Schizophrenic women were more likely to express a form of the illness characterized by dysphoria, persecutory delusions, and a higher family morbidity risk for schizophrenia than schizophrenic men. Results for spectrum disorders among relatives were equivocal with regard to gender.
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OBJECTIVE: To evaluate whether mothers' psychiatric history biases reports of their children's behavior problems, mothers' and teachers' reports of children's behavior problems were compared using a recently developed statistical approach. METHOD: Child Behavior Checklists and Teacher's Report Forms were completed by mothers and teachers, respectively, about 801 six-year-old children. Mother's history of major depression, anxiety disorders, and substance use disorder was assessed by using the National Institute of Mental Health Diagnostic Interview Schedule. Generalized estimating equations were used for data analysis. RESULTS: According to both teachers and mothers, maternal history of major depression was associated with more internalizing problems; the association was significantly stronger when mothers were the informants. Mothers with history of any psychiatric disorder reported more externalizing problems in their children than expected, whereas teachers' reports of externalizing behaviors were unrelated to maternal psychiatric history. These findings could not be explained by variations in children's behaviors across settings. CONCLUSION: The generalized estimating equation models enabled simultaneous examination of whether children of depressed mothers have excess behavior problems and whether depressed mothers overreport behavior problems in their children. The results indicate that children of depressed mothers have more internalizing problems. In addition, depressed mothers overstate and overgeneralize their offspring's behavior problems. This study broadens the concerns with reporting bias beyond maternal depression to include other psychiatric problems. The results emphasize the potential for bias in family history studies that rely on informants.
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