Leydig-cell tumour in children: variable clinical presentation, diagnostic features, follow-up and genetic analysis of four cases

BACKGROUND: Testicular tumours are relatively uncommon in infants and children, accounting for only 1-2% of all paediatric solid tumours. Of these approximately 1.5% are Leydig-cell tumours. Further, activating mutations of the luteinizing hormone receptor gene (LHR), as well as of the G protein genes, such as Gsalpha (gsp) and Gialpha (gip2) subunits, and cyclin-dependent kinase gene 4(CDK4) have been associated with the development of several endocrine neoplasms. AIMS/METHODS: In this report, the clinical variability of Leydig-cell tumours in four children is described. The LHR-, gsp-, gip2- and CDK4 genes were investigated to establish the possible molecular pathogenesis of the variable phenotype of the Leydig-cell tumours. RESULTS: No activating mutations in these genes were found in the four Leydig-cell tumours studied. Therefore, the absence of activating mutations in LHR, as well as in both the 'hot spot' regions for activating mutations within the G-alpha subunits and in the regulatory 'hot spot' on the CDK4 genes in these tumours indicates molecular heterogeneity among Leydig-cell tumours. CONCLUSION: Four children with a variable phenotype caused by Leydig-cell tumours are described. A molecular analysis of all the 'activating' genes and mutational regions known so far was performed, but no abnormalities were found. The lessons learnt from these clinically variable cases are: perform ultrasound early and most importantly, consider discrepancies between testicular swelling, tumour size and androgen production.

Motives for cannabis use as a moderator variable of distress among young adults

This study examined the moderating effect of social and coping motives on distress among young cannabis-using adults. A random sample of 2031 young Swiss adults was interviewed by means of a computer-assisted telephone interview. Cannabis users showed more distress, less positive health behaviour and higher hedonism compared to non-users. Taking motive for use as a moderator variable into consideration, it became evident that only cannabis users with coping motives showed lower mental health, more symptoms of psychopathology, more psychosocial distress and more life events than non-users. Young adults with social motives for use on the other hand did not differ from non-users in terms of distress. These differences between cannabis users with social and those with coping motives remained stable over two years. In both subgroups, participants with regular cannabis use at baseline did not increase distress nor did participants with higher distress at baseline increase the frequency of their cannabis use. Our results suggest that secondary prevention for cannabis users should target especially young adults with coping motives for use.

The Importance of Scale for Spatial-confounding Bias and Precision of Spatial Regression Estimators

Increasingly, regression models are used when residuals are spatially correlated. Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants. I consider the effects of residual spatial structure on the bias and precision of regression coefficients, developing a simple framework in which to understand the key issues and derive informative analytic results. When the spatial residual is induced by an unmeasured confounder, regression models with spatial random effects and closely-related models such as kriging and penalized splines are biased, even when the residual variance components are known. Analytic and simulation results show how the bias depends on the spatial scales of the covariate and the residual; bias is reduced only when there is variation in the covariate at a scale smaller than the scale of the unmeasured confounding. I also discuss how the scales of the residual and the covariate affect efficiency and uncertainty estimation when the residuals can be considered independent of the covariate. In an application on the association between black carbon particulate matter air pollution and birth weight, controlling for large-scale spatial variation appears to reduce bias from unmeasured confounders, while increasing uncertainty in the estimated pollution effect.

Semiparametric Theory for Causal Mediation Analysis: efficiency bounds, multiple robustness, and sensitivity analysis

Whilst estimation of the marginal (total) causal effect of a point exposure on an outcome is arguably the most common objective of experimental and observational studies in the health and social sciences, in recent years, investigators have also become increasingly interested in mediation analysis. Specifically, upon establishing a non-null total effect of the exposure, investigators routinely wish to make inferences about the direct (indirect) pathway of the effect of the exposure not through (through) a mediator variable that occurs subsequently to the exposure and prior to the outcome. Although powerful semiparametric methodologies have been developed to analyze observational studies, that produce double robust and highly efficient estimates of the marginal total causal effect, similar methods for mediation analysis are currently lacking. Thus, this paper develops a general semiparametric framework for obtaining inferences about so-called marginal natural direct and indirect causal effects, while appropriately accounting for a large number of pre-exposure confounding factors for the exposure and the mediator variables. Our analytic framework is particularly appealing, because it gives new insights on issues of efficiency and robustness in the context of mediation analysis. In particular, we propose new multiply robust locally efficient estimators of the marginal natural indirect and direct causal effects, and develop a novel double robust sensitivity analysis framework for the assumption of ignorability of the mediator variable.

On Causal Mediation Analysis with a Survival Outcome

Suppose that having established a marginal total effect of a point exposure on a time-to-event outcome, an investigator wishes to decompose this effect into its direct and indirect pathways, also know as natural direct and indirect effects, mediated by a variable known to occur after the exposure and prior to the outcome. This paper proposes a theory of estimation of natural direct and indirect effects in two important semiparametric models for a failure time outcome. The underlying survival model for the marginal total effect and thus for the direct and indirect effects, can either be a marginal structural Cox proportional hazards model, or a marginal structural additive hazards model. The proposed theory delivers new estimators for mediation analysis in each of these models, with appealing robustness properties. Specifically, in order to guarantee ignorability with respect to the exposure and mediator variables, the approach, which is multiply robust, allows the investigator to use several flexible working models to adjust for confounding by a large number of pre-exposure variables. Multiple robustness is appealing because it only requires a subset of working models to be correct for consistency; furthermore, the analyst need not know which subset of working models is in fact correct to report valid inferences. Finally, a novel semiparametric sensitivity analysis technique is developed for each of these models, to assess the impact on inference, of a violation of the assumption of ignorability of the mediator.

Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

TRENDS IN PARTICULATE MATTER AND MORTALITY: AN APPROACH TO THE ASSESSMENT OF UNMEASURED CONFOUNDING

We propose a method for diagnosing confounding bias under a model which links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 U.S. counties from 2000-2002. We decompose the association between PM2.5 and mortality into two components: 1) the association between “national trends” in PM2.5 and mortality; and 2) the association between “local trends,” defined as county-specificdeviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these two spatio-temporalscales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.

A Mechanistic Latent Variable Model for Estimating Drug Concentrations in the Male Genital Tract

The purpose of this study is to develop statistical methodology to facilitate indirect estimation of the concentration of antiretroviral drugs and viral loads in the prostate gland and the seminal vesicle. The differences in antiretroviral drug concentrations in these organs may lead to suboptimal concentrations in one gland compared to the other. Suboptimal levels of the antiretroviral drugs will not be able to fully suppress the virus in that gland, lead to a source of sexually transmissible virus and increase the chance of selecting for drug resistant virus. This information may be useful selecting antiretroviral drug regimen that will achieve optimal concentrations in most of male genital tract glands. Using fractionally collected semen ejaculates, Lundquist (1949) measured levels of surrogate markers in each fraction that are uniquely produced by specific male accessory glands. To determine the original glandular concentrations of the surrogate markers, Lundquist solved a simultaneous series of linear equations. This method has several limitations. In particular, it does not yield a unique solution, it does not address measurement error, and it disregards inter-subject variability in the parameters. To cope with these limitations, we developed a mechanistic latent variable model based on the physiology of the male genital tract and surrogate markers. We employ a Bayesian approach and perform a sensitivity analysis with regard to the distributional assumptions on the random effects and priors. The model and Bayesian approach is validated on experimental data where the concentration of a drug should be (biologically) differentially distributed between the two glands. In this example, the Bayesian model-based conclusions are found to be robust to model specification and this hierarchical approach leads to more scientifically valid conclusions than the original methodology. In particular, unlike existing methods, the proposed model based approach was not affected by a common form of outliers.

Wide QRS-complex tachycardia with variable VA-conduction: what is the mechanism?

We describe the case of a 16-year-old woman with a surgically corrected tetralogy of Fallot presenting with recurrent wide-QRS-complex tachycardia. The tachycardia could be induced and terminated with ventricular stimulation only. QRS morphology during sinus rhythm and tachycardia was identical and variable VA-conduction was observed. Mapping of the tachycardia showed that variations of HH intervals preceded VV intervals. Therefore, a mechanism involving re-entry within the bundle branches was suggested. However, detailed mapping showed cranial to caudal depolarization of the His bundle, leading to the diagnosis of atrioventricular node re-entrant tachycardia. The tachycardia was abolished by radiofrequency catheter ablation of the slow AV nodal pathway. We conclude that variable VA conduction can occur in patients with atrioventricular node re-entrant tachycardia. The atrial tissue is not always an integral part of the re-entrant circuit.

Precise pose recovery of distal locking holes from single calibrated fluoroscopic image via a novel variable decomposition approach

This paper presents a novel variable decomposition approach for pose recovery of the distal locking holes using single calibrated fluoroscopic image. The problem is formulated as a model-based optimal fitting process, where the control variables are decomposed into two sets: (a) the angle between the nail axis and its projection on the imaging plane, and (b) the translation and rotation of the geometrical model of the distal locking hole around the nail axis. By using an iterative algorithm to find the optimal values of the latter set of variables for any given value of the former variable, we reduce the multiple-dimensional model-based optimal fitting problem to a one-dimensional search along a finite interval. We report the results of our in vitro experiments, which demonstrate that the accuracy of our approach is adequate for successful distal locking of intramedullary nails.

Recurrent rearrangements of chromosome 1q21.1 and variable pediatric phenotypes

BACKGROUND: Duplications and deletions in the human genome can cause disease or predispose persons to disease. Advances in technologies to detect these changes allow for the routine identification of submicroscopic imbalances in large numbers of patients. METHODS: We tested for the presence of microdeletions and microduplications at a specific region of chromosome 1q21.1 in two groups of patients with unexplained mental retardation, autism, or congenital anomalies and in unaffected persons. RESULTS: We identified 25 persons with a recurrent 1.35-Mb deletion within 1q21.1 from screening 5218 patients. The microdeletions had arisen de novo in eight patients, were inherited from a mildly affected parent in three patients, were inherited from an apparently unaffected parent in six patients, and were of unknown inheritance in eight patients. The deletion was absent in a series of 4737 control persons (P=1.1x10(-7)). We found considerable variability in the level of phenotypic expression of the microdeletion; phenotypes included mild-to-moderate mental retardation, microcephaly, cardiac abnormalities, and cataracts. The reciprocal duplication was enriched in nine children with mental retardation or autism spectrum disorder and other variable features (P=0.02). We identified three deletions and three duplications of the 1q21.1 region in an independent sample of 788 patients with mental retardation and congenital anomalies. CONCLUSIONS: We have identified recurrent molecular lesions that elude syndromic classification and whose disease manifestations must be considered in a broader context of development as opposed to being assigned to a specific disease. Clinical diagnosis in patients with these lesions may be most readily achieved on the basis of genotype rather than phenotype.

Effect of constant and variable domain glycosylation on pharmacokinetics of therapeutic antibodies in mice

Previous studies on the effect of glycosylation on the elimination rate of antibodies have produced conflicting results. Here, we performed pharmacokinetic studies in mice with two preparations of a monoclonal IgG1 antibody enriched for complex type or high mannose type oligosaccharides at the Fc glycosylation site. No significant difference in the serum half-life was found between the two antibody glycoforms, nor was any difference observed in the serum half-lives of different complex type glycoforms. To evaluate the influence of glycosylation within the variable domain, a second monoclonal antibody, glycosylated in both the Fc and Fv domains, was separated into fractions containing different amounts of Fv-associated sialic acid and administered to mice. Again, no significant difference was found in the clearance rates of variants carrying different amounts of Fv-associated sialic acid or lacking Fv-glycosylation. These results suggest that glycosylation has little or no impact on the pharmacokinetic behavior of these two monoclonal antibodies in mice.