Stress at work and psychological and immunological parameters in a group of Norwegian female bank employees

This study concerned the immunological correlates of stress and work. Self reported stress, emotional state, ego-strength, and defensiveness were examined in relation to measures of cellular immunity (T-cell response to Con A and number of T4 and T8 cells) and humoral immunity (concentrations of immunoglobulins and complement components in plasma) in a group of 96 Norwegian female bank employees. The analyses showed that workload, the stress factor with the highest group mean, was associated with number of T-cells, while strain due to body posture during work was related to concentrations of IgM and C3. Further, depression was significantly correlated with IgM concentration and T-cell number, and anxiety with C3 concentration. T-cell activity was the immunological parameter most consistently associated with psychological well-being, showing positive correlations with defensiveness and ego-strength and negative correlations with anxiety and depression. In conclusion, T-cell number and concentrations of IgM and C3 were sensitive to both work-related stress experience and emotional distress. Coping abilities, indicated by the MMPI Ego-strength scale and emotional distress, were reflected in T-cell activity; insufficient coping was associated with low T-cell activity.     

Modelling data from single-photon emission computerized tomography

The completely random character of radioactive disintegration provides the basis of a strong justification for a Poisson linear model for single-photon emission computed tomography data, which can be used to produce reconstructions of isotope densities, whether by maximum likelihood or Bayesian methods. However, such a model requires the construction of a matrix of weights, which represent the mean rates of arrival at each detector of photons originating from each point within the body space. Two methods of constructing these weights are discussed, and reconstructions resulting from phantom and real data are presented.     

MEANINGFUL REGRESSION AND ASSOCIATION MODELS FOR CLUSTERED ORDINAL DATA

Many proposed methods for analyzing clustered ordinal data focus on the regression model and consider the association structure within a cluster as a nuisance. However, the association structure is often of equal interest—for example, temporal association in longitudinal studies and association between responses to similar questions in a survey. We discuss the use, appropriateness, and interpretability of various latent variable and Markov models for the association structure and propose a new structure that exploits the ordinality of the response. The models are illustrated with a study concerning opinions regarding government spending and an analysis of stability and change in teenage marijuana use over time, where we reveal different behavioral patterns for boys and girls through a comprehensive investigation of individual response profiles.     

African American Children's Adjustment: The Roles of Maternal and Teacher Depressive Symptoms

This study examined the unique and interactive roles of mother and teacher depressive symptoms in child adjustment in 277 African American single mother‐headed families, as well as whether the associations differed depending on the age and gender of the child. Findings revealed a significant association between maternal depressive symptoms and child depressive symptoms in girls, but not boys. Moreover, the combination of higher levels of both mother and teacher depressive symptoms was associated with the highest level of child depressive symptoms and, for younger children, externalizing symptoms. The importance of considering the multiple social contexts in which children interact is discussed.     

Physiologically Based Liver Modeling and Risk Assessment

Because of the inherent complexity of biological systems, there is often a choice between a number of apparently equally applicable physiologically based models to describe uptake and metabolism processes in toxicology or risk assessment. These models may fit the particular data sets of interest equally well, but may give quite different parameter estimates or predictions under different (extrapolated) conditions. Such competing models can be discriminated by a number of methods, including potential refutation by means of strategic experiments, and their ability to suitably incorporate all relevant physiological processes. For illustration, three currently used models for steady-state hepatic elimination--the venous equilibration model, the parallel tube model, and the distributed sinusoidal perfusion model--are reviewed and compared with particular reference to their application in the area of risk assessment. The ability of each of the models to describe and incorporate such physiological processes as protein binding, precursor-metabolite relations and hepatic zones of elimination, capillary recruitment, capillary heterogeneity, and intrahepatic shunting is discussed. Differences between the models in hepatic parameter estimation, extrapolation to different conditions, and interspecies scaling are discussed, and criteria for choosing one model over the others are presented. In this case, the distributed model provides the most general framework for describing physiological processes taking place in the liver, and has so far not been experimentally refuted, as have the other two models. These simpler models may, however, provide useful bounds on parameter estimates and on extrapolations and risk assessments.