Social cognition as a mediator variable between neurocognition and functional outcome in schizophrenia: empirical review and new results by structural equation modeling

Cognitive impairments are currently regarded as important determinants of functional domains and are promising treatment goals in schizophrenia. Nevertheless, the exact nature of the interdependent relationship between neurocognition and social cognition as well as the relative contribution of each of these factors to adequate functioning remains unclear. The purpose of this article is to systematically review the findings and methodology of studies that have investigated social cognition as a mediator variable between neurocognitive performance and functional outcome in schizophrenia. Moreover, we carried out a study to evaluate this mediation hypothesis by the means of structural equation modeling in a large sample of 148 schizophrenia patients. The review comprised 15 studies. All but one study provided evidence for the mediating role of social cognition both in cross-sectional and in longitudinal designs. Other variables like motivation and social competence additionally mediated the relationship between social cognition and functional outcome. The mean effect size of the indirect effect was 0.20. However, social cognitive domains were differentially effective mediators. On average, 25% of the variance in functional outcome could be explained in the mediation model. The results of our own statistical analysis are in line with these conclusions: Social cognition mediated a significant indirect relationship between neurocognition and functional outcome. These results suggest that research should focus on differential mediation pathways. Future studies should also consider the interaction with other prognostic factors, additional mediators, and moderators in order to increase the predictive power and to target those factors relevant for optimizing therapy effects.

Coronary artery disease, nitric oxide and oxidative stress: the "Yin-Yang" effect--a Chinese concept for a worldwide pandemic

Prevention of coronary artery disease (CAD) and reduction of its mortality and morbidity remains a major public health challenge throughout the "Western world". Recent evidence supports the concept that the impairment of endothelial function, a hallmark of insulin resistance states, is an upstream event in the pathophysiology of insulin resistance and its main corollaries: atherosclerosis and myocardial infarction. Atherosclerosis is currently thought to be the consequence of a subtle imbalance between pro- and anti-oxidants that produces favourable conditions for lesion progression towards acute thrombotic complications and clinical events. Over the last decade, a remarkable burst of evidence has accumulated, offering the new perspective that bioavailable nitric oxide (NO) plays a pivotal role throughout the CAD-spectrum, from its genesis to the outcome after acute events. Vascular NO is a critical modulator of coronary blood flow by inhibiting smooth muscle contraction and platelet aggregation. It also acts in angiogenesis and cytoprotection. Defective endothelial nitric oxide synthase (eNOS) driven NO synthesis causes development of major cardiovascular risk factors (insulin resistance, arterial hypertension and dyslipidaemia) in mice, and characterises CAD-prone insulin-resistant humans. On the other hand, stimulation of inducible nitric oxide synthase (iNOS) and NO overproduction causes metabolic insulin resistance and characterises atherosclerosis, heart failure and cardiogenic shock in humans, suggesting a "Yin-Yang" effect of NO in the cardiovascular homeostasis. Here, we will present a concise overview of the evidence for this novel concept, providing the conceptual framework for developing a potential therapeutic strategy to prevent and treat CAD.

Fitting ACE Structural Equation Models to Case-Control Family Data

Investigators interested in whether a disease aggregates in families often collect case-control family data, which consist of disease status and covariate information for families selected via case or control probands. Here, we focus on the use of case-control family data to investigate the relative contributions to the disease of additive genetic effects (A), shared family environment (C), and unique environment (E). To this end, we describe a ACE model for binary family data and then introduce an approach to fitting the model to case-control family data. The structural equation model, which has been described previously, combines a general-family extension of the classic ACE twin model with a (possibly covariate-specific) liability-threshold model for binary outcomes. Our likelihood-based approach to fitting involves conditioning on the proband’s disease status, as well as setting prevalence equal to a pre-specified value that can be estimated from the data themselves if necessary. Simulation experiments suggest that our approach to fitting yields approximately unbiased estimates of the A, C, and E variance components, provided that certain commonly-made assumptions hold. These assumptions include: the usual assumptions for the classic ACE and liability-threshold models; assumptions about shared family environment for relative pairs; and assumptions about the case-control family sampling, including single ascertainment. When our approach is used to fit the ACE model to Austrian case-control family data on depression, the resulting estimate of heritability is very similar to those from previous analyses of twin data.

Analysis of nonlinear gain-induced effects on short-pulse amplification in doped fibers by use of an extended power equation

Optical pulse amplification in doped fibers is studied using an extended power transport equation for the coupled pulse spectral components. This equation includes the effects of gain saturation, gain dispersion, fiber dispersion, fiber nonlinearity, and amplified spontaneous emission. The new model is employed to study nonlinear gain-induced effects on the spectrotemporal characteristics of amplified subpicosecond pulses, in both the anomalous and the normal dispersion regimes.

Numerical solutions of elliptic inverse problems via the equation error method

To estimate a parameter in an elliptic boundary value problem, the method of equation error chooses the value that minimizes the error in the PDE and boundary condition (the solution of the BVP having been replaced by a measurement). The estimated parameter converges to the exact value as the measured data converge to the exact value, provided Tikhonov regularization is used to control the instability inherent in the problem. The error in the estimated solution can be bounded in an appropriate quotient norm; estimates can be derived for both the underlying (infinite-dimensional) problem and a finite-element discretization that can be implemented in a practical algorithm. Numerical experiments demonstrate the efficacy and limitations of the method.