Cognitive behavioral therapy of socially phobic children focusing on cognition: a randomised wait-list control study

BACKGROUND: Although literature provides support for cognitive behavioral therapy (CBT) as an efficacious intervention for social phobia, more research is needed to improve treatments for children. METHODS: Forty four Caucasian children (ages 8-14) meeting diagnostic criteria of social phobia according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed.; APA, 1994) were randomly allocated to either a newly developed CBT program focusing on cognition according to the model of Clark and Wells (n = 21) or a wait-list control group (n = 23). The primary outcome measure was clinical improvement. Secondary outcomes included improvements in anxiety coping, dysfunctional cognitions, interaction frequency and comorbid symptoms. Outcome measures included child report and clinican completed measures as well as a diagnostic interview. RESULTS: Significant differences between treatment participants (4 dropouts) and controls (2 dropouts) were observed at post test on the German version of the Social Phobia and Anxiety Inventory for Children. Furthermore, in the treatment group, significantly more children were free of diagnosis than in wait-list group at post-test. Additional child completed and clinician completed measures support the results. DISCUSSION: The study is a first step towards investigating whether CBT focusing on cognition is efficacious in treating children with social phobia. Future research will need to compare this treatment to an active treatment group. There remain the questions of whether the effect of the treatment is specific to the disorder and whether the underlying theoretical model is adequate. CONCLUSION: Preliminary support is provided for the efficacy of the cognitive behavioral treatment focusing on cognition in socially phobic children. Active comparators should be established with other evidence-based CBT programs for anxiety disorders, which differ significantly in their dosage and type of cognitive interventions from those of the manual under evaluation (e.g. Coping Cat)

Chelators in Iron and Copper Toxicity

Purpose of Review Chelation therapy is used for diseases causing an imbalance of iron levels (for example haemochromatosis and thalassaemia) or copper levels (for example Menkes’ and Wilson’s diseases). Currently, most pharmaceutical chelators are relatively simple but often have side effects. Some have been taken off the market. This review attempts to find theory and knowledge required to design or find better chelators. Recent Findings Recent research attempting to understand the biological mechanisms of protection against iron and copper toxicity is reviewed. Understanding of molecular mechanisms behind normal iron/copper regulation may lead to the design of more sophisticated chelators. The theory of metal ion toxicity explains why some chelators, such as EDTA, which chelate metal ions in a way which exposes the ion to the surrounding environment are shown to be unsuitable except as a means of killing cancer cells. The Lewis theory of acids and bases suggests which amino acids favour the attachment of the hard/intermediate ions Fe2+, Fe3+, Cu2+ and soft ion Cu+. Non-polar amino acids will chelate the ion in a position not in contact with the surrounding cellular environment. The conclusion is that only the soft ion binding cysteine and methionine appear as suitable chelators. Clearly, nature has developed proteins which are less restricted. Recent research on naturally produced chelators such as siderophores and phytochemicals show some promise as pharmaceuticals. Summary Although an understanding of natural mechanisms of Fe/Cu regulation continues to increase, the pharmaceutical chelators for metal overload diseases remain simple non-protein molecules. Natural and synthetic alternatives have been studied but require further research before being accepted

Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation

The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid‐dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient‐specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed‐form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state‐of‐the‐art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long‐term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system