Alexithymia and interleukin variations in somatoform disorder

Objective: The aim of the present study was to investigate if somatoform disorders (SFD) are associated with changes in the normal serum levels of important interleukins, and further, to establish if these changes are related to the presence and severity of alexithymia in patients with SFD. Methods: Twenty-four unmedicated patients who met the International Classification of Diseases (ICD-10) diagnostic criteria for SFD completed the psychological questionnaire to assess alexithymia (Toronto Alexithymia Scale), symptom reporting (SCL-90-R) and diagnostic criteria for SFD (Screening for Somatoform Symptoms scale). Serum concentrations of soluble interleukin 2 receptor α (sIL-2 Rα), IL-4, IL-6, IL-10 and IL-12 were determined in patients with SFD and in 9 healthy subjects. Results: In patients with SFD, serum levels of IL-6 (p < 0.001), IL-10 (p = 0.047) and immunoglobulin E (p = 0.045) were significantly increased in comparison with healthy controls. Additionally, a negative correlation was observed between the level of alexithymia ('total' Toronto Alexithymia Scale score) and the serum levels of sIL-2 Rα (r = -0.538) in SFD. Conclusions: Taken together, these results suggest that SFD, with clinically significant alexithymia, are associated with a reduction in Th1-mediated immune function and an increase in the activation of the Th2 immune function, indicated by the augmented serum levels of IL-6 and IL-10 and elevated immunoglobulin E. Copyright © 2007 S. Karger AG.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Hierarchical classification of G-protein-coupled receptors with data-driven selection of attributes and classifiers

We address the important bioinformatics problem of predicting protein function from a protein's primary sequence. We consider the functional classification of G-Protein-Coupled Receptors (GPCRs), whose functions are specified in a class hierarchy. We tackle this task using a novel top-down hierarchical classification system where, for each node in the class hierarchy, the predictor attributes to be used in that node and the classifier to be applied to the selected attributes are chosen in a data-driven manner. Compared with a previous hierarchical classification system selecting classifiers only, our new system significantly reduced processing time without significantly sacrificing predictive accuracy.

On the 'classification' of neurodegenerative disorders:discrete entities, overlap or continuum?

The traditional method of classifying neurodegenerative diseases is based on the original clinico-pathological concept supported by 'consensus' criteria and data from molecular pathological studies. This review discusses first, current problems in classification resulting from the coexistence of different classificatory schemes, the presence of disease heterogeneity and multiple pathologies, the use of 'signature' brain lesions in diagnosis, and the existence of pathological processes common to different diseases. Second, three models of neurodegenerative disease are proposed: (1) that distinct diseases exist ('discrete' model), (2) that relatively distinct diseases exist but exhibit overlapping features ('overlap' model), and (3) that distinct diseases do not exist and neurodegenerative disease is a 'continuum' in which there is continuous variation in clinical/pathological features from one case to another ('continuum' model). Third, to distinguish between models, the distribution of the most important molecular 'signature' lesions across the different diseases is reviewed. Such lesions often have poor 'fidelity', i.e., they are not unique to individual disorders but are distributed across many diseases consistent with the overlap or continuum models. Fourth, the question of whether the current classificatory system should be rejected is considered and three alternatives are proposed, viz., objective classification, classification for convenience (a 'dissection'), or analysis as a continuum.

Health of entrepreneurs versus employees in a national representative sample

Prior research has found entrepreneurs to experience significantly higher job control and job demands compared with employees. This suggests that entrepreneurs have so-called active jobs and thus may benefit from positive health consequences. The present research compared entrepreneurs' health with employees' health in a national representative sample with regard to the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) diagnoses of somatic diseases, the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) diagnoses of mental disorders, blood pressure, well-being (life-satisfaction) as well as behavioural health indicators (sick days, physician visits). Entrepreneurs showed significantly lower overall somatic and mental morbidity, lower blood pressure, lower prevalence rates of hypertension, and somatoform disorders, as well as higher well-being and more favourable behavioural health indicators. The results are discussed with regard to the active job hypothesis and recommendations for future research are provided.