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Background and Objectives: Cycloid psychoses are characterized by polymorphic symptomatology with intraphasic bipolarity, a remitting and recurrent course and favourable prognosis. Perris and Brocicington (P&B) described the first set of operational criteria that were partly incorporated in ICD-10. The present study investigates psychopathological profiles according to the P&B criteria and the original descriptions by Leonhard, both against the background of the criteria from the prevailing international classification systems.
Methods: Eighty patients with psychotic disorders were recruited and assessed with various psychometric instruments at baseline and after six weeks of antipsychotic treatment in order to investigate the presence of cycloid psychoses according to Leonhard (LCP) and the effect of treatment with antipsychotics. The overlap between LCP and DSM-IV Brief Psychotic Disorder (BPD), ICD Acute Polymorphic Psychotic Disorder (APP) and P&B criteria was calculated.
Results: Using P&B criteria and a symptom checklist adapted from the original descriptions by Leonhard, 14 and 12 cases of cycloid psychosis were identified respectively reflecting a prevalence of 15-18%. Small though significant concordance rates were found between LCP and both DSM-BPD and ICD-APP. Concordance between LCP and P&B criteria was also significant, but modest.
Conclusions: This study demonstrates that LCP can be identified in a substantial number of patients with psychotic disorders. Cycloid psychoses are not adequately covered in current classification systems and criteria. Since they are demonstrated to have a specific psychopathological profile, relapsing course and favourable prognosis, it is advocated to include these psychoses in daily differential diagnostic procedures.
Primary involvement of skeletal muscle is a very rare event in ALK-1 positive anaplastic large cell lymphoma (ALCL). We describe a case of a 10-year old boy presenting with a three week history of pain and a palpable firm swelling at the dorsal aspect of the left thigh. Histological examination of the lesion revealed a tumoral and diffuse polymorphic infiltration of the muscle by large lymphoid cells. Tumor cells displayed eccentric, lobulated "horse shoe" or "kidney-shape" nuclei. The cells showed immunohistochemical positivity for CD30, ALK-1, CD2, CD3, CD7, CD8, and Perforin. Fluorescence in situ hybridization analysis revealed a characteristic rearrangement of the ALK-1 gene in 2p23 leading to the diagnosis of ALK-1 positive ALCL. Chemotherapy according to the ALCL-99-NHL-BFM protocol was initiated and resulted in a complete remission after two cycles. This case illustrates the unusual presentation of a pediatric ALCL in soft tissue with a good response to chemotherapy.
Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.