Human rhinovirus infection in young African children with acute wheezing

<p>Abstract</p> <p>Background</p> <p>Infections caused by human rhinoviruses (HRVs) are important triggers of wheezing in young children. Wheezy illness has increasingly been recognised as an important cause of morbidity in African children, but there is little information on the contribution of HRV to this. The aim of this study was to determine the role of HRV as a cause of acute wheezing in South African children.</p> <p>Methods</p> <p>Two hundred and twenty children presenting consecutively at a tertiary children's hospital with a wheezing illness from May 2004 to November 2005 were prospectively enrolled. A nasal swab was taken and reverse transcription PCR used to screen the samples for HRV. The presence of human metapneumovirus, human bocavirus and human coronavirus-NL63 was assessed in all samples using PCR-based assays. A general shell vial culture using a pool of monoclonal antibodies was used to detect other common respiratory viruses on 26% of samples. Phylogenetic analysis to determine circulating HRV species was performed on a portion of HRV-positive samples. Categorical characteristics were analysed using Fisher's Exact test.</p> <p>Results</p> <p>HRV was detected in 128 (58.2%) of children, most (72%) of whom were under 2 years of age. Presenting symptoms between the HRV-positive and negative groups were similar. Most illness was managed with ambulatory therapy, but 45 (35%) were hospitalized for treatment and 3 (2%) were admitted to intensive care. There were no in-hospital deaths. All 3 species of HRV were detected with HRV-C being the most common (52%) followed by HRV-A (37%) and HRV-B (11%). Infection with other respiratory viruses occurred in 20/128 (16%) of HRV-positive children and in 26/92 (28%) of HRV-negative samples.</p> <p>Conclusion</p> <p>HRV may be the commonest viral infection in young South African children with acute wheezing. Infection is associated with mild or moderate clinical disease.</p

Construct development: The Suicide Trigger Scale (STS-2), a measure of a hypothesized suicide trigger state

This study aims to develop the construct of a 'suicide trigger state' by exploring data gathered with a novel psychometric self-report instrument, the STS-2. The STS-2, was administered to 141 adult psychiatric patients with suicidal ideation. Multiple statistical methods were used to explore construct validity and structure. Cronbach's alpha (0.949) demonstrated excellent internal consistency. Factor analyses yielded two-component solutions with good agreement. The first component described near-psychotic somatization and ruminative flooding, while the second described frantic hopelessness. ROC analysis determined an optimal cut score for a history of suicide attempt, with significance of p < 0.03. Logistic regression analysis found items sensitive to history of suicide attempt described ruminative flooding, doom, hopelessness, entrapment and dread. The STS-2 appears to measure a distinct and novel clinical entity, which we speculatively term the 'suicide trigger state.' High scores on the STS-2 associate with reported history of past suicide attempt

How Many Separable Sources? Model Selection In Independent Components Analysis

Unlike mixtures consisting solely of non-Gaussian sources, mixtures including two or more Gaussian components cannot be separated using standard independent components analysis methods that are based on higher order statistics and independent observations. The mixed Independent Components Analysis/Principal Components Analysis (mixed ICA/PCA) model described here accommodates one or more Gaussian components in the independent components analysis model and uses principal components analysis to characterize contributions from this inseparable Gaussian subspace. Information theory can then be used to select from among potential model categories with differing numbers of Gaussian components. Based on simulation studies, the assumptions and approximations underlying the Akaike Information Criterion do not hold in this setting, even with a very large number of observations. Cross-validation is a suitable, though computationally intensive alternative for model selection. Application of the algorithm is illustrated using Fisher’s iris data set and Howells’ craniometric data set. Mixed ICA/PCA is of potential interest in any field of scientific investigation where the authenticity of blindly separated non-Gaussian sources might otherwise be questionable. Failure of the Akaike Information Criterion in model selection also has relevance in traditional independent components analysis where all sources are assumed non-Gaussian