Use of NMR metabolomic plasma profiling methodologies to identify illicit growth-promoting administrations

Detection of growth-promoter use in animal production systems still proves to be an analytical challenge despite years of activity in the field. This study reports on the capability of NMR metabolomic profiling techniques to discriminate between plasma samples obtained from cattle treated with different groups of growth-promoting hormones (dexamethasone, prednisolone, oestradiol) based on recorded metabolite profiles. Two methods of NMR analysis were investigated—a Carr–Purcell–Meiboom–Gill (CPMG)-pulse sequence technique and a conventional 1H NMR method using pre-extracted plasma. Using the CPMG method, 17 distinct metabolites could be identified from the spectra. 1H NMR analysis of extracted plasma facilitated identification of 23 metabolites—six more than the alternative method and all within the aromatic region. Multivariate statistical analysis of acquired data from both forms of NMR analysis separated the plasma metabolite profiles into distinct sample cluster sets representative of the different animal study groups. Samples from both sets of corticosteroid-treated animals—dexamethasone and prednisolone—were found to be clustered relatively closely and had similar alterations to identified metabolite panels. Distinctive metabolite profiles, different from those observed within plasma from corticosteroid-treated animal plasma, were observed in oestradiol-treated animals and samples from these animals formed a cluster spatially isolated from control animal plasma samples. These findings suggest the potential use of NMR methodologies of plasma metabolite analysis as a high-throughput screening technique to aid detection of growth promoter use.

Exploring statistical and population aspects of network complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples.

Statistical-mechanical description of classical test-particle dynamics in the presence of an external force field: modelling noise and damping from first principles

Aiming to establish a rigorous link between macroscopic random motion (described e.g. by Langevin-type theories) and microscopic dynamics, we have undertaken a kinetic-theoretical study of the dynamics of a classical test-particle weakly coupled to a large heat-bath in thermal equilibrium. Both subsystems are subject to an external force field. From the (time-non-local) generalized master equation a Fokker-Planck-type equation follows as a "quasi-Markovian" approximation. The kinetic operator thus defined is shown to be ill-defined; in specific, it does not preserve the positivity of the test-particle distribution function f(x, v; t). Adopting an alternative approach, previously introduced for quantum open systems, is proposed to lead to a correct kinetic operator, which yields all the expected properties. A set of explicit expressions for the diffusion and drift coefficients are obtained, allowing for modelling macroscopic diffusion and dynamical friction phenomena, in terms of an external field and intrinsic physical parameters.

Handwritten undergraduate case reports

Background: This article describes a 'back to the future' approach to case 'write-ups', with medical students producing handwritten instead of word-processed case reports during their clinical placements. Word-processed reports had been found to have a number of drawbacks, including the inappropriate use of 'cutting and pasting', undue length and lack of focus. Method: We developed a template to be completed by hand, based on the hospital 'clerking-in process', and matched this to a new assessment proforma. An electronic survey was conducted of both students and assessors after the first year of operation to evaluate impact and utility. Results: The new template was well received by both students and assessors. Most students said they preferred handwriting the case reports (55.6%), although a significant proportion (44.4%) preferred the word processor. Many commented that the template enabled them to effectively learn the structure of a case history and to improve their history-taking skills. Most assessors who had previously marked case reports felt the new system represented an improvement. The average time spent marking each report fell from 23.56 to 16.38minutes using the new proforma. Discussion: Free text comments from the survey have led to the development of a more flexible case report template better suited to certain specialties (e.g. dermatology). This is an evolving process and there will be opportunities for further adaptation as electronic medical records become more common in hospital. © Blackwell Publishing Ltd 2012.

Ensuring the statistical soundness of competitive gene set approaches: gene filtering and genome-scale coverage are essential

In this article, we focus on the analysis of competitive gene set methods for detecting the statistical significance of pathways from gene expression data. Our main result is to demonstrate that some of the most frequently used gene set methods, GSEA, GSEArot and GAGE, are severely influenced by the filtering of the data in a way that such an analysis is no longer reconcilable with the principles of statistical inference, rendering the obtained results in the worst case inexpressive. A possible consequence of this is that these methods can increase their power by the addition of unrelated data and noise. Our results are obtained within a bootstrapping framework that allows a rigorous assessment of the robustness of results and enables power estimates. Our results indicate that when using competitive gene set methods, it is imperative to apply a stringent gene filtering criterion. However, even when genes are filtered appropriately, for gene expression data from chips that do not provide a genome-scale coverage of the expression values of all mRNAs, this is not enough for GSEA, GSEArot and GAGE to ensure the statistical soundness of the applied procedure. For this reason, for biomedical and clinical studies, we strongly advice not to use GSEA, GSEArot and GAGE for such data sets.

Enquiring about traumatic experiences in bipolar disorder: A case note and self- report comparison

Objectives: This study examined: (i) the prevalence of lifetime trauma, childhood trauma and trauma related to civil unrest in a Bipolar Disorder sample, and (ii) the agreement between rates of disclosure of trauma in case notes and self-report questionnaires.

Methods: The case notes of sixty participants, recruited from a geographically well-defined mental health service in Northern Ireland, were examined for reports of experiences of lifetime, childhood and traumatic events related to civil conflict. The participants also completed self-report measures of trauma.

Results: Considerable differences were found between the prevalence of trauma as measured by self-report questionnaires and case notes reports. The prevalence of lifetime trauma as measured by the Trauma History Questionnaire was 61.7% (compared to case notes prevalence of 33.3%). The prevalence of moderate and severe levels of childhood trauma as measured by the Childhood Trauma Questionnaire was 65% (case notes 21.7%). Rates of trauma related to civil unrest were 35% (case notes 3.3%). Poor levels of agreement were found between all self-report trauma measures and case notes reports. Agreement on two categories of trauma (childhood emotional neglect and childhood physical neglect) reached statistical significance but kappa scores suggest this agreement was poor (kappa = .14. p<.05; kappa = .127, p<.05). © 2011 Elsevier B.V. All rights reserved.

Conclusions: It is probable that the increased rate of trauma disclosed in the self-report questionnaire arises because clinicians during initial assessment and subsequent treatment do not consistently enquire about trauma. The need for staff training is discussed. (C) 2011 Elsevier B.V. All rights reserved.