Diagnostic performance of line-immunoassay based algorithms for incident HIV-1 infection

Background: Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have previously demonstrated that a patient's antibody reaction pattern in a confirmatory line immunoassay (INNO-LIA™ HIV I/II Score) provides information on the duration of infection, which is unaffected by clinical, immunological and viral variables. In this report we have set out to determine the diagnostic performance of Inno-Lia algorithms for identifying incident infections in patients with known duration of infection and evaluated the algorithms in annual cohorts of HIV notifications. Methods: Diagnostic sensitivity was determined in 527 treatment-naive patients infected for up to 12 months. Specificity was determined in 740 patients infected for longer than 12 months. Plasma was tested by Inno-Lia and classified as either incident (< = 12 m) or older infection by 26 different algorithms. Incident infection rates (IIR) were calculated based on diagnostic sensitivity and specificity of each algorithm and the rule that the total of incident results is the sum of true-incident and false-incident results, which can be calculated by means of the pre-determined sensitivity and specificity. Results: The 10 best algorithms had a mean raw sensitivity of 59.4% and a mean specificity of 95.1%. Adjustment for overrepresentation of patients in the first quarter year of infection further reduced the sensitivity. In the preferred model, the mean adjusted sensitivity was 37.4%. Application of the 10 best algorithms to four annual cohorts of HIV-1 notifications totalling 2'595 patients yielded a mean IIR of 0.35 in 2005/6 (baseline) and of 0.45, 0.42 and 0.35 in 2008, 2009 and 2010, respectively. The increase between baseline and 2008 and the ensuing decreases were highly significant. Other adjustment models yielded different absolute IIR, although the relative changes between the cohorts were identical for all models Conclusions: The method can be used for comparing IIR in annual cohorts of HIV notifications. The use of several different algorithms in combination, each with its own sensitivity and specificity to detect incident infection, is advisable as this reduces the impact of individual imperfections stemming primarily from relatively low sensitivities and sampling bias

Women's Preferences for Treatment of Perinatal Depression and Anxiety : A Discrete Choice Experiment

Perinatal depression and anxiety (PNDA) are an international healthcare priority, associated with significant short- and long-term problems for women, their children and families. Effective treatment is available but uptake is suboptimal: some women go untreated whilst others choose treatments without strong evidence of efficacy. Better understanding of women's preferences for treatment is needed to facilitate uptake of effective treatment. To address this issue, a discrete choice experiment (DCE) was administered to 217 pregnant or postnatal women in Australia, who were recruited through an online research company and had similar sociodemographic characteristics to Australian data for perinatal women. The DCE investigated preferences regarding cost, treatment type, availability of childcare, modality and efficacy. Data were analysed using logit-based models accounting for preference and scale heterogeneity. Predicted probability analysis was used to explore relative attribute importance and policy change scenarios, including how these differed by women's sociodemographic characteristics. Cost and treatment type had the greatest impact on choice, such that a policy of subsidising effective treatments was predicted to double their uptake compared with the base case. There were differences in predicted uptake associated with certain sociodemographic characteristics: for example, women with higher educational attainment were more likely to choose effective treatment. The findings suggest policy directions for decision makers whose goal is to reduce the burden of PNDA on women, their children and families

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

We provide a user guide on the analysis of data (including best–worst and best–best data) generated from discrete-choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post-estimation. We also provide a review of standard software. In providing this guide, we endeavour to not only provide guidance on choice modelling but to do so in a way that provides a ‘way in’ for researchers to the practicalities of data analysis. We argue that choice of modelling approach depends on the research questions, study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful to researchers not only within but also beyond health economics

Recapitulation of tumor heterogeneity and molecular signatures in a 3D brain cancer model with decreased sensitivity to histone deacetylase inhibition

INTRODUCTION Physiologically relevant pre-clinical ex vivo models recapitulating CNS tumor micro-environmental complexity will aid development of biologically-targeted agents. We present comprehensive characterization of tumor aggregates generated using the 3D Rotary Cell Culture System (RCCS). METHODS CNS cancer cell lines were grown in conventional 2D cultures and the RCCS and comparison with a cohort of 53 pediatric high grade gliomas conducted by genome wide gene expression and microRNA arrays, coupled with immunohistochemistry, ex vivo magnetic resonance spectroscopy and drug sensitivity evaluation using the histone deacetylase inhibitor, Vorinostat. RESULTS Macroscopic RCCS aggregates recapitulated the heterogeneous morphology of brain tumors with a distinct proliferating rim, necrotic core and oxygen tension gradient. Gene expression and microRNA analyses revealed significant differences with 3D expression intermediate to 2D cultures and primary brain tumors. Metabolic profiling revealed differential profiles, with an increase in tumor specific metabolites in 3D. To evaluate the potential of the RCCS as a drug testing tool, we determined the efficacy of Vorinostat against aggregates of U87 and KNS42 glioblastoma cells. Both lines demonstrated markedly reduced sensitivity when assaying in 3D culture conditions compared to classical 2D drug screen approaches. CONCLUSIONS Our comprehensive characterization demonstrates that 3D RCCS culture of high grade brain tumor cells has profound effects on the genetic, epigenetic and metabolic profiles of cultured cells, with these cells residing as an intermediate phenotype between that of 2D cultures and primary tumors. There is a discrepancy between 2D culture and tumor molecular profiles, and RCCS partially re-capitulates tissue specific features, allowing drug testing in a more relevant ex vivo system

Novel facultative Methylocella strains are active methane consumers at terrestrial natural gas seeps

Natural gas seeps contribute to global climate change by releasing substantial amounts of the potent greenhouse gas methane and other climate-active gases including ethane and propane to the atmosphere. However, methanotrophs, bacteria capable of utilising methane as the sole source of carbon and energy, play a significant role in reducing the emissions of methane from many environments. Methylocella-like facultative methanotrophs are a unique group of bacteria that grow on other components of natural gas (i.e. ethane and propane) in addition to methane but a little is known about the distribution and activity of Methylocella in the environment. The purposes of this study were to identify bacteria involved in cycling methane emitted from natural gas seeps and, most importantly, to investigate if Methylocella-like facultative methanotrophs were active utilisers of natural gas at seep sites

The concept of a random coil - Residual structure in peptides and denatured proteins

Non-native states of proteins are of increasing interest because of their relevance to issues such as protein folding, translocation and stability. A framework for interpreting the wealth of experimental data for non-native states emerging from rapid advances in experimental techniques involves comparison with a "random coll' state, which possesses no structure except that inherent in the local interactions. We review here the concept of a random coil, from its global to its local properties. In particular, we focus on the description of a random coil in terms of statistical distributions in psi, phi space. We show that such a model, in combination with experimental data, provides insight into the structural properties of polypeptide chains and has significance for understanding protein folding and for molecular design

Toward a description of the conformations of denatured states of proteins. Comparison of a random coil model with NMR measurements

A strategy is proposed to describe the backbone conformations sampled in denatured states of proteins. Main chain dihedral angle distributions are extracted from the protein data base and used to predict NMR parameters such as coupling constants and NOE intensities. A simple model in which each residue samples its φ,ψ distribution noncooperatively has been found to reproduce many of the features of experimental NMR data for hen egg-white lysozyme denatured in 8 M urea at low pH. This model provides a framework which allows identification of residual structure inherent in experimental data of nonnative states of proteins. The effects of introducing local conformational cooperativity on NMR parameters are discussed and analyzed in light of the experimental data for lysozyme. © 1996 American Chemical Society

Structural and dynamical properties of a denatured protein. Heteronuclear 3D NMR experiments and theoretical simulations of lysozyme in 8 M urea.

Oxidized and reduced hen lysozyme denatured in 8 M urea at low pH have been studied in detail by NMR methods. 15N correlated NOESY and TOCSY experiments have provided near complete sequential assignment for both 1H and 15N resonances. Over 900 NOEs, including 130 (i, i + 2) and 23 (i, i + 3) NOEs, could be identified by analysis of the NOESY spectra of the denatured states, and 3J(HN, Halpha) coupling constants and 15N relaxation rates have been measured. The coupling constant and NOE data were analyzed by comparisons with theoretical predictions from a random coil polypeptide model based on amino acid specific phi,psi distributions extracted from the protein data bank. There is significant agreement between predicted and experimental NMR parameters suggesting that local conformations of the denatured states are largely determined by short-range interactions within the polypeptide chain. This result is supported by the observation that the chemical shift, coupling constant, and NOE data are little affected by whether or not the four disulfide bridge cross-links are formed in the denatured protein. The relaxation data, however, show significant differences between the oxidized and reduced protein. Analysis of the relaxation data in terms of simple dynamics models provides evidence for weak clustering of hydrophobic groups near tryptophan residues and increased barriers to motion in the more compact conformers formed when the polypeptide chain is cross-linked by the disulfide bridges. Using this information, a structural description of these denatured states is given in terms of an ensemble of conformers, which have a complex relationship between their local and global characteristics