Lung function in idiopathic pulmonary fibrosis--extended analyses of the IFIGENIA trial.

BACKGROUND The randomized placebo-controlled IFIGENIA-trial demonstrated that therapy with high-dose N-acetylcysteine (NAC) given for one year, added to prednisone and azathioprine, significantly ameliorates (i.e. slows down) disease progression in terms of vital capacity (VC) (+9%) and diffusing capacity (DLco) (+24%) in idiopathic pulmonary fibrosis (IPF). To better understand the clinical implications of these findings we performed additional, explorative analyses of the IFGENIA data set. METHODS We analysed effects of NAC on VC, DLco, a composite physiologic index (CPI), and mortality in the 155 study-patients. RESULTS In trial completers the functional indices did not change significantly with NAC, whereas most indices deteriorated with placebo; in non-completers the majority of indices worsened but decline was generally less pronounced in most indices with NAC than with placebo. Most categorical analyses of VC, DLco and CPI also showed favourable changes with NAC. The effects of NAC on VC, DLco and CPI were significantly better if the baseline CPI was 50 points or lower. CONCLUSION This descriptive analysis confirms and extends the favourable effects of NAC on lung function in IPF and emphasizes the usefulness of VC, DLco, and the CPI for the evaluation of a therapeutic effect. Most importantly, less progressed disease as indicated by a CPI of 50 points or lower at baseline was more responsive to therapy in this study.

Homology modeling and metabolism prediction of human carboxylesterase-2 using docking analyses by GriDock: a parallelized tool based on AutoDock 4.0.

Metabolic problems lead to numerous failures during clinical trials, and much effort is now devoted to developing in silico models predicting metabolic stability and metabolites. Such models are well known for cytochromes P450 and some transferases, whereas less has been done to predict the activity of human hydrolases. The present study was undertaken to develop a computational approach able to predict the hydrolysis of novel esters by human carboxylesterase hCES2. The study involved first a homology modeling of the hCES2 protein based on the model of hCES1 since the two proteins share a high degree of homology (congruent with 73%). A set of 40 known substrates of hCES2 was taken from the literature; the ligands were docked in both their neutral and ionized forms using GriDock, a parallel tool based on the AutoDock4.0 engine which can perform efficient and easy virtual screening analyses of large molecular databases exploiting multi-core architectures. Useful statistical models (e.g., r (2) = 0.91 for substrates in their unprotonated state) were calculated by correlating experimental pK(m) values with distance between the carbon atom of the substrate's ester group and the hydroxy function of Ser228. Additional parameters in the equations accounted for hydrophobic and electrostatic interactions between substrates and contributing residues. The negatively charged residues in the hCES2 cavity explained the preference of the enzyme for neutral substrates and, more generally, suggested that ligands which interact too strongly by ionic bonds (e.g., ACE inhibitors) cannot be good CES2 substrates because they are trapped in the cavity in unproductive modes and behave as inhibitors. The effects of protonation on substrate recognition and the contrasting behavior of substrates and products were finally investigated by MD simulations of some CES2 complexes.

Specificity of immunoblotting analyses in eosinophilic meningitis

Angiostrongylus cantonensis and Gnathostoma spinigerum are the two most common causative parasites of eosinophilic meningitis (EOM). Serological tests are helpful tools for confirming the identity of the pathogen. Recent reports determined the specificity of such tests by using normal healthy controls. There have been limited studies done to rule out the cross-reactivity between these two causative parasites of EOM. This study aims to assess the specificity of the serological test in EOM by using each condition as a control for the other. Thirty-three patients with a diagnosis of EOM were enrolled. Sera from 22 patients with a positive 29-kDa antigenic diagnostic band of A. cantonensis were tested for the 21 and 24-kDa antigenic bands of G. spinigerum. Similarly, sera of 11 gnathostomiasis patients were tested for the 29-kDa diagnostic band for A. cantonensis. Only one patient in the angiostrongyliasis group had a positive result for the 21 and 24-kDa antigenic bands of G. spinigerum, while no gnathostomiasis patients showed a positive result for the 29-kDa antigenic band of A. cantonensis. The specificity of the 21 and 24-kDa antigenic bands for gnathostomiasis and the 29-kDa antigenic band for A. cantonensis was 95.5% and 100%, respectively. The antigenic bands for the diagnosis of gnathostomiasis and angiostrongyliasis in EOM were highly specific.

Longitudinal analysis of patterns and predictors of changes in self-reported adherence to antiretroviral therapy: Swiss HIV Cohort Study.

BACKGROUND: Adherence to combination antiretroviral therapy (cART) is a dynamic process, however, changes in adherence behavior over time are insufficiently understood. METHODS: Data on self-reported missed doses of cART was collected every 6 months in Swiss HIV Cohort Study participants. We identified behavioral groups associated with specific cART adherence patterns using trajectory analyses. Repeated measures logistic regression identified predictors of changes in adherence between consecutive visits. RESULTS: Six thousand seven hundred nine individuals completed 49,071 adherence questionnaires [median 8 (interquartile range: 5-10)] during a median follow-up time of 4.5 years (interquartile range: 2.4-5.1). Individuals were clustered into 4 adherence groups: good (51.8%), worsening (17.4%), improving (17.6%), and poor adherence (13.2%). Independent predictors of worsening adherence were younger age, basic education, loss of a roommate, starting intravenous drug use, increasing alcohol intake, depression, longer time with HIV, onset of lipodystrophy, and changing care provider. Independent predictors of improvements in adherence were regimen simplification, changing class of cART, less time on cART, and starting comedications. CONCLUSIONS: Treatment, behavioral changes, and life events influence patterns of drug intake in HIV patients. Clinical care providers should routinely monitor factors related to worsening adherence and intervene early to reduce the risk of treatment failure and drug resistance.

Structural and mean level analyses of the Five-Factor Model and Locus of Control: Further evidence from Africa

The present study examines the Five-Factor Model (FFM) of personality and locus of control in French-speaking samples in Burkina Faso (N = 470) and Switzerland (Ns = 1,090, 361), using the Revised NEO Personality Inventory (NEO-PI-R) and Levenson's Internality, Powerful others, and Chance (IPC) scales. Alpha reliabilities were consistently lower in Burkina Faso, but the factor structure of the NEO-PI-R was replicated in both cultures. The intended three-factor structure of the IPC could not be replicated, although a two-factor solution was replicable across the two samples. Although scalar equivalence has not been demonstrated, mean level comparisons showed the hypothesized effects for most of the five factors and locus of control; Burkinabè scored higher in Neuroticism than anticipated. Findings from this African sample generally replicate earlier results from Asian and Western cultures, and are consistent with a biologically-based theory of personality.

Natural abundance of stable isotopes (15N and 13C) and isotopic fractionation of sea bream muscle as markers of food and culture conditions and its relationship with metabolic and proteomic analyses

Els isòtops estables com a traçadors de la cadena alimentària, s'han utilitzat per caracteritzar la relació entre els consumidors i els seus aliments, ja que el fraccionament isotòpic implica una discriminació en contra de certs isòtops. Però les anàlisis d'isòtops estables (SIA), també es poden dur a terme en peixos cultivats amb dietes artificials, com la orada (Sparus aurata), la especie más cultivada en el Mediterráneo. Canvis en l'abundància natural d'isòtops estables (13C i 15N) en els teixits i les seves reserves poden reflectir els canvis en l'ús i reciclatge dels nutrients ja que els enzims catabòlics implicats en els processos de descarboxilació i desaminació mostren una preferència pels isòtops més lleugers. Per tant, aquestes anàlisis ens poden proporcionar informació útil sobre l'estat nutricional i metabòlic dels peixos. L'objectiu d'aquest projecte va ser determinar la capacitat dels isòtops estables per ser utilitzats com a marcadors potencials de la capacitat de creixement i condicions de cria de l'orada. En aquest sentit, les anàlisis d'isòtops estables s'han combinat amb altres metabòlics (activitats citocrom-c-oxidasa, COX, i citrat sintasa, CS) i els paràmetres de creixement (ARN/ADN). El conjunt de resultats obtinguts en els diferents estudis realitzats en aquest projecte demostra que el SIA, en combinació amb altres paràmetres metabòlics, pot servir com una eina eficaç per discriminar els peixos amb millor potencial de creixement, així com a marcador sensible de l'estat nutricional i d'engreix. D'altra banda, la combinació de l'anàlisi d'isòtops estables amb les eines emergents, com ara tècniques de proteòmica (2D-PAGE), ens proporciona nous coneixements sobre els canvis metabòlics que ocorren en els músculs dels peixos durant l‟increment del creixement muscular induït per l'exercici.

Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.

Imputation in data fusion of heterogeneous data sets a model-based numerical experiment

Given the very large amount of data obtained everyday through population surveys, much of the new research again could use this information instead of collecting new samples. Unfortunately, relevant data are often disseminated into different files obtained through different sampling designs. Data fusion is a set of methods used to combine information from different sources into a single dataset. In this article, we are interested in a specific problem: the fusion of two data files, one of which being quite small. We propose a model-based procedure combining a logistic regression with an Expectation-Maximization algorithm. Results show that despite the lack of data, this procedure can perform better than standard matching procedures.

Comparative analyses of classical phenotypic method and ribosomal RNA gene sequencing for identification of medically relevant Candida species

As the distribution of Candida species and their susceptibility to antifungal agents have changed, a new means of accurately and rapidly identifying these species is necessary for the successful early resolution of infection and the subsequent reduction of morbidity and mortality. The current work aimed to evaluate ribosomal RNA gene sequencing for the identification of medically relevant Candida species in comparison with a standard phenotypic method. Eighteen reference strains (RSs), 69 phenotypically identified isolates and 20 inconclusively identified isolates were examined. Internal transcribed spaces (ITSs) and D1/D2 of the 26S ribosomal RNA gene regions were used as targets for sequencing. Additionally, the sequences of the ITS regions were used to establish evolutionary relationships. The sequencing of the ITS regions was successful for 88% (94/107) of the RS and isolates, whereas 100% of the remaining 12% (13/107) of the samples were successfully analysed by sequencing the D1/D2 region. Similarly, genotypic analysis identified all of the RS and isolates, including the 20 isolates that were not phenotypically identified. Phenotypic analysis, however, misidentified 10% (7/69) of the isolates. Phylogenetic analysis allowed the confirmation of the relationships between evolutionarily close species. Currently, the use of genotypic methods is necessary for the correct identification of Candida species.

Inference about the number of contributors to a DNA mixture: Comparative analyses of a Bayesian network approach and the maximum allele count method.

In the forensic examination of DNA mixtures, the question of how to set the total number of contributors (N) presents a topic of ongoing interest. Part of the discussion gravitates around issues of bias, in particular when assessments of the number of contributors are not made prior to considering the genotypic configuration of potential donors. Further complication may stem from the observation that, in some cases, there may be numbers of contributors that are incompatible with the set of alleles seen in the profile of a mixed crime stain, given the genotype of a potential contributor. In such situations, procedures that take a single and fixed number contributors as their output can lead to inferential impasses. Assessing the number of contributors within a probabilistic framework can help avoiding such complication. Using elements of decision theory, this paper analyses two strategies for inference on the number of contributors. One procedure is deterministic and focuses on the minimum number of contributors required to 'explain' an observed set of alleles. The other procedure is probabilistic using Bayes' theorem and provides a probability distribution for a set of numbers of contributors, based on the set of observed alleles as well as their respective rates of occurrence. The discussion concentrates on mixed stains of varying quality (i.e., different numbers of loci for which genotyping information is available). A so-called qualitative interpretation is pursued since quantitative information such as peak area and height data are not taken into account. The competing procedures are compared using a standard scoring rule that penalizes the degree of divergence between a given agreed value for N, that is the number of contributors, and the actual value taken by N. Using only modest assumptions and a discussion with reference to a casework example, this paper reports on analyses using simulation techniques and graphical models (i.e., Bayesian networks) to point out that setting the number of contributors to a mixed crime stain in probabilistic terms is, for the conditions assumed in this study, preferable to a decision policy that uses categoric assumptions about N.