Levo-α-acetylmethadol (LAAM) induced QTc-prolongation - results from a controlled clinical trial

<p>Abstract</p> <p>Background</p> <p>Due to potential proarrhythmic side-effects levo-α-Acetylmethadol (LAAM) is currently not available in EU countries as maintenance drug in the treatment of opiate addiction. However, recent studies and meta-analyses underline the clinical advantages of LAAM with respect to the reduction of heroin use. Thus a reappraisal of LAAM has been demanded. The aim of the present study was to evaluate the relative impact of LAAM on QTc-interval, as a measure of pro-arrhythmic risk, in comparison to methadone, the current standard in substitution therapy.</p> <p>Methods</p> <p>ECG recordings were analysed within a randomized, controlled clinical trial evaluating the efficacy and tolerability of maintenance treatment with LAAM compared with racemic methadone. Recordings were done at two points: 1) during a run-in period with all patients on methadone and 2) 24 weeks after randomisation into methadone or LAAM treatment group. These ECG recordings were analysed with respect to QTc-values and QTc-dispersion. Mean values as well as individual changes compared to baseline parameters were evaluated. QTc-intervals were classified according to CPMP-guidelines.</p> <p>Results</p> <p>Complete ECG data sets could be obtained in 53 patients (31 LAAM-group, 22 methadone-group). No clinical cardiac complications were observed in either group. After 24 weeks, patients receiving LAAM showed a significant increase in QTc-interval (0.409 s ± 0.022 s versus 0.418 s ± 0.028 s, p = 0.046), whereas no significant changes could be observed in patients remaining on methadone. There was no statistically significant change in QTc-dispersion in either group. More patients with borderline prolonged and prolonged QTc-intervals were observed in the LAAM than in the methadone treatment group (n = 7 vs. n = 1; p = 0.1).</p> <p>Conclusions</p> <p>In this controlled trial LAAM induced QTc-prolongation in a higher degree than methadone. Given reports of severe arrhythmic events, careful ECG-monitoring is recommended under LAAM medication.</p

Supramolecular organizations in the aerobic respiratory chain of Escherichia coli

The organization of respiratory chain complexes in supercomplexes has been shown in the mitochondria of several eukaryotes and in the cell membranes of some bacteria. These supercomplexes are suggested to be important for oxidative phosphorylation efficiency and to prevent the formation of reactive oxygen species. Here we describe, for the first time, the identification of supramolecular organizations in the aerobic respiratory chain of Escherichia coli, including a trimer of succinate dehydrogenase. Furthermore, two heterooligomerizations have been shown: one resulting from the association of the NADH:quinone oxidoreductases NDH-1 and NDH-2, and another composed by the cytochrome bo3 quinol:oxygen reductase, cytochrome bd quinol:oxygen reductase and formate dehydrogenase (fdo). These results are supported by blue native-electrophoresis, mass spectrometry and kinetic data of wild type and mutant E . coli strains

The Use of Urine Proteomic and Metabonomic Patterns for the Diagnosis of Interstitial Cystitis and Bacterial Cystitis

The advent of systems biology approaches that have stemmed from the sequencing of the human genome has led to the search for new methods to diagnose diseases. While much effort has been focused on the identification of disease-specific biomarkers, recent efforts are underway toward the use of proteomic and metabonomic patterns to indicate disease. We have developed and contrasted the use of both proteomic and metabonomic patterns in urine for the detection of interstitial cystitis (IC). The methodology relies on advanced bioinformatics to scrutinize information contained within mass spectrometry (MS) and high-resolution proton nuclear magnetic resonance (1H-NMR) spectral patterns to distinguish IC-affected from non-affected individuals as well as those suffering from bacterial cystitis (BC). We have applied a novel pattern recognition tool that employs an unsupervised system (self-organizing-type cluster mapping) as a fitness test for a supervised system (a genetic algorithm). With this approach, a training set comprised of mass spectra and 1H-NMR spectra from urine derived from either unaffected individuals or patients with IC is employed so that the most fit combination of relative, normalized intensity features defined at precise m/z or chemical shift values plotted in n-space can reliably distinguish the cohorts used in training. Using this bioinformatic approach, we were able to discriminate spectral patterns associated with IC-affected, BC-affected, and unaffected patients with a success rate of approximately 84%

On positivity of Ehrhart polynomials

Ehrhart discovered that the function that counts the number of lattice points in dilations of an integral polytope is a polynomial. We call the coefficients of this polynomial Ehrhart coefficients, and say a polytope is Ehrhart positive if all Ehrhart coefficients are positive (which is not true for all integral polytopes). The main purpose of this article is to survey interesting families of polytopes that are known to be Ehrhart positive and discuss the reasons from which their Ehrhart positivity follows. We also include examples of polytopes that have negative Ehrhart coefficients and polytopes that are conjectured to be Ehrhart positive, as well as pose a few relevant questions.Comment: 40 pages, 7 figures. To appear in in Recent Trends in Algebraic Combinatorics, a volume of the Association for Women in Mathematics Series, Springer International Publishin

The APEX Quantitative Proteomics Tool: Generating protein quantitation estimates from LC-MS/MS proteomics results

Mass spectrometry (MS) based label-free protein quantitation has mainly focused on analysis of ion peak heights and peptide spectral counts. Most analyses of tandem mass spectrometry (MS/MS) data begin with an enzymatic digestion of a complex protein mixture to generate smaller peptides that can be separated and identified by an MS/MS instrument. Peptide spectral counting techniques attempt to quantify protein abundance by counting the number of detected tryptic peptides and their corresponding MS spectra. However, spectral counting is confounded by the fact that peptide physicochemical properties severely affect MS detection resulting in each peptide having a different detection probability. Lu et al. (2007) described a modified spectral counting technique, Absolute Protein Expression (APEX), which improves on basic spectral counting methods by including a correction factor for each protein (called O(i) value) that accounts for variable peptide detection by MS techniques. The technique uses machine learning classification to derive peptide detection probabilities that are used to predict the number of tryptic peptides expected to be detected for one molecule of a particular protein (O(i)). This predicted spectral count is compared to the protein's observed MS total spectral count during APEX computation of protein abundances. Results: The APEX Quantitative Proteomics Tool, introduced here, is a free open source Java application that supports the APEX protein quantitation technique. The APEX tool uses data from standard tandem mass spectrometry proteomics experiments and provides computational support for APEX protein abundance quantitation through a set of graphical user interfaces that partition thparameter controls for the various processing tasks. The tool also provides a Z-score analysis for identification of significant differential protein expression, a utility to assess APEX classifier performance via cross validation, and a utility to merge multiple APEX results into a standardized format in preparation for further statistical analysis. Conclusion: The APEX Quantitative Proteomics Tool provides a simple means to quickly derive hundreds to thousands of protein abundance values from standard liquid chromatography-tandem mass spectrometry proteomics datasets. The APEX tool provides a straightforward intuitive interface design overlaying a highly customizable computational workflow to produce protein abundance values from LC-MS/MS datasets.National Institute of Allergy and Infectious Diseases (NIAID) N01-AI15447National Institutes of HealthNational Science Foundation, the Welsh and Packard FoundationsInternational Human Frontier Science ProgramCenter for Systems and Synthetic Biolog

Recipes for sparse LDA of horizontal data

Many important modern applications require analyzing data with more variables than observations, called for short horizontal. In such situation the classical Fisher’s linear discriminant analysis (LDA) does not possess solution because the within-group scatter matrix is singular. Moreover, the number of the variables is usually huge and the classical type of solutions (discriminant functions) are difficult to interpret as they involve all available variables. Nowadays, the aim is to develop fast and reliable algorithms for sparse LDA of horizontal data. The resulting discriminant functions depend on very few original variables, which facilitates their interpretation. The main theoretical and numerical challenge is how to cope with the singularity of the within-group scatter matrix. This work aims at classifying the existing approaches according to the way they tackle this singularity issue, and suggest new ones

Tannerella forsythia, a periodontal pathogen entering the genomic era

Several questions need to be addressed to evaluate whether Tannerella forsythia is to be considered a periodontal pathogen. T. forsythia has been detected in periodontal health and disease, so could it be a pathogen? The species was not detected in many studies despite finding other putative pathogens, so could it be important in pathogenicity? The challenges of working with T. forsythia include its fastidious and anaerobic growth requirements for cultural detection. Thus, studies associating T. forsythia with periodontal and other oral infections have used noncultural approaches (immunoassays and DNA-based assays) in addition to cultural approaches. We feel the timing of this review represents an interesting transition period in our understanding of the relationships of species with infection. Information from the recently released full genome sequence data of T. forsythia will provide new approaches and tools that can be directed to assess pathogenicity. Furthermore, molecular assessment of gene expression will provide a new understanding of the pathogenical potential of the species, and its effect on the host. T. forsythia, was described in reviews focusing on periodontal pathogens associated with herpesvirus detection (200), species for which genome projects were underway (41), members of polybacterial periodontal pathogenic consortium (91), and participants in periodontal microbial ecology (202). We will describe the history, taxonomy, and characteristics of T. forsythia, and related species or phylotypes in the genus Tannerella. To assess the pathogenic potential of T. forsythia, we first describe species associations with periodontal and other infections, including animal models, as has been the traditional approach arising from Koch’s postulates (203). Criteria for pathogenicity were expanded to incorporate sequence- derived information (58), and again more recently to include molecular signatures of pathogens and disease (170). We used sequence and genome-derived information, in addition to biofilm, pathogenic mediators, and host responses, to further explore the pathogenic potential of T. forsythia

An Ultra-Fast Metabolite Prediction Algorithm

Small molecules are central to all biological processes and metabolomics becoming an increasingly important discovery tool. Robust, accurate and efficient experimental approaches are critical to supporting and validating predictions from post-genomic studies. To accurately predict metabolic changes and dynamics, experimental design requires multiple biological replicates and usually multiple treatments. Mass spectra from each run are processed and metabolite features are extracted. Because of machine resolution and variation in replicates, one metabolite may have different implementations (values) of retention time and mass in different spectra. A major impediment to effectively utilizing untargeted metabolomics data is ensuring accurate spectral alignment, enabling precise recognition of features (metabolites) across spectra. Existing alignment algorithms use either a global merge strategy or a local merge strategy. The former delivers an accurate alignment, but lacks efficiency. The latter is fast, but often inaccurate. Here we document a new algorithm employing a technique known as quicksort. The results on both simulated data and real data show that this algorithm provides a dramatic increase in alignment speed and also improves alignment accuracy

Estimation of Relevant Variables on High-Dimensional Biological Patterns Using Iterated Weighted Kernel Functions

BACKGROUND The analysis of complex proteomic and genomic profiles involves the identification of significant markers within a set of hundreds or even thousands of variables that represent a high-dimensional problem space. The occurrence of noise, redundancy or combinatorial interactions in the profile makes the selection of relevant variables harder. METHODOLOGY/PRINCIPAL FINDINGS Here we propose a method to select variables based on estimated relevance to hidden patterns. Our method combines a weighted-kernel discriminant with an iterative stochastic probability estimation algorithm to discover the relevance distribution over the set of variables. We verified the ability of our method to select predefined relevant variables in synthetic proteome-like data and then assessed its performance on biological high-dimensional problems. Experiments were run on serum proteomic datasets of infectious diseases. The resulting variable subsets achieved classification accuracies of 99% on Human African Trypanosomiasis, 91% on Tuberculosis, and 91% on Malaria serum proteomic profiles with fewer than 20% of variables selected. Our method scaled-up to dimensionalities of much higher orders of magnitude as shown with gene expression microarray datasets in which we obtained classification accuracies close to 90% with fewer than 1% of the total number of variables. CONCLUSIONS Our method consistently found relevant variables attaining high classification accuracies across synthetic and biological datasets. Notably, it yielded very compact subsets compared to the original number of variables, which should simplify downstream biological experimentation