Online genetic databases informing human genome epidemiology.

BACKGROUND: With the advent of high throughput genotyping technology and the information available via projects such as the human genome sequencing and the HapMap project, more and more data relevant to the study of genetics and disease risk will be produced. Systematic reviews and meta-analyses of human genome epidemiology studies rely on the ability to identify relevant studies and to obtain suitable data from these studies. A first port of call for most such reviews is a search of MEDLINE. We examined whether this could be usefully supplemented by identifying databases on the World Wide Web that contain genetic epidemiological information. METHODS: We conducted a systematic search for online databases containing genetic epidemiological information on gene prevalence or gene-disease association. In those containing information on genetic association studies, we examined what additional information could be obtained to supplement a MEDLINE literature search. RESULTS: We identified 111 databases containing prevalence data, 67 databases specific to a single gene and only 13 that contained information on gene-disease associations. Most of the latter 13 databases were linked to MEDLINE, although five contained information that may not be available from other sources. CONCLUSION: There is no single resource of structured data from genetic association studies covering multiple diseases, and in relation to the number of studies being conducted there is very little information specific to gene-disease association studies currently available on the World Wide Web. Until comprehensive data repositories are created and utilized regularly, new data will remain largely inaccessible to many systematic review authors and meta-analysts

Meta-analysis of continuous outcomes: using pseudo IPD created from aggregate data to adjust for baseline imbalance and assess treatment-by-baseline modification.

Meta-analysis of individual participant data (IPD) is considered the "gold-standard" for synthesizing clinical study evidence. However, gaining access to IPD can be a laborious task (if possible at all) and in practice only summary (aggregate) data are commonly available. In this work we focus on meta-analytic approaches of comparative studies where aggregate data are available for continuous outcomes measured at baseline (pre-treatment) and follow-up (post-treatment). We propose a method for constructing pseudo individual baselines and outcomes based on the aggregate data. These pseudo IPD can be subsequently analysed using standard analysis of covariance (ANCOVA) methods. Pseudo IPD for continuous outcomes reported at two timepoints can be generated using the sufficient statistics of an ANCOVA model i.e., the mean and standard deviation at baseline and follow-up per group, together with the correlation of the baseline and follow-up measurements. Applying the ANCOVA approach, which crucially adjusts for baseline imbalances and accounts for the correlation between baseline and change scores, to the pseudo IPD results in identical estimates to the ones obtained by an ANCOVA on the true IPD. In addition, an interaction term between baseline and treatment effect can be added. There are several modelling options available under this approach, which makes it very flexible. Methods are exemplified using reported data of a previously published IPD metaanalysis of 10 trials investigating the effect of antihypertensive treatments on systolic blood pressure, leading to identical results compared with the true IPD analysis and of a meta-analysis of fewer trials, where baseline imbalance occurred. This article is protected by copyright. All rights reserved

The Role of SurA PPIase Domains in Preventing Aggregation of the Outer Membrane Proteins tOmpA and OmpT

SurA is a conserved ATP-independent periplasmic chaperone involved in the biogenesis of outer-membrane proteins (OMPs). Escherichia coli SurA has a core domain and two peptidylprolyl isomerase (PPIase) domains, the role(s) of which remain unresolved. Here we show that while SurA homologues in early proteobacteria typically contain one or no PPIase domains, the presence of two PPIase domains is common in SurA in later proteobacteria, implying an evolutionary advantage for this domain architecture. Bioinformatics analysis of > 350,000 OMP sequences showed that their length, hydrophobicity and aggregation propensity are similar across the proteobacterial classes, ruling out a simple correlation between SurA domain architecture and these properties of OMP sequences. To investigate the role of the PPIase domains in SurA activity, we deleted one or both PPIase domains from E. coli SurA and investigated the ability of the resulting proteins to bind and prevent the aggregation of tOmpA (19 kDa) and OmpT (33 kDa). The results show that wild-type SurA inhibits the aggregation of both OMPs, as do the cytoplasmic OMP chaperones trigger factor and SecB. However, while the ability of SurA to bind and prevent tOmpA aggregation does not depend on its PPIase domains, deletion of even a single PPIase domain ablates the ability of SurA to prevent OmpT aggregation. The results demonstrate that the core domain of SurA endows its generic chaperone ability, while the presence of PPIase domains enhances its chaperone activity for specific OMPs, suggesting one reason for the conservation of multiple PPIase domains in SurA in proteobacteria

Intra- and interspecific polymorphisms ofLeishmania donovani andL. tropica minicircle DNA

A pair of degenerate polymerase chain reaction (PCR) primers (LEI-1, TCG GAT CC[C,T] [G,C]TG GGT AGG GGC GT; LEI-2, ACG GAT CC[G,C] [G,C][A,C]C TAT [A,T]TT ACA CC) defining a 0.15-kb segment ofLeishmania minicircle DNA was constructed. These primers amplified not only inter- but also intraspecifically polymorphic sequences. Individual sequences revealed a higher intraspecific than interspecific divergence. It is concluded that individual sequences are of limited relevance for species determination. In contrast, when a data base of 19 different sequences was analyzed in a dendrographic plot, an accurate species differentiation was feasible

Band edge evolution of transparent Zn M2III O4 (MIII=Co, Rh, Ir) spinels

ZnMIII 2 O4 (MIII = Co, Rh, Ir) spinels have been recently identified as promising p-type semiconductors for transparent electronics. However, discrepancies exist in the literature regarding their fundamental optoelectronic properties. In this paper, the electronic structures of these spinels are directly investigated using soft/hard x-ray photoelectron and x-ray absorption spectroscopies in conjunction with density functional theory calculations. In contrast to previous results, ZnCo2O4 is found to have a small electronic band gap with forbidden optical transitions between the true band edges, allowing for both bipolar doping and high optical transparency. Furthermore, increased d-d splitting combined with a concomitant lowering of Zn s/p conduction states is found to result in a ZnCo2O4 (ZCO) < ZnRh2O4 (ZRO) ≈ ZnIr2O4 (ZIO) band gap trend, finally resolving long-standing discrepancies in the literature

A Microsoft-Excel-based tool for running and critically appraising network meta-analyses--an overview and application of NetMetaXL.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.BACKGROUND: The use of network meta-analysis has increased dramatically in recent years. WinBUGS, a freely available Bayesian software package, has been the most widely used software package to conduct network meta-analyses. However, the learning curve for WinBUGS can be daunting, especially for new users. Furthermore, critical appraisal of network meta-analyses conducted in WinBUGS can be challenging given its limited data manipulation capabilities and the fact that generation of graphical output from network meta-analyses often relies on different software packages than the analyses themselves. METHODS: We developed a freely available Microsoft-Excel-based tool called NetMetaXL, programmed in Visual Basic for Applications, which provides an interface for conducting a Bayesian network meta-analysis using WinBUGS from within Microsoft Excel. . This tool allows the user to easily prepare and enter data, set model assumptions, and run the network meta-analysis, with results being automatically displayed in an Excel spreadsheet. It also contains macros that use NetMetaXL's interface to generate evidence network diagrams, forest plots, league tables of pairwise comparisons, probability plots (rankograms), and inconsistency plots within Microsoft Excel. All figures generated are publication quality, thereby increasing the efficiency of knowledge transfer and manuscript preparation. RESULTS: We demonstrate the application of NetMetaXL using data from a network meta-analysis published previously which compares combined resynchronization and implantable defibrillator therapy in left ventricular dysfunction. We replicate results from the previous publication while demonstrating result summaries generated by the software. CONCLUSIONS: Use of the freely available NetMetaXL successfully demonstrated its ability to make running network meta-analyses more accessible to novice WinBUGS users by allowing analyses to be conducted entirely within Microsoft Excel. NetMetaXL also allows for more efficient and transparent critical appraisal of network meta-analyses, enhanced standardization of reporting, and integration with health economic evaluations which are frequently Excel-based.CC is a recipient of a Vanier Canada Graduate Scholarship from the Canadian Institutes of Health Research (funding reference number—CGV 121171) and is a trainee on the Canadian Institutes of Health Research Drug Safety and Effectiveness Network team grant (funding reference number—116573). BH is funded by a New Investigator award from the Canadian Institutes of Health Research and the Drug Safety and Effectiveness Network. This research was partly supported by funding from CADTH as part of a project to develop Excel-based tools to support the conduct of health technology assessments. This research was also supported by Cornerstone Research Group