There are numerous reference equations available for the single-breath transfer factor of the lung for carbon monoxide (); however, it is not always clear which reference set should be used in clinical practice. The aim of the study was to develop the Global Lung Function Initiative (GLI) all-age reference values for Data from 19 centres in 14 countries were collected to define reference values. Similar to the GLI spirometry project, reference values were derived using the LMS (lambda, mu, sigma) method and the GAMLSS (generalised additive models for location, scale and shape) programme in R.12 660 measurements from asymptomatic, lifetime nonsmokers were submitted; 85% of the submitted data were from Caucasians. All data were uncorrected for haemoglobin concentration. Following adjustments for elevation above sea level, gas concentration and assumptions used for calculating the anatomic dead space volume, there was a high degree of overlap between the datasets. Reference values for Caucasians aged 5-85 years were derived for, transfer coefficient of the lung for carbon monoxide and alveolar volume.This is the largest collection of normative data, and the first global reference values available for.

Background: Research in modern biomedicine and social science requires sample sizes so large that they can often only be achieved through a pooled co-analysis of data from several studies. But the pooling of information from individuals in a central database that may be queried by researchers raises important ethico-legal questions and can be controversial. In the UK this has been highlighted by recent debate and controversy relating to the UK’s proposed ‘care.data’ initiative, and these issues reflect important societal and professional concerns about privacy, confidentiality and intellectual property. DataSHIELD provides a novel technological solution that can circumvent some of the most basic challenges in facilitating the access of researchers and other healthcare professionals to individual-level data.Methods: Commands are sent from a central analysis computer (AC) to several data computers (DCs) storing the data to be co-analysed. The data sets are analysed simultaneously but in parallel. The separate parallelized analyses are linked by non-disclosive summary statistics and commands transmitted back and forth between the DCs and the AC. This paper describes the technical implementation of DataSHIELD using a modified R statistical environment linked to an Opal database deployed behind the computer firewall of each DC. Analysis is controlled through a standard R environment at the AC.Results: Based on this Opal/R implementation, DataSHIELD is currently used by the Healthy Obese Project and the Environmental Core Project (BioSHaRE-EU) for the federated analysis of 10 data sets across eight European countries, and this illustrates the opportunities and challenges presented by the DataSHIELD approach.Conclusions: DataSHIELD facilitates important research in settings where: (i) a co-analysis of individual-level data from several studies is scientifically necessary but governance restrictions prohibit the release or sharing of some of the required data, and/or render data access unacceptably slow; (ii) a research group (e.g. in a developing nation) is particularly vulnerable to loss of intellectual property—the researchers want to fully share the information held in their data with national and international collaborators, but do not wish to hand over the physical data themselves; and (iii) a data set is to be included in an individual-level co-analysis but the physical size of the data precludes direct transfer to a new site for analysis.

The Global Lung Function Initiative (GLI) Network has become the largest resource for reference values for routine lung function testing ever assembled. This article addresses how the GLI Network came about, why it is important, and its current challenges and future directions. It is an extension of an article published in Breathe in 2013 [1], and summarises recent developments and the future of the GLI Network.Key pointsThe Global Lung Function Initiative (GLI) Network was established as a result of international collaboration, and altruism between researchers, clinicians and industry partners. The ongoing success of the GLI relies on network members continuing to work together to further improve how lung function is reported and interpreted across all age groups around the world.The GLI Network has produced standardised lung function reference values for spirometry and gas transfer tests.GLI reference equations should be adopted immediately for spirometry and gas transfer by clinicians and physiologists worldwide.The recently established GLI data repository will allow ongoing development and evaluation of reference values, and will offer opportunities for novel research.Educational aimsTo highlight the advances made by the GLI Network during the past 5 years.To highlight the importance of using GLI reference values for routine lung function testing (e.g. spirometry and gas transfer tests).To discuss the challenges that remain for developing and improving reference values for lung function tests.

Thus, in addition to the known effects on cortical excitability and synaptic plasticity, our data demonstrate that LI-rMS can change the survival and structural complexity of neurons. These findings provide a cellular and molecular framework for understanding what low intensity magnetic stimulation may contribute to human rTMS outcomes.

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The field of neuroimaging has embraced the need for sharing and collaboration. Data sharing mandates from public funding agencies and major journal publishers have spurred the development of data repositories and neuroinformatics consortia. However, efficient and effective data sharing still faces several hurdles. For example, open data sharing is on the rise but is not suitable for sensitive data that are not easily shared, such as genetics. Current approaches can be cumbersome (such as negotiating multiple data sharing agreements). There are also significant data transfer, organization and computational challenges. Centralized repositories only partially address the issues. We propose a dynamic, decentralized platform for large scale analyses called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The COINSTAC solution can include data missing from central repositories, allows pooling of both open and “closed” repositories by developing privacy-preserving versions of widely-used algorithms, and incorporates the tools within an easy-to-use platform enabling distributed computation. We present an initial prototype system which we demonstrate on two multi-site data sets, without aggregating the data. In addition, by iterating across sites, the COINSTAC model enables meta-analytic solutions to converge to “pooled-data” solutions (i.e., as if the entire data were in hand). More advanced approaches such as feature generation, matrix factorization models, and preprocessing can be incorporated into such a model. In sum, COINSTAC enables access to the many currently unavailable data sets, a user friendly privacy enabled interface for decentralized analysis, and a powerful solution that complements existing data sharing solutions.

Iron and cholesterol are both essential metabolites in mammalian systems, and too much or too little of either can have serious clinical consequences. In addition, both have been associated with steatosis and its progression, contributing, inter alia, to an increase in hepatic oxidative stress. The interaction between iron and cholesterol is unclear, with no consistent evidence emerging with respect to changes in plasma cholesterol on the basis of iron status. We sought to clarify the role of iron in lipid metabolism by studying the effects of iron status on hepatic cholesterol synthesis in mice with differing iron status. Transcripts of seven enzymes in the cholesterol biosynthesis pathway were significantly upregulated with increasing hepatic iron (R 2 between 0.602 and 0.164), including those of the rate-limiting enzyme, 3-hydroxy-3-methylglutarate-coenzyme A reductase (Hmgcr; R 2 5 0.362, P < 0.002). Hepatic cholesterol content correlated positively with hepatic iron (R 2 5 0.255, P < 0.007). There was no significant relationship between plasma cholesterol and either hepatic cholesterol or iron (R 2 5 0.101 and 0.014, respectively). Hepatic iron did not correlate with a number of known regulators of cholesterol synthesis, including sterol-regulatory element binding factor 2 (Srebf2; R 2 5 0.015), suggesting that the increases seen in the cholesterol biosynthesis pathway are independent of Srebf2. Transcripts of genes involved in bile acid synthesis, transport, or regulation did not increase with increasing hepatic iron. Conclusion: This study suggests that hepatic iron loading increases liver cholesterol synthesis and provides a new and potentially important additional mechanism by which iron could contribute to the development of fatty liver disease or lipotoxicity. (HEPATOLOGY 2010;52:462-471) Abbreviations: Abc, adenosine triphosphate-binding cassette; Apo, apolipoprotein; Bhmt2, betaine-homocysteine methyltransferase 2; C/EBPa, CCAAT/enhancer binding protein a; Cyp51, lanosterol-14a demethylase; Cyp27b1, 25-hydroxyvitamin D3-1a-hydroxylase; Cyp7a1, cholesterol 7a-monooxygenase; Ebp, cholestenol-D-isomerase; Ggcx, gamma-glutamyl carboxylase; Ggps1, geranylgeranyl diphosphate synthase 1; GSEA, gene set enrichment analysis; Hmgcr, 3-hydroxy-3-methylglutarate-coenzymeA reductase; Hnf4a, hepatocyte nuclear factor 4a; Hsd17b7, 3-keto-steroid reductase; Hsd3b7, hydroxy-D5-steroid dehydrogenase; Idi1, isopentenyl-diphosphate-D-isomerase; mRNA, messenger RNA; Mvk, mevalonate kinase; NAFLD, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; Nqo1, NAD(P)H dehydrogenase (quinone) 1; Nr1h3, nuclear receptor 1H3; Nsdhl, sterol-4a-carboxylate 3-dehydrogenase; Pmvk, phosphomevalonate kinase; Psap, prosaponin; RT-PCR, real-time polymerase chain reaction; Sc5d, lathosterol oxidase; Srebf2, sterol-regulatory element binding factor 2; Tm7sf2, D14-sterol reductase; Tmem97, transmembrane protein 97; Vkorc1, vitamin K epoxide reductase complex (subunit 1); VLDL, very low density lipoprotein; Vrk3, vaccini...

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