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Modern biomedical research is increasingly data-driven. To create the required big datasets, health data needs to be shared or reused, which often leads to privacy challenges. Data anonymization is an important protection method where data is transformed such that privacy guarantees can be provided according to formal models. For applications in practice, anonymization methods need to be integrated into scalable and reliable tools. In this work, we tackle the problem of achieving reliability. Privacy models often involve mathematical definitions using real numbers which are typically approximated using floating-point numbers when implemented as software. We study the effect on the privacy guarantees provided and present a reliable computing framework based on fractional and interval arithmetic for improving the reliability of implementations. Extensive evaluations demonstrate that reliable data anonymization is practical and that it can be achieved with minor impacts on executions times and data utility.

New and emerging molecular techniques are expanding understanding of the pathophysiology of spondyloarthritis (SpA). Genome-wide association studies identified novel pathways in antigen processing and presentation as well as helper T cell type 17 (T H 17) immunity associated with SpA. Immune cell profiling techniques have supported T H 17 immune responses and increasingly are revealing intestinal mucosal immune cells as associated with disease. Emerging technologies in epigenetics, transcriptomics, microbiome, and proteomics/metabolomics are adding to these, refining disease pathways and potentially identifying biomarkers for diagnosis and treatment responses. This review describes many of the new molecular techniques that are being utilized to investigate SpA., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Rheumatoid arthritis (RA) is a debilitating autoimmune disease characterized by chronic inflammation and progressive destruction of joint tissue. It is also characterized by aberrant blood phenotypes including anemia and suppressed lymphopoiesis that contribute to morbidity in RA patients. However, the impact of RA on hematopoietic stem cells (HSC) has not been fully elucidated. Using a collagen-induced mouse model of human RA, we identified systemic inflammation and myeloid overproduction associated with activation of a myeloid differentiation gene program in HSC. Surprisingly, despite ongoing inflammation, HSC from arthritic mice remain in a quiescent state associated with activation of a proliferation arrest gene program. Strikingly, we found that inflammatory cytokine blockade using the interleukin-1 receptor antagonist anakinra led to an attenuation of inflammatory arthritis and myeloid expansion in the bone marrow of arthritic mice. In addition, anakinra reduced expression of inflammation-driven myeloid lineage and proliferation arrest gene programs in HSC of arthritic mice. Altogether, our findings show that inflammatory cytokine blockade can contribute to normalization of hematopoiesis in the context of chronic autoimmune arthritis., (Copyright© 2020 Ferrata Storti Foundation.)

Background: Modern data driven medical research promises to provide new insights into the development and course of disease and to enable novel methods of clinical decision support. To realize this, machine learning models can be trained to make predictions from clinical, paraclinical and biomolecular data. In this process, privacy protection and regulatory requirements need careful consideration, as the resulting models may leak sensitive personal information. To counter this threat, a wide range of methods for integrating machine learning with formal methods of privacy protection have been proposed. However, there is a significant lack of practical tools to create and evaluate such privacy-preserving models. In this software article, we report on our ongoing efforts to bridge this gap., Results: We have extended the well-known ARX anonymization tool for biomedical data with machine learning techniques to support the creation of privacy-preserving prediction models. Our methods are particularly well suited for applications in biomedicine, as they preserve the truthfulness of data (e.g. no noise is added) and they are intuitive and relatively easy to explain to non-experts. Moreover, our implementation is highly versatile, as it supports binomial and multinomial target variables, different types of prediction models and a wide range of privacy protection techniques. All methods have been integrated into a sound framework that supports the creation, evaluation and refinement of models through intuitive graphical user interfaces. To demonstrate the broad applicability of our solution, we present three case studies in which we created and evaluated different types of privacy-preserving prediction models for breast cancer diagnosis, diagnosis of acute inflammation of the urinary system and prediction of the contraceptive method used by women. In this process, we also used a wide range of different privacy models (k-anonymity, differential privacy and a game-theoretic approach) as well as different data transformation techniques., Conclusions: With the tool presented in this article, accurate prediction models can be created that preserve the privacy of individuals represented in the training set in a variety of threat scenarios. Our implementation is available as open source software.

Mucosal surfaces are a unique symbiotic environment between a host and a vast and diverse ecology of microbes. These microbes have great immunomodulatory potential with respect to the host organism. Indeed, the mucosal immune system strikes a delicate balance between tolerance of commensal organisms and overt inflammation to ward off pathogens. Disruptions of the microbial ecology at mucosal surfaces has been described in a vast number of different human disease processes including many forms of arthritis, and the resulting implications are still being understood to their fullest. Herein, we review the current state of knowledge in microbe-host interactions as it relates to the development of arthritis through bacterial translocation, bacterial metabolite production, education of the immune response, and molecular mimicry., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Background: Relationships between specific microbes and proper immune system development, composition, and function have been reported in a number of studies. However, researchers have discovered only a fraction of the likely relationships. "Omic" methodologies such as 16S ribosomal RNA (rRNA) sequencing and time-of-flight mass cytometry (CyTOF) immunophenotyping generate data that support generation of hypotheses, with the potential to identify additional relationships at a level of granularity ripe for further experimentation. Pairwise linear regressions between microbial and host immune features provide one approach for quantifying relationships between "omes", and the differences in these relationships across study cohorts or arms. This approach yields a top table of candidate results. However, the top table alone lacks the detail that domain experts such as microbiologists and immunologists need to vet candidate results for follow-up experiments., Results: To support this vetting, we developed VOLARE (Visualization Of LineAr Regression Elements), a web application that integrates a searchable top table, small in-line graphs illustrating the fitted models, a network summarizing the top table, and on-demand detailed regression plots showing full sample-level detail. We applied VOLARE to three case studies-microbiome:cytokine data from fecal samples in human immunodeficiency virus (HIV), microbiome:cytokine data in inflammatory bowel disease and spondyloarthritis, and microbiome:immune cell data from gut biopsies in HIV. We present both patient-specific phenomena and relationships that differ by disease state. We also analyzed interaction data from system logs to characterize usage scenarios. This log analysis revealed that users frequently generated detailed regression plots, suggesting that this detail aids the vetting of results., Conclusions: Systematically integrating microbe:immune cell readouts through pairwise linear regressions and presenting the top table in an interactive environment supports the vetting of results for scientific relevance. VOLARE allows domain experts to control the analysis of their results, screening dozens of candidate relationships with ease. This interactive environment transcends the limitations of a static top table.

Risk stratification in patients with Barrett's esophagus (BE) to prevent the development of esophageal adenocarcinoma (EAC) is an unsolved task. The incidence of EAC and BE is increasing and patients are still at unknown risk. BarrettNET is an ongoing multicenter prospective cohort study initiated to identify and validate molecular and clinical biomarkers that allow a more personalized surveillance strategy for patients with BE. For BarrettNET participants are recruited in 20 study centers throughout Germany, to be followed for progression to dysplasia (low-grade dysplasia or high-grade dysplasia) or EAC for >10 years. The study instruments comprise self-administered epidemiological information (containing data on demographics, lifestyle factors, and health), as well as biological specimens, i.e., blood-based samples, esophageal tissue biopsies, and feces and saliva samples. In follow-up visits according to the individual surveillance plan of the participants, sample collection is repeated. The standardized collection and processing of the specimen guarantee the highest sample quality. Via a mobile accessible database, the documentation of inclusion, epidemiological data, and pathological disease status are recorded subsequently. Currently the BarrettNET registry includes 560 participants (23.1% women and 76.9% men, aged 22-92 years) with a median follow-up of 951 days. Both the design and the size of BarrettNET offer the advantage of answering research questions regarding potential causes of disease progression from BE to EAC. Here all the integrated methods and materials of BarrettNET are presented and reviewed to introduce this valuable German registry., (© The Author(s) 2019. Published by Oxford University Press on behalf of International Society for Diseases of the Esophagus.)

Background: Modern data-driven approaches to medical research require patient-level information at comprehensive depth and breadth. To create the required big datasets, information from disparate sources can be integrated into clinical and translational warehouses. This is typically implemented with Extract, Transform, Load (ETL) processes, which access, harmonize and upload data into the analytics platform., Objective: Privacy-protection needs careful consideration when data is pooled or re-used for secondary purposes, and data anonymization is an important protection mechanism. However, common ETL environments do not support anonymization, and common anonymization tools cannot easily be integrated into ETL workflows. The objective of the work described in this article was to bridge this gap., Methods: Our main design goals were (1) to base the anonymization process on expert-level risk assessment methodologies, (2) to use transformation methods which preserve both the truthfulness of data and its schematic properties (e.g. data types), (3) to implement a method which is easy to understand and intuitive to configure, and (4) to provide high scalability., Results: We designed a novel and efficient anonymization process and implemented a plugin for the Pentaho Data Integration (PDI) platform, which enables integrating data anonymization and re-identification risk analyses directly into ETL workflows. By combining different instances into a single ETL process, data can be protected from multiple threats. The plugin supports very large datasets by leveraging the streaming-based processing model of the underlying platform. We present results of an extensive experimental evaluation and discuss successful applications., Conclusions: Our work shows that expert-level anonymization methodologies can be integrated into ETL workflows. Our implementation is available under a non-restrictive open source license and it overcomes several limitations of other data anonymization tools., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Maternal obesity is associated with increased risk for offspring obesity and non-alcoholic fatty liver disease (NAFLD), but the causal drivers of this association are unclear. Early colonization of the infant gut by microbes plays a critical role in establishing immunity and metabolic function. Here, we compare germ-free mice colonized with stool microbes (MB) from 2-week-old infants born to obese (Inf-ObMB) or normal-weight (Inf-NWMB) mothers. Inf-ObMB-colonized mice demonstrate increased hepatic gene expression for endoplasmic reticulum stress and innate immunity together with histological signs of periportal inflammation, a histological pattern more commonly reported in pediatric cases of NAFLD. Inf-ObMB mice show increased intestinal permeability, reduced macrophage phagocytosis, and dampened cytokine production suggestive of impaired macrophage function. Furthermore, exposure to a Western-style diet in Inf-ObMB mice promotes excess weight gain and accelerates NAFLD. Overall, these results provide functional evidence supporting a causative role of maternal obesity-associated infant dysbiosis in childhood obesity and NAFLD.

Individuals at high risk of developing seropositive rheumatoid arthritis (RA) can be identified for translational research and disease prevention studies through the presence of highly informative and predictive patterns of RA-related autoantibodies, especially anti-citrullinated protein antibodies (ACPAs), in the serum. In serologically positive individuals without arthritis, designated ACPA positive at risk, the presence of mucosal inflammatory processes associated with the presence of local ACPA production has been demonstrated. In other at-risk populations, local RA-related autoantibody production is present even in the absence of serum autoantibodies. Additionally, a proportion of at-risk individuals exhibit local mucosal ACPA production in the lung, as well as radiographic small-airway disease, sputum hypercellularity and increased neutrophil extracellular trap formation. Other mucosal sites in at-risk individuals also exhibit autoantibody production, inflammation and/or evidence of dysbiosis. As the proportion of individuals who exhibit such localized inflammation-associated ACPA production is substantially higher than the likelihood of an individual developing future RA, this finding raises the hypothesis that mucosal ACPAs have biologically relevant protective roles. Identifying the mechanisms that drive both the generation and loss of externally focused mucosal ACPA production and promote systemic autoantibody expression and ultimately arthritis development should provide insights into new therapeutic approaches to prevent RA.

Objective: Observations of microbial dysbiosis in patients with rheumatoid arthritis (RA) have raised interest in studying microbial-mucosal interactions as a potential trigger of RA. Using the murine collagen-induced arthritis (CIA) model, we undertook this study to test our hypothesis that microbiota modulate immune responses leading to autoimmune arthritis., Methods: CIA was induced by immunization of mice with type II collagen (CII) in adjuvant on days 0 and 21, with arthritis appearing on days 23 and 24. Intestinal microbiota were profiled by 16S ribosomal RNA sequencing every 7 days during the course of CIA, and intestinal mucosal changes were evaluated on days 14 and 35. Then, microbiota were depleted either early (7 days before immunization) or late (day 21 after immunization) by administration of broad-spectrum antibiotics. Disease severity, autoantibody and systemic cytokine production, and intestinal mucosal responses were monitored in the setting of microbial reduction., Results: Significant dysbiosis and mucosal inflammation occurred early in CIA, prior to visible arthritis, and continued to evolve during the course of disease. Depletion of the microbiota prior to the induction of CIA resulted in an ~40% reduction in disease severity and in significantly reduced levels of serum inflammatory cytokines and anti-CII antibodies. In intestinal tissue, production of interleukin-17A (IL-17A) and IL-22 was delayed. Unexpectedly, microbial depletion during the late phase of CIA resulted in a >50% decrease in disease severity. Anti-CII antibodies were mildly reduced but were significantly impaired in their ability to activate complement, likely due to altered glycosylation profiles., Conclusion: These data support a model in which intestinal dysbiosis triggers mucosal immune responses that stimulate T and B cells that are key for the development of inflammatory arthritis., (© 2018, American College of Rheumatology.)

Background: Dysbiosis occurs in spondyloarthritis (SpA) and inflammatory bowel disease (IBD), which is subdivided into Crohn's disease (CD) and ulcerative colitis (UC). The immunologic consequences of alterations in microbiota, however, have not been defined. Intraepithelial lymphocytes (IELs) are T cells within the intestinal epithelium that are in close contact with bacteria and are likely to be modulated by changes in microbiota. We examined differences in human gut-associated bacteria and tested correlation with functional changes in IELs in patients with axial SpA (axSpA), CD, or UC, and in controls., Methods: We conducted a case-control study to evaluate IELs from pinch biopsies of grossly normal colonic tissue from subjects with biopsy-proven CD or UC, axSpA fulfilling Assessment of SpondyloArthritis International Society (ASAS) criteria and from controls during endoscopy. IELs were harvested and characterized by flow cytometry for cell surface markers. Secreted cytokines were measured by ELISA. Microbiome analysis was by 16S rRNA gene sequencing from rectal swabs. Statistical analyses were performed with the Kruskal-Wallis and Spearman's rank tests., Results: The total number of IELs was significantly decreased in subjects with axSpA compared to those with IBD and controls, likely due to a decrease in TCRβ+ IELs. We found strong, significant negative correlation between peripheral lymphocyte count and IEL number. IELs secreted significantly increased IL-1β in patients with UC, significantly increased IL-17A and IFN-γ in patients with CD, and significantly increased TNF-α in patients with CD and axSpA as compared to other cohorts. For each disease subtype, IELs and IEL-produced cytokines were positively and negatively correlated with the relative abundance of multiple bacterial taxa., Conclusions: Our data indicate differences in IEL function among subjects with axSpA, CD, and UC compared to healthy controls. We propose that the observed correlation between altered microbiota and IEL function in these populations are relevant to the pathogenesis of axSpA and IBD, and discuss possible mechanisms., Trial Registration: ClinicalTrials.gov, NCT02389075 . Registered on 17 March 2015.

Introduction: This article is part of the Focus Theme of Methods of Information in Medicine on the German Medical Informatics Initiative. Future medicine will be predictive, preventive, personalized, participatory and digital. Data and knowledge at comprehensive depth and breadth need to be available for research and at the point of care as a basis for targeted diagnosis and therapy. Data integration and data sharing will be essential to achieve these goals. For this purpose, the consortium Data Integration for Future Medicine (DIFUTURE) will establish Data Integration Centers (DICs) at university medical centers., Objectives: The infrastructure envisioned by DIFUTURE will provide researchers with cross-site access to data and support physicians by innovative views on integrated data as well as by decision support components for personalized treatments. The aim of our use cases is to show that this accelerates innovation, improves health care processes and results in tangible benefits for our patients. To realize our vision, numerous challenges have to be addressed. The objective of this article is to describe our concepts and solutions on the technical and the organizational level with a specific focus on data integration and sharing., Governance and Policies: Data sharing implies significant security and privacy challenges. Therefore, state-of-the-art data protection, modern IT security concepts and patient trust play a central role in our approach. We have established governance structures and policies safeguarding data use and sharing by technical and organizational measures providing highest levels of data protection. One of our central policies is that adequate methods of data sharing for each use case and project will be selected based on rigorous risk and threat analyses. Interdisciplinary groups have been installed in order to manage change., Architectural Framework and Methodology: The DIFUTURE Data Integration Centers will implement a three-step approach to integrating, harmonizing and sharing structured, unstructured and omics data as well as images from clinical and research environments. First, data is imported and technically harmonized using common data and interface standards (including various IHE profiles, DICOM and HL7 FHIR). Second, data is preprocessed, transformed, harmonized and enriched within a staging and working environment. Third, data is imported into common analytics platforms and data models (including i2b2 and tranSMART) and made accessible in a form compliant with the interoperability requirements defined on the national level. Secure data access and sharing will be implemented with innovative combinations of privacy-enhancing technologies (safe data, safe settings, safe outputs) and methods of distributed computing., Use Cases: From the perspective of health care and medical research, our approach is disease-oriented and use-case driven, i.e. following the needs of physicians and researchers and aiming at measurable benefits for our patients. We will work on early diagnosis, tailored therapies and therapy decision tools with focuses on neurology, oncology and further disease entities. Our early uses cases will serve as blueprints for the following ones, verifying that the infrastructure developed by DIFUTURE is able to support a variety of application scenarios., Discussion: Own previous work, the use of internationally successful open source systems and a state-of-the-art software architecture are cornerstones of our approach. In the conceptual phase of the initiative, we have already prototypically implemented and tested the most important components of our architecture., Competing Interests: Disclosure The authors report no conflicts of interest in this work., (Schattauer GmbH.)

Tropomyosin receptor kinase (Trk) inhibitors are investigated as a novel targeted therapy in various cancers. We investigated the in vitro effects of the pan-Trk inhibitor GNF-5837 in human neuroendocrine tumor (NET) cells. The human neuroendocrine pancreatic BON1, bronchopulmonary NCI-H727 and ileal GOT1 cell lines were treated with GNF-5837 alone and in combination with everolimus. Cell viability decreased in a time- and dose-dependent manner in GOT1 cells in response to GNF-5837 treatment, while treatment in BON1 and NCI-H727 cells showed no effect on cellular viability. Trk receptor expression determined GNF-5837 sensitivity. GNF-5837 caused downregulation of PI3K-Akt-mTOR signaling, Ras-Raf-MEK-ERK signaling, the cell cycle and increased apoptotic cell death. The combinational treatment of GNF-5837 with everolimus showed a significant enhancement in inhibition of cell viability vs single substance treatments, due to a cooperative PI3K-Akt-mTOR and Ras-Raf-MEK-ERK pathway downregulation, as well as an enhanced cell cycle component downregulation. Immunohistochemical staining for Trk receptors were performed using a tissue microarray containing 107 tumor samples of gastroenteropancreatic NETs. Immunohistochemical staining with TrkA receptor and pan-Trk receptor antibodies revealed a positive staining in pancreatic NETs in 24.2% (8/33) and 33.3% (11/33), respectively. We demonstrated that the pan-Trk inhibitor GNF-5837 has promising anti-tumoral properties in human NET cell lines expressing the TrkA receptor. Immunohistochemical or molecular screening for Trk expression particularly in pancreatic NETs might serve as predictive marker for molecular targeted therapy with Trk inhibitors., (© 2018 Society for Endocrinology.)

The sharing of sensitive personal health data is an important aspect of biomedical research. Methods of data de-identification are often used in this process to trade the granularity of data off against privacy risks. However, traditional approaches, such as HIPAA safe harbor or -anonymization, often fail to provide data with sufficient quality. Alternatively, data can be de-identified only to a degree which still allows us to use it as required, e.g., to carry out specific analyses. Controlled environments, which restrict the ways recipients can interact with the data, can then be used to cope with residual risks. The contributions of this article are twofold. First, we present a method for implementing controlled data sharing environments and analyze its privacy properties. Second, we present a de-identification method which is specifically suited for sanitizing health data which is to be shared in such environments. Traditional de-identification methods control the uniqueness of records in a dataset. The basic idea of our approach is to reduce the probability that a record in a dataset has characteristics which are unique within the underlying population. As the characteristics of the population are typically not known, we have implemented a pragmatic solution in which properties of the population are modeled with statistical methods. We have further developed an accompanying process for evaluating and validating the degree of protection provided. The results of an extensive experimental evaluation show that our approach enables the safe sharing of high-quality data and that it is highly scalable.

Interactions between the microbiota and distal gut are important for the maintenance of a healthy intestinal barrier; dysbiosis of intestinal microbial communities has emerged as a likely contributor to diseases that arise at the level of the mucosa. Intraepithelial lymphocytes (IELs) are positioned within the epithelial barrier, and in the small intestine they function to maintain epithelial homeostasis. We hypothesized that colon IELs promote epithelial barrier function through the expression of cytokines in response to interactions with commensal bacteria. Profiling of bacterial 16S ribosomal RNA revealed that candidate bacteria in the order Bacteroidales are sufficient to promote IEL presence in the colon that in turn produce interleukin-6 (IL-6) in a MyD88 (myeloid differentiation primary response 88)-dependent manner. IEL-derived IL-6 is functionally important in the maintenance of the epithelial barrier as IL-6 -/- mice were noted to have increased paracellular permeability, decreased claudin-1 expression, and a thinner mucus gel layer, all of which were reversed by transfer of IL-6 +/+ IELs, leading to protection of mice in response to Citrobacter rodentium infection. Therefore, we conclude that microbiota provide a homeostatic role for epithelial barrier function through regulation of IEL-derived IL-6.

The association of inflammatory arthritis with intestinal pathology extends back more than 100 years. This association is now supported by epidemiologic studies demonstrating an elevated prevalence of inflammatory bowel disease in spondyloarthritis and vice versa, compared with the general population. Genetic and intestinal microbiome studies have further linked these diseases. Although diabetes and nonalcoholic fatty liver disease disproportionately affect individuals with psoriatic arthritis, diseases of the esophagus, stomach, pancreas, and liver are not particularly common in spondyloarthritis. Clinicians should be aware of the differential diagnosis and the appropriate diagnostic tools available when evaluating digestive and hepatic disorders in spondyloarthritis., (Published by Elsevier Inc.)

Background: Translational researchers need robust IT solutions to access a range of data types, varying from public data sets to pseudonymised patient information with restricted access, provided on a case by case basis. The reason for this complication is that managing access policies to sensitive human data must consider issues of data confidentiality, identifiability, extent of consent, and data usage agreements. All these ethical, social and legal aspects must be incorporated into a differential management of restricted access to sensitive data., Methods: In this paper we present a pilot system that uses several common open source software components in a novel combination to coordinate access to heterogeneous biomedical data repositories containing open data (open access) as well as sensitive data (restricted access) in the domain of biobanking and biosample research. Our approach is based on a digital identity federation and software to manage resource access entitlements., Results: Open source software components were assembled and configured in such a way that they allow for different ways of restricted access according to the protection needs of the data. We have tested the resulting pilot infrastructure and assessed its performance, feasibility and reproducibility., Conclusions: Common open source software components are sufficient to allow for the creation of a secure system for differential access to sensitive data. The implementation of this system is exemplary for researchers facing similar requirements for restricted access data. Here we report experience and lessons learnt of our pilot implementation, which may be useful for similar use cases. Furthermore, we discuss possible extensions for more complex scenarios.

Multiple genetic and environmental factors have been associated with an increased risk for rheumatoid arthritis (RA). Of these, the strongest associations have been seen with female sex, a family history of RA, the genetic factor the "shared epitope," and exposure to tobacco smoke. There is also renewed interest in mucosal inflammation and microbial factors as contributors to the development of RA. However, the identification of a "preclinical" period of RA that can be defined as local or systemic autoimmunity as measured by autoantibodies and other biomarkers prior to the development of clinically apparent synovitis suggests that the risk factors for RA are acting long prior to first clinical evidence of IA. As such, a major challenge to the field will be to investigate the full spectrum of the development of RA, from initiation and propagation of autoimmunity during preclinical RA and transition to clinically apparent synovitis and classifiable RA, to determine which genetic and environmental factors are important at each stage of disease development. Understanding the exact role and timing of action of risk factors for RA is especially important given the advent of prevention trials in RA, and the hope that a full understanding of genetic and environmental factors in RA could lead to effective preventive interventions., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

When individual-level health data are shared in biomedical research, the privacy of patients must be protected. This is typically achieved by data de-identification methods, which transform data in such a way that formal privacy requirements are met. In the process, it is important to minimize the loss of information to maintain data quality. Although several models have been proposed for measuring this aspect, it remains unclear which model is best suited for which application. We have therefore performed an extensive experimental comparison. We first implemented several common quality models into the ARX de-identification tool for biomedical data. We then used each model to de-identify a patient discharge dataset covering almost 4 million cases and outputs were analyzed to measure the impact of different quality models on real-world applications. Our results show that different models are best suited for specific applications, but that one model (Non-Uniform Entropy) is particularly well suited for general-purpose use.

Background: Compared to traditional persuasive technology and health games, gamification is posited to offer several advantages for motivating behaviour change for health and well-being, and increasingly used. Yet little is known about its effectiveness., Aims: We aimed to assess the amount and quality of empirical support for the advantages and effectiveness of gamification applied to health and well-being., Methods: We identified seven potential advantages of gamification from existing research and conducted a systematic literature review of empirical studies on gamification for health and well-being, assessing quality of evidence, effect type, and application domain., Results: We identified 19 papers that report empirical evidence on the effect of gamification on health and well-being. 59% reported positive, 41% mixed effects, with mostly moderate or lower quality of evidence provided. Results were clear for health-related behaviours, but mixed for cognitive outcomes., Conclusions: The current state of evidence supports that gamification can have a positive impact in health and wellbeing, particularly for health behaviours. However several studies report mixed or neutral effect. Findings need to be interpreted with caution due to the relatively small number of studies and methodological limitations of many studies (e.g., a lack of comparison of gamified interventions to non-gamified versions of the intervention).

Background: Data sharing is a central aspect of modern biomedical research. It is accompanied by significant privacy concerns and often data needs to be protected from re-identification. With methods of de-identification datasets can be transformed in such a way that it becomes extremely difficult to link their records to identified individuals. The most important challenge in this process is to find an adequate balance between an increase in privacy and a decrease in data quality., Objectives: Accurately measuring the risk of re-identification in a specific data sharing scenario is an important aspect of data de-identification. Overestimation of risks will significantly deteriorate data quality, while underestimation will leave data prone to attacks on privacy. Several models have been proposed for measuring risks, but there is a lack of generic methods for risk-based data de-identification. The aim of the work described in this article was to bridge this gap and to show how the quality of de-identified datasets can be improved by using risk models to tailor the process of de-identification to a concrete context., Methods: We implemented a generic de-identification process and several models for measuring re-identification risks into the ARX de-identification tool for biomedical data. By integrating the methods into an existing framework, we were able to automatically transform datasets in such a way that information loss is minimized while it is ensured that re-identification risks meet a user-defined threshold. We performed an extensive experimental evaluation to analyze the impact of using different risk models and assumptions about the goals and the background knowledge of an attacker on the quality of de-identified data., Results: The results of our experiments show that data quality can be improved significantly by using risk models for data de-identification. On a scale where 100 % represents the original input dataset and 0 % represents a dataset from which all information has been removed, the loss of information content could be reduced by up to 10 % when protecting datasets against strong adversaries and by up to 24 % when protecting datasets against weaker adversaries., Conclusions: The methods studied in this article are well suited for protecting sensitive biomedical data and our implementation is available as open-source software. Our results can be used by data custodians to increase the information content of de-identified data by tailoring the process to a specific data sharing scenario. Improving data quality is important for fostering the adoption of de-identification methods in biomedical research.

The m.8344A>G mutation in the MTTK gene, which encodes the mitochondrial transfer RNA for lysine, is traditionally associated with myoclonic epilepsy and ragged-red fibres (MERRF), a multisystemic mitochondrial disease that is characterised by myoclonus, seizures, cerebellar ataxia, and mitochondrial myopathy with ragged-red fibres. We studied the clinical and paraclinical phenotype of 34 patients with the m.8344A>G mutation, mainly derived from the nationwide mitoREGISTER, the multicentric registry of the German network for mitochondrial disorders (mitoNET). Mean age at symptom onset was 24.5 years ±10.9 (6-48 years) with adult onset in 75 % of the patients. In our cohort, the canonical features seizures, myoclonus, cerebellar ataxia and ragged-red fibres that are traditionally associated with MERRF, occurred in only 61, 59, 70, and 63 % of the patients, respectively. In contrast, other features such as hearing impairment were even more frequently present (72 %). Other common features in our cohort were migraine (52 %), psychiatric disorders (54 %), respiratory dysfunction (45 %), gastrointestinal symptoms (38 %), dysarthria (36 %), and dysphagia (35 %). Brain MRI revealed cerebral and/or cerebellar atrophy in 43 % of our patients. There was no correlation between the heteroplasmy level in blood and age at onset or clinical phenotype. Our findings further broaden the clinical spectrum of the m.8344A>G mutation, document the large clinical variability between carriers of the same mutation, even within families and indicate an overlap of the phenotype with other mitochondrial DNA-associated syndromes.

Background: Privacy must be protected when sensitive biomedical data is shared, e.g. for research purposes. Data de-identification is an important safeguard, where datasets are transformed to meet two conflicting objectives: minimizing re-identification risks while maximizing data quality. Typically, de-identification methods search a solution space of possible data transformations to find a good solution to a given de-identification problem. In this process, parts of the search space must be excluded to maintain scalability., Objectives: The set of transformations which are solution candidates is typically narrowed down by storing the results obtained during the search process and then using them to predict properties of the output of other transformations in terms of privacy (first objective) and data quality (second objective). However, due to the exponential growth of the size of the search space, previous implementations of this method are not well-suited when datasets contain many attributes which need to be protected. As this is often the case with biomedical research data, e.g. as a result of longitudinal collection, we have developed a novel method., Methods: Our approach combines the mathematical concept of antichains with a data structure inspired by prefix trees to represent properties of a large number of data transformations while requiring only a minimal amount of information to be stored. To analyze the improvements which can be achieved by adopting our method, we have integrated it into an existing algorithm and we have also implemented a simple best-first branch and bound search (BFS) algorithm as a first step towards methods which fully exploit our approach. We have evaluated these implementations with several real-world datasets and the k-anonymity privacy model., Results: When integrated into existing de-identification algorithms for low-dimensional data, our approach reduced memory requirements by up to one order of magnitude and execution times by up to 25 %. This allowed us to increase the size of solution spaces which could be processed by almost a factor of 10. When using the simple BFS method, we were able to further increase the size of the solution space by a factor of three. When used as a heuristic strategy for high-dimensional data, the BFS approach outperformed a state-of-the-art algorithm by up to 12 % in terms of the quality of output data., Conclusions: This work shows that implementing methods of data de-identification for real-world applications is a challenging task. Our approach solves a problem often faced by data custodians: a lack of scalability of de-identification software when used with datasets having realistic schemas and volumes. The method described in this article has been implemented into ARX, an open source de-identification software for biomedical data.

Background: In addition to the Biobanking and BioMolecular resources Research Initiative (BBMRI), which is establishing a European research infrastructure for biobanks, a network for large European prospective cohorts (LPC) is being built to facilitate transnational research into important groups of diseases and health care. One instrument for this is the database "LPC Catalogue," which supports access to the biomaterials of the participating cohorts., Objectives: To present the LPC Catalogue as a relevant tool for connecting European biobanks. In addition, the LPC Catalogue has been extended to establish compatibility with existing Minimum Information About Biobank data Sharing (MIABIS) and to allow for more detailed search requests. This article describes the LPC Catalogue, its organizational and technical structure, and the aforementioned extensions., Materials and Methods: The LPC Catalogue provides a structured overview of the participating LPCs. It offers various retrieval possibilities and a search function. To support more detailed search requests, a new module has been developed, called a "data cube". The provision of data by the cohorts is being supported by a "connector" component., Results: The LPC Catalogue contains data on 22 cohorts and more than 3.8 million biosamples. At present, data on the biosamples of three cohorts have been acquired for the "cube," which is continuously being expanded. In the BBMRI-LPC, tendering for scientific projects using the data and samples of the participating cohorts is currently being carried out. In this context, several proposals have already been approved., Conclusions: The LPC Catalogue is supporting transnational access to biosamples. A comparison with existing solutions illustrates the relevance of its functionality.

Data sharing plays an important role in modern biomedical research. Due to the inherent sensitivity of health data, patient privacy must be protected. De-identification means to transform a dataset in such a way that it becomes extremely difficult for an attacker to link its records to identified individuals. This can be achieved with different types of data transformations. As transformation impacts the information content of a dataset, it is important to balance an increase in privacy with a decrease in data quality. To this end, models for measuring both aspects are needed. Non-Uniform Entropy is a model for data quality which is frequently recommended for de-identifying health data. In this work we show that it cannot be used in a meaningful way for measuring the quality of data which has been transformed with several important types of data transformation. We introduce a generic variant, which overcomes this limitation. We performed experiments with real-world datasets, which show that our method provides a unified framework in which the quality of differently transformed data can be compared to find a good or even optimal solution to a given data de-identification problem. We have implemented our method into ARX, an open source anonymization tool for biomedical data.

Objective: With the ARX data anonymization tool structured biomedical data can be de-identified using syntactic privacy models, such as k-anonymity. Data is transformed with two methods: (a) generalization of attribute values, followed by (b) suppression of data records. The former method results in data that is well suited for analyses by epidemiologists, while the latter method significantly reduces loss of information. Our tool uses an optimal anonymization algorithm that maximizes output utility according to a given measure. To achieve scalability, existing optimal anonymization algorithms exclude parts of the search space by predicting the outcome of data transformations regarding privacy and utility without explicitly applying them to the input dataset. These optimizations cannot be used if data is transformed with generalization and suppression. As optimal data utility and scalability are important for anonymizing biomedical data, we had to develop a novel method., Methods: In this article, we first confirm experimentally that combining generalization with suppression significantly increases data utility. Next, we proof that, within this coding model, the outcome of data transformations regarding privacy and utility cannot be predicted. As a consequence, existing algorithms fail to deliver optimal data utility. We confirm this finding experimentally. The limitation of previous work can be overcome at the cost of increased computational complexity. However, scalability is important for anonymizing data with user feedback. Consequently, we identify properties of datasets that may be predicted in our context and propose a novel and efficient algorithm. Finally, we evaluate our solution with multiple datasets and privacy models., Results: This work presents the first thorough investigation of which properties of datasets can be predicted when data is anonymized with generalization and suppression. Our novel approach adopts existing optimization strategies to our context and combines different search methods. The experiments show that our method is able to efficiently solve a broad spectrum of anonymization problems., Conclusion: Our work shows that implementing syntactic privacy models is challenging and that existing algorithms are not well suited for anonymizing data with transformation models which are more complex than generalization alone. As such models have been recommended for use in the biomedical domain, our results are of general relevance for de-identifying structured biomedical data., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Collaborative collection and sharing of data have become a core element of biomedical research. Typical applications are multi-site registries which collect sensitive person-related data prospectively, often together with biospecimens. To secure these sensitive data, national and international data protection laws and regulations demand the separation of identifying data from biomedical data and to introduce pseudonyms. Neither the formulation in laws and regulations nor existing pseudonymization concepts, however, are precise enough to directly provide an implementation guideline. We therefore describe core requirements as well as implementation options for registries and study databases with sensitive biomedical data., Methods: We first analyze existing concepts and compile a set of fundamental requirements for pseudonymized data management. Then we derive a system architecture that fulfills these requirements. Next, we provide a comprehensive overview and a comparison of different technical options for an implementation. Finally, we develop a generic software solution for managing pseudonymized data and show its feasibility by describing how we have used it to realize two research networks., Results: We have found that pseudonymization models are highly heterogeneous, already on a conceptual level. We have compiled a set of requirements from different pseudonymization schemes. We propose an architecture and present an overview of technical options. Based on a selection of technical elements, we suggest a generic solution. It supports the multi-site collection and management of biomedical data. Security measures are multi-tier pseudonymity and physical separation of data over independent backend servers. Integrated views are provided by a web-based user interface. Our approach has been successfully used to implement a national and an international rare disease network., Conclusions: We were able to identify a set of core requirements out of several pseudonymization models. Considering various implementation options, we realized a generic solution which was implemented and deployed in research networks. Still, further conceptual work on pseudonymity is needed. Specifically, it remains unclear how exactly data is to be separated into distributed subsets. Moreover, a thorough risk and threat analysis is needed.

Intestinal epithelial cells (IECs) are exposed to profound fluctuations in oxygen tension and have evolved adaptive transcriptional responses to a low-oxygen environment. These adaptations are mediated primarily through the hypoxia-inducible factor (HIF) complex. Given the central role of the IEC in barrier function, we sought to determine whether HIF influenced epithelial tight junction (TJ) structure and function. Initial studies revealed that short hairpin RNA-mediated depletion of the HIF1β in T84 cells resulted in profound defects in barrier and nonuniform, undulating TJ morphology. Global HIF1α chromatin immunoprecipitation (ChIP) analysis identified claudin-1 (CLDN1) as a prominent HIF target gene. Analysis of HIF1β-deficient IEC revealed significantly reduced levels of CLDN1. Overexpression of CLDN1 in HIF1β-deficient cells resulted in resolution of morphological abnormalities and restoration of barrier function. ChIP and site-directed mutagenesis revealed prominent hypoxia response elements in the CLDN1 promoter region. Subsequent in vivo analysis revealed the importance of HIF-mediated CLDN1 expression during experimental colitis. These results identify a critical link between HIF and specific tight junction function, providing important insight into mechanisms of HIF-regulated epithelial homeostasis., (© 2015 Saeedi, Kao, et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).)

Interactions between the microbiota and distal gut are fundamental determinants of human health. Such interactions are concentrated at the colonic mucosa and provide energy for the host epithelium through the production of the short-chain fatty acid butyrate. We sought to determine the role of epithelial butyrate metabolism in establishing the austere oxygenation profile of the distal gut. Bacteria-derived butyrate affects epithelial O2 consumption and results in stabilization of hypoxia-inducible factor (HIF), a transcription factor coordinating barrier protection. Antibiotic-mediated depletion of the microbiota reduces colonic butyrate and HIF expression, both of which are restored by butyrate supplementation. Additionally, germ-free mice exhibit diminished retention of O2-sensitive dyes and decreased stabilized HIF. Furthermore, the influences of butyrate are lost in cells lacking HIF, thus linking butyrate metabolism to stabilized HIF and barrier function. This work highlights a mechanism where host-microbe interactions augment barrier function in the distal gut., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Microbes interact with the host immune system via several potential mechanisms. One essential step for each mechanism is the method by which intestinal microbes or their antigens access specific host immune cells. Using genetically susceptible mice (dnKO) that develop spontaneous, fulminant colitis, triggered by Bacteroides thetaiotaomicron (B. theta), we investigated the mechanism of intestinal microbial access under conditions that stimulate colonic inflammation. B. theta antigens localized to host immune cells through outer membrane vesicles (OMVs) that harbor bacterial sulfatase activity. We deleted the anaerobic sulfatase maturating enzyme (anSME) from B. theta, which is required for post-translational activation of all B. theta sulfatase enzymes. This bacterial mutant strain did not stimulate colitis in dnKO mice. Lastly, access of B. theta OMVs to host immune cells was sulfatase dependent. These data demonstrate that bacterial OMVs and associated enzymes promote inflammatory immune stimulation in genetically susceptible hosts., (Copyright © 2015 Elsevier Inc. All rights reserved.)

This article is part of a For-Discussion-Section of Methods of Information in Medicine about the paper "Combining Health Data Uses to Ignite Health System Learning" written by John D. Ainsworth and Iain E. Buchan [1]. It is introduced by an editorial. This article contains the combined commentaries invited to independently comment on the paper of Ainsworth and Buchan. In subsequent issues the discussion can continue through letters to the editor. With these comments on the paper "Combining Health Data Uses to Ignite Health System Learning", written by John D. Ainsworth and Iain E. Buchan [1], the journal seeks to stimulate a broad discussion on new ways for combining data sources for the reuse of health data in order to identify new opportunities for health system learning. An international group of experts has been invited by the editor of Methods to comment on this paper. Each of the invited commentaries forms one section of this paper.

IL-6 is a pleiotropic cytokine often associated with inflammation. Inhibition of this pathway has led to successful treatment of rheumatoid arthritis, but one unforeseen potential complication of anti-IL-6 therapy is bowel perforation. Within the intestine, IL-6 has been shown to prevent epithelial apoptosis during prolonged inflammation. The role of IL-6 in the intestine during an initial inflammatory insult is unknown. Here, we evaluate the role of IL-6 at the onset of an inflammatory injury. Using two murine models of bowel injury - wound by biopsy and bacterial triggered colitis - we demonstrated that IL-6 is induced soon after injury by multiple cell types including intraepithelial lymphocytes. Inhibition of IL-6 resulted in impaired wound healing due to decreased epithelial proliferation. Using intestinal tissue obtained from patients who underwent surgical resection of the colon due to traumatic perforation, we observed cells with detectable IL-6 within the area of perforation and not at distant sites. Our data demonstrate the important role of IL-6 produced in part by intraepithelial lymphocytes at the onset of an inflammatory injury for epithelial proliferation and wound repair.

Collaboration and data sharing have become core elements of biomedical research. Especially when sensitive data from distributed sources are linked, privacy threats have to be considered. Statistical disclosure control allows the protection of sensitive data by introducing fuzziness. Reduction of data quality, however, needs to be balanced against gains in protection. Therefore, tools are needed which provide a good overview of the anonymization process to those responsible for data sharing. These tools require graphical interfaces and the use of intuitive and replicable methods. In addition, extensive testing, documentation and openness to reviews by the community are important. Existing publicly available software is limited in functionality, and often active support is lacking. We present ARX, an anonymization tool that i) implements a wide variety of privacy methods in a highly efficient manner, ii) provides an intuitive cross-platform graphical interface, iii) offers a programming interface for integration into other software systems, and iv) is well documented and actively supported.

Introduction: Accelerometry is an important method for extending our knowledge about intensity, duration, frequency and patterns of physical activity needed to promote health. This study has used accelerometry to detect associations between intensity levels and related activity patterns with multimorbidity and disability. Moreover, the proportion of people meeting the physical activity recommendations for older people was assessed., Methods: Physical activity was measured in 168 subjects (78 males; 65-89 years of age), using triaxial GT3X accelerometers for ten consecutive days. The associations between physical activity parameters and multimorbidity or disability was examined using multiple logistic regression models, which were adjusted for gender, age, education, smoking, alcohol consumption, lung function, nutrition and multimorbidity or disability., Results: 35.7% of the participants met the physical activity recommendations of at least 150 minutes of moderate to vigorous activity per week. Only 11.9% reached these 150 minutes, when only bouts of at least 10 minutes were counted. Differences in moderate to vigorous activity between people with and without multimorbidity or disability were more obvious when shorter bouts instead of only longer bouts were included. Univariate analyses showed an inverse relationship between physical activity and multimorbidity or disability for light and moderate to vigorous physical activity. A higher proportion of long activity bouts spent sedentarily was associated with higher risk for multimorbidity, whereas a high proportion of long bouts in light activity seemed to prevent disability. After adjustment for covariates, there were no significant associations, anymore., Conclusions: The accumulated time in moderate to vigorous physical activity seems to have a stronger relationship with health and functioning when shorter activity bouts and not only longer bouts were counted. We could not detect an association of the intensity levels or activity patterns with multimorbidity or disability in elderly people after adjustment for covariates.

The etiology of most systemic autoimmune diseases remains unknown. There is often a preclinical period of systemic autoimmunity prior to the onset of clinically classifiable disease; established and emerging data suggest that dysregulated immune interactions with commensal microbiota may play a role in the initial generation of autoimmunity in this preclinical period. This article reviews potential mechanisms by which alterations of healthy microbiota may induce autoimmunity as well as mucosal microbial associations with autoimmune diseases. If mucosal microbiota lead to the development of autoimmunity, these mucosal sites, microorganisms, and immunologic mechanisms can be targeted to prevent the onset of systemic autoimmune disease., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Sensitive biomedical data is often collected from distributed sources, involving different information systems and different organizational units. Local autonomy and legal reasons lead to the need of privacy preserving integration concepts. In this article, we focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data. We present a flexible solution for anonymizing distributed data in the semi-honest model. Prior to the anonymization procedure, an encrypted global view of the dataset is constructed by means of a secure multi-party computing (SMC) protocol. This global representation can then be anonymized. Our approach is not limited to specific anonymization algorithms but provides pre- and postprocessing for a broad spectrum of algorithms and many privacy criteria. We present an extensive analytical and experimental evaluation and discuss which types of methods and criteria are supported. Our prototype demonstrates the approach by implementing k-anonymity, ℓ-diversity, t-closeness and δ-presence with a globally optimal de-identification method in horizontally and vertically distributed setups. The experiments show that our method provides highly competitive performance and offers a practical and flexible solution for anonymizing distributed biomedical datasets., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Objective: Bilateral adrenalectomy (BADX) is an important treatment option for patients with Cushing's syndrome (CS). Our aim is to analyze the long-term outcomes, surgical, biochemical, and clinical as well as morbidity and mortality, of patients who underwent BADX., Design: A total of 50 patients who underwent BADX since 1990 in two German centers were identified. Of them, 34 patients had Cushing's disease (CD), nine ectopic CS (ECS), and seven ACTH-independent bilateral adrenal hyperplasia (BAH)., Methods: Standardized follow-up examination was performed in 36 patients with a minimum follow-up time of 6 months after BADX and a median follow-up time of 11 years., Results: Surgical morbidity and mortality were 6 and 4% respectively. All patients were found to be in remission after BADX. Almost all Cushing's-specific comorbidities except for psychiatric diseases improved significantly. Health-related quality of life remained impaired in 45.0% of female and 16.7% of male patients compared with a healthy population. The median number of adrenal crises per 100 patient-years was four. Nelson tumor occurred in 24% of CD patients after a median time span of 51 months. Long-term mortality after 10 years was high in ECS (44%) compared with CD (3%) and BAH (14%)., Conclusions: BADX is an effective and relatively safe treatment option especially in patients with CD. The majority of patients experience considerable improvement of Cushing's symptoms., (© 2014 European Society of Endocrinology.)

Background: Elderly people obtain significant health benefits from physical activity (PA), but the role of activity patterns has scarcely been researched. The present study aims to describe the patterns of PA among different intensities of activity in elderly people. We assess how patterns differ between more and less active groups ('rare', 'average', and 'frequent'), and explore whether and how various PA parameters are associated with functional exercise capacity (FEC)., Methods: PA was measured in 168 subjects (78 males; 65-89 years of age), using a triaxial GT3X accelerometer for ten consecutive days. Subjects were divided into three groups by activity and the groups were compared. A multiple linear regression model was used to predict FEC., Results: Participants greater than or equal to 80 years are most prone to being sedentary for long periods, while women and the obese are the groups most likely to spend insufficient time in moderate to vigorous PA (MVPA). Rarely active elderly people had a decreased proportion of long bouts of MVPA and light PA and of short bouts in sedentary behavior than frequently active subjects did (p<0.001). As predictors of FEC, younger age, lower BMI, male sex, better lung function, absence of multimorbidity, longer times and longer bouts of MVPA emerged as significant parameters (r(2)=0.54). Patterns of MVPA explained most of the variance., Conclusions: PA patterns provide information beyond reports of activity alone. MVPA in elderly people may be increased by increasing the proportion of long bouts, in order to increase FEC as well as average PA. However, health conditions may limit PA. In rarely active people (often with reduced FEC, worse lung function, and diagnosis of multimorbidity or disability), longer periods of time in light PA may be sufficient to increase the overall level of activity.

There is growing interest in understanding the effects of host-microbial interactions on host physiologic processes. Much of the work in this arena is logically focused on the interaction at mucosal surfaces as this is a primary site of interaction. However, there is ample evidence to suggest that the effects of the microbiota have a much farther reach including the systemic immune system. While there are some similarities to effects at mucosal surfaces (i.e. reduced numbers of adaptive immune cells, diminished innate responses), there are some important differences that we highlight such as the response to immunogens and bacterial antigens. We propose that understanding the details of how specific components of the microbiota influence the systemic immune system likely will have significant impact on our understanding the pathophysiology of a variety of autoimmune diseases., (Copyright © 2013 Elsevier Ltd. All rights reserved.)

The creation of socially and technically robust biobank privacy regimes presupposes knowledge of and compliance with legal rules, professional standards of the biomedical community, and state-of-the-art data safety and security measures. The strategies in privacy management and data protection presented in this review show a trend that goes beyond searching for compromises or efforts of balancing scientific demands for efficiency and societal demands for effective privacy regimes. They focus on developing synergies that facilitate cooperative use of biomaterials and data and enhance sample search efficiency for researchers on the one hand, and protect rights and interests of donors and citizens on the other hand. Among the issues covered are: a) ethical sensitivities and public perceptions on privacy in biobanking b) tools and procedures that allow maintenance of the rights and dignity of donors, without jeopardizing legitimate information needs of researchers and autonomy of biobanks, and c) a privacy sensitive framework for sharing of data and biomaterials in the research context.

Background: Next generation sequencing has become the core technology for gene discovery in rare inherited disorders. However, the interpretation of the numerous sequence variants identified remains challenging. We assessed the application of exome sequencing for diagnostics in complex I deficiency, a disease with vast genetic heterogeneity., Methods: Ten unrelated individuals with complex I deficiency were selected for exome sequencing and sequential bioinformatic filtering. Cellular rescue experiments were performed to verify pathogenicity of novel disease alleles., Results: The first filter criterion was 'Presence of known pathogenic complex I deficiency variants'. This revealed homozygous mutations in NDUFS3 and ACAD9 in two individuals. A second criterion was 'Presence of two novel potentially pathogenic variants in a structural gene of complex I', which discovered rare variants in NDUFS8 in two unrelated individuals and in NDUFB3 in a third. Expression of wild-type cDNA in mutant cell lines rescued complex I activity and assembly, thus providing a functional validation of their pathogenicity. Using the third criterion 'Presence of two potentially pathogenic variants in a gene encoding a mitochondrial protein', loss-of-function mutations in MTFMT were discovered in two patients. In three patients the molecular genetic correlate remained unclear and follow-up analysis is ongoing., Conclusion: Appropriate in silico filtering of exome sequencing data, coupled with functional validation of new disease alleles, is effective in rapidly identifying disease-causative variants in known and new complex I associated disease genes.

Obesity has reached epidemic proportions, in the United States as well as among its trade partners such as Mexico. It has been established that an "obesogenic" (obesity-causing) food environment is one influence on obesity prevalence. To isolate the particular role of NAFTA, the North American Free Trade Agreement, in changing Mexico's food environment, we plotted the flow of several key products between the United States and Mexico over the 14-year NAFTA period (1994-2008) and situated them in a broader historical context. Key sources of USDA data include the Foreign Agricultural Service's Global Agricultural Trade System, its official repository for current and historical data on imports, exports and re-exports, and its Production, Supply, and Distribution online database. US export data were queried for agricultural products linked to shifting diet patterns including: corn, soybeans, sugar and sweeteners, consumer-oriented products, and livestock products. The Bureau of Economic Analysis' Balance of Payments and Direct Investment Position Data in their web-based International Economic Accounts system also helped determine changes in US direct investment abroad from 1982 to 2009. Directly and indirectly, the United States has exported increasing amounts of corn, soybeans, sugar, snack foods, and meat products into Mexico over the last two decades. Facilitated by NAFTA, these exports are one important way in which US agriculture and trade policy influences Mexico's food system. Because of significant US agribusiness investment in Mexico across the full spectrum of the latter's food supply chain, from production and processing to distribution and retail, the Mexican food system increasingly looks like the industrialized food system of the United States.

With the advent of technologies that allow simultaneous genotyping of thousands of single-nucleotide polymorphisms (SNPs) across the genome, the genetic contributions to complex diseases can be explored at an unprecedented detail. This study is among the first to apply the genome-wide association study (GWAS) approach to Alzheimer disease (AD). We present our GWAS results from the German population for genes included in the 'Top Results' list on the AlzGene database website. In addition to the apolipoprotein E locus, we identified nominally significant association signals in six of the ten genes investigated, albeit predominantly for SNPs other than those already published as being disease associated. Further, all of the four AD genes previously identified through GWAS also showed nominally significant association signals in our data. The results of our comparative study reinforce the necessity for replication and validation, not only of GWAS but also of candidate gene case-control studies, in different populations. Furthermore, cross-platform comparison of genotyping results can also identify new association signals. Finally, our data confirm that GWAS, regardless of the platform, are valuable for the identification of genetic variants associated with AD.

Biobanking is of high importance for research in rare diseases. There are >6,000 rare diseases with at least 30 million people affected in the European Union (EU). The European Commission (EC) has prioritized rare diseases in recent health and research programs. The rarity and diversity of rare diseases and their associated biomaterials harbor specific challenges and opportunities for biobanking requiring transnational collaboration and harmonization. Small collections or even individual samples may be extremely precious for research. Importantly, most rare disease biobanks work through the active participation of patients and patient organizations, and share benefits with them. This article gives recommendations related to rare disease biobanking reflecting consensus of an expert working group of the Biobank and Biomolecular Research Infrastructure program at a meeting in Munich on December 17-18, 2008.

Unlabelled: Antibody titers to P. gingivalis are increased in patients with rheumatoid arthritis and are associated with disease-specific autoimmunity., Background: Periodontitis (PD) has been implicated as a risk factor for rheumatoid arthritis (RA). We sought to characterize antibody titers to P. gingivalis (a pathogen in PD) in subjects with RA, PD, and in healthy controls and to examine their relationship with disease autoantibodies., Methods: P. gingivalis antibody was measured in subjects with RA (n=78), PD (n=39), and in controls (n=40). Group frequencies of bacterial titer elevations were compared using the Chi-square test and antibody titers were compared using non-parametric tests. Correlations of P. gingivalis titer with C-reactive protein (CRP), antibody to cyclic citrullinated peptide (anti-CCP), and rheumatoid factor (RF) were examined in those with RA while CRP and autoantibody concentrations were compared based on seropositivity to P. gingivalis., Results: Antibody titers to P. gingivalis were highest in PD, lowest in controls, and intermediate in RA (p=0.0003). Elevations in P. gingivalis (titer> or =800) were more common in RA and PD (67% and 77%, respectively) than in controls (40%) (p=0.002). In RA, there were significant correlations with P. gingivalis titer with CRP, anti-CCP-IgM, and -IgG-2. CRP (p=0.006), anti-CCP-IgM (p=0.01) and -IgG2 (p=0.04) concentrations were higher in RA cases with P. gingivalis titers > or =800 compared to cases with titers <800., Conclusion: Antibodies to P. gingivalis are more common in RA subjects than controls, although lower than that in PD. Associations of P. gingivalis titers with RA-related autoantibody and CRP concentrations suggests that infection with this organism plays a role in disease risk and progression in RA.