| 1. |
- Dunham, I, et al.
(författare)
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The DNA sequence of human chromosome 22
- 1999
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Ingår i: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 402:6761, s. 489-495
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Tidskriftsartikel (refereegranskat)
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| 3. |
- Seroussi, E, et al.
(författare)
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Duplications on human chromosome 22 reveal a novel Ret Finger Protein-like gene family with sense and endogenous antisense transcripts
- 1999
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Ingår i: Genome research. - : Cold Spring Harbor Laboratory. - 1088-9051 .- 1549-5469. ; 9:9, s. 803-814
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Tidskriftsartikel (refereegranskat)abstract
- Analysis of 600 kb of sequence encompassing the beta-prime adaptin (BAM22) gene on human chromosome 22 revealed intrachromosomal duplications within 22q12–13 resulting in three active RFPLgenes, two RFPL pseudogenes, and two pseudogenes ofBAM22. The genomic sequence of BAM22ϑ1 shows a remarkable similarity to that of BAM22. The cDNA sequence comparison of RFPL1, RFPL2, and RFPL3 showed 95%–96% identity between the genes, which were most similar to theRet Finger Protein gene from human chromosome 6. The sense RFPL transcripts encode proteins with the tripartite structure, composed of RING finger, coiled–coil, and B30-2 domains, which are characteristic of the RING–B30 family. Each of these domains are thought to mediate protein–protein interactions by promoting homo- or heterodimerization. The MID1 gene on Xp22 is also a member of the RING-B30 family and is mutated in Opitz syndrome (OS). The autosomal dominant form of OS shows linkage to 22q11–q12. We detected a polymorphic protein-truncating allele ofRFPL1 in 8% of the population, which was not associated with the OS phenotype. We identified 6-kb and 1.2-kb noncoding antisense mRNAs of RFPL1S and RFPL3S antisense genes, respectively. The RFPL1S and RFPL3S genes cover substantial portions of their sense counterparts, which suggests that the function of RFPL1S and RFPL3S is a post-transcriptional regulation of the sense RFPLgenes. We illustrate the role of intrachromosomal duplications in the generation of RFPL genes, which were created by a series of duplications and share an ancestor with the RING-B30 domain containing genes from the major histocompatibility complex region on human chromosome 6.[The sequence data described in this paper have been submitted to GenBank under the following accession nos:AJ010228–AJ010233, AC000025, AC000041, AC000045, and AC002059.]
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| 4. |
- Simon, M P, et al.
(författare)
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Deregulation of the platelet-derived growth factor B-chain gene via fusion with collagen gene COL1A1 in dermatofibrosarcoma protuberans and giant-cell fibroblastoma.
- 1997
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Ingår i: Nature Genetics. - : Springer Science and Business Media LLC. - 1061-4036 .- 1546-1718. ; 15:1, s. 95-8
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Tidskriftsartikel (refereegranskat)abstract
- Dermatofibrosarcoma protuberans (DP), an infiltrative skin tumour of intermediate malignancy, presents specific features such as reciprocal translocations t(17;22)(q22;q13) and supernumerary ring chromosomes derived from the t(17;22). In this report, the breakpoints from translocations and rings in DP and its juvenile form, giant cell fibroblastoma (GCF), were characterised on the genomic and RNA level. These rearrangements fuse the platelet-derived growth factor B-chain (PDGFB, c-sis proto-oncogene) and the collagen type I alpha 1 (COL1A1) genes. PDGFB has transforming activity and is a potent mitogen for a number of cell types, but its role in oncogenic processes is not fully understood. COL1A1 is a major constituent of the connective tissue matrix. Neither PDGFB nor COL1A1 have so far been implicated in any tumour translocations. These gene fusions delete exon 1 of PDGFB, and release this growth factor from its normal regulation.
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| 7. |
- Courvoisier, D, et al.
(författare)
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POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY
- 2020
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 79, s. 124-125
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Comparing drug effectiveness in observational settings is hampered by several major threats, among them confounding and attrition bias bias (patients who stop treatment no longer contribute information, which may overestimate true drug effectiveness).Objectives:To present points to consider (PtC) when analysing and reporting comparative effectiveness with observational data in rheumatology (EULAR-funded taskforce).Methods:The task force comprises 18 experts: epidemiologists, statisticians, rheumatologists, patients, and health professionals.Results:A systematic literature review of methods currently used for comparative effectiveness research in rheumatology and a statistical simulation study were used to inform the PtC (table). Overarching principles focused on defining treatment effectiveness and promoting robust and transparent epidemiological and statistical methods increase the trustworthiness of the results.Points to considerReporting of comparative effectiveness observational studies must follow the STROBE guidelinesAuthors should prepare a statistical analysis plan in advanceTo provide a more complete picture of effectiveness, several outcomes across multiple health domains should be comparedLost to follow-up from the study sample must be reported by the exposure of interestThe proportion of patients who stop and/or change therapy over time, as well as the reasons for treatment discontinuation must be reportedCovariates should be chosen based on subject matter knowledge and model selection should be justifiedThe study baseline should be at treatment initiation and a description of how covariate measurements relate to baseline should be includedThe analysis should be based on all patients starting a treatment and not limited to patients remaining on treatment at a certain time pointWhen treatment discontinuation occurs before the time of outcome assessment, this attrition should be taken into account in the analysis.Sensitivity analyses should be undertaken to explore the influence of assumptions related to missingness, particularly in case of attritionConclusion:The increased use of real-world comparative effectiveness studies makes it imperative to reduce divergent or contradictory results due to biases. Having clear recommendations for the analysis and reporting of these studies should promote agreement of observational studies, and improve studies’ trustworthiness, which may also facilitate meta-analysis of observational data.Disclosure of Interests:Delphine Courvoisier: None declared, Kim Lauper: None declared, Sytske Anne Bergstra: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Joanna KEDRA: None declared, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific
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| 8. |
- de Jong, Yde, et al.
(författare)
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PESI - a taxonomic backbone for Europe
- 2015
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Ingår i: Biodiversity Data Journal. - 1314-2836 .- 1314-2828. ; 3, s. 1-51
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Tidskriftsartikel (refereegranskat)abstract
- Reliable taxonomy underpins communication in all of biology, not least nature conservation and sustainable use of ecosystem resources. The flexibility of taxonomic interpretations, however, presents a serious challenge for end-users of taxonomic concepts. Users need standardised and continuously harmonised taxonomic reference systems, as well as high-quality and complete taxonomic data sets, but these are generally lacking for non-specialists. The solution is in dynamic, expertly curated web-based taxonomic tools.The Pan-European Species-directories Infrastructure (PESI) worked to solve this key issue by providing a taxonomic e-infrastructure for Europe. It strengthened the relevant social (expertise) and information (standards, data and technical) capacities of five major community networks on taxonomic indexing in Europe, which is essential for proper biodiversity assessment and monitoring activities. The key objectives of PESI were: 1) standardisation in taxonomic reference systems, 2) enhancement of the quality and completeness of taxonomic data sets and 3) creation of integrated access to taxonomic information.This paper describes the results of PESI and its future prospects, including the involvement in major European biodiversity informatics initiatives and programs.
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| 9. |
- Gossec, L, et al.
(författare)
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EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
- 2020
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Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 79:1, s. 69-76
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Tidskriftsartikel (refereegranskat)abstract
- Tremendous opportunities for health research have been unlocked by the recent expansion of big data and artificial intelligence. However, this is an emergent area where recommendations for optimal use and implementation are needed. The objective of these European League Against Rheumatism (EULAR) points to consider is to guide the collection, analysis and use of big data in rheumatic and musculoskeletal disorders (RMDs).MethodsA multidisciplinary task force of 14 international experts was assembled with expertise from a range of disciplines including computer science and artificial intelligence. Based on a literature review of the current status of big data in RMDs and in other fields of medicine, points to consider were formulated. Levels of evidence and strengths of recommendations were allocated and mean levels of agreement of the task force members were calculated.ResultsThree overarching principles and 10 points to consider were formulated. The overarching principles address ethical and general principles for dealing with big data in RMDs. The points to consider cover aspects of data sources and data collection, privacy by design, data platforms, data sharing and data analyses, in particular through artificial intelligence and machine learning. Furthermore, the points to consider state that big data is a moving field in need of adequate reporting of methods and benchmarking, careful data interpretation and implementation in clinical practice.ConclusionThese EULAR points to consider discuss essential issues and provide a framework for the use of big data in RMDs.
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| 13. |
- Kedra, D, et al.
(författare)
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Characterization of the human synaptogyrin gene family.
- 1998
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Ingår i: Human Genetics. - : Springer Science and Business Media LLC. - 0340-6717 .- 1432-1203. ; 103:2, s. 131-41
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Tidskriftsartikel (refereegranskat)abstract
- Genomic sequencing was combined with searches of databases for identification of active genes on human chromosome 22. A cosmid from 22q13, located in the telomeric vicinity of the PDGFB (platelet-derived growth factor B-chain) gene, was fully sequenced. Using an expressed sequence tag-based approach we characterized human (SYNGR1) and mouse (Syngr1) orthologs of the previously cloned rat synaptogyrin gene (RATSYNGR1). The human SYNGR1 gene reveals three (SYNGR1a, SYNGR1b, SYNGR1c) alternative transcript forms of 4.5, 1.3 and 0.9 kb, respectively. The transcription of SYNGR1 starts from two different promoters, and leads to predicted proteins with different N- and C-terminal ends. The most abundant SYNGR1 a transcript, the 4.5-kb form, which corresponds to RATSYNGR1, is highly expressed in neurons of the central nervous system and at much lower levels in other tissues, as determined by in situ hybridization histochemistry. The levels of SYNGR1b and SYNGR1c transcripts are low and limited to heart, skeletal muscle, ovary and fetal liver. We also characterized two additional members of this novel synaptogyrin gene family in human (SYNGR2 and SYNGR3), and one in mouse (Syngr2). The human SYNGR2 gene transcript of 1.6 kb is expressed at high levels in all tissues, except brain. The 2.2-kb SYNGR3 transcript was detected in brain and placenta only. The human SYNGR2 and SYNGR3 genes were mapped by fluorescence in situ hybridization to 17qtel and 16ptel, respectively. The human SYNGR2 gene has a processed pseudogene localized in 15q11. All predicted synaptogyrin proteins contain four strongly conserved transmembrane domains, which is consistent with the M-shaped topology. The C-terminal polypeptide ends are variable in length, display a low degree of sequence similarity between family members, and are therefore likely to convey the functional specificity of each protein.
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| 15. |
- Kedra, J, et al.
(författare)
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Current status of use of big data and artificial intelligence in RMDs: a systematic literature review informing EULAR recommendations
- 2019
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Ingår i: RMD open. - : BMJ. - 2056-5933. ; 5:2, s. e001004-
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Tidskriftsartikel (refereegranskat)abstract
- To assess the current use of big data and artificial intelligence (AI) in the field of rheumatic and musculoskeletal diseases (RMDs).MethodsA systematic literature review was performed in PubMed MEDLINE in November 2018, with key words referring to big data, AI and RMDs. All original reports published in English were analysed. A mirror literature review was also performed outside of RMDs on the same number of articles. The number of data analysed, data sources and statistical methods used (traditional statistics, AI or both) were collected. The analysis compared findings within and beyond the field of RMDs.ResultsOf 567 articles relating to RMDs, 55 met the inclusion criteria and were analysed, as well as 55 articles in other medical fields. The mean number of data points was 746 million (range 2000–5 billion) in RMDs, and 9.1 billion (range 100 000–200 billion) outside of RMDs. Data sources were varied: in RMDs, 26 (47%) were clinical, 8 (15%) biological and 16 (29%) radiological. Both traditional and AI methods were used to analyse big data (respectively, 10 (18%) and 45 (82%) in RMDs and 8 (15%) and 47 (85%) out of RMDs). Machine learning represented 97% of AI methods in RMDs and among these methods, the most represented was artificial neural network (20/44 articles in RMDs).ConclusionsBig data sources and types are varied within the field of RMDs, and methods used to analyse big data were heterogeneous. These findings will inform a European League Against Rheumatism taskforce on big data in RMDs.
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| 19. |
- Lauper, K, et al.
(författare)
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A SYSTEMATIC REVIEW TO INFORM THE EULAR POINTS TO CONSIDER WHEN ANALYSING AND REPORTING COMPARATIVE EFFECTIVENESS RESEARCH WITH OBSERVATIONAL DATA IN RHEUMATOLOGY
- 2020
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Ingår i: ANNALS OF THE RHEUMATIC DISEASES. - : BMJ. - 0003-4967 .- 1468-2060. ; 79, s. 123-124
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- Comparative effectiveness studies using observational data are increasingly used. Despite their high potential for bias, there are no detailed recommendations on how these studies should best be analysed and reported in rheumatology.Objectives:To conduct a systematic literature review of comparative effectiveness research in rheumatology to inform the EULAR task force developing points to consider when analysing and reporting comparative effectiveness research with observational data.Methods:All original articles comparing drug effectiveness in longitudinal observational studies of ≥100 patients published in key rheumatology journals (Scientific Citation Index > 2) between 1.01.2008 and 25.03.2019 available in Ovid MEDLINE® were included. Titles and abstracts were screened by two reviewers for the first 1000 abstracts and independently checked to ensure sufficient agreement has been reached. The main information extracted included the types of outcomes used to assess effectiveness, and the types of analyses performed, focusing particularly on confounding and attrition.Results:9969 abstracts were screened, with 218 articles proceeding to full-text extraction (Figure 1), representing a number of rheumatic and musculoskeletal diseases. Agreement between the two reviewers for the first 1000 abstracts was 92.7% with a kappa of 0.6. The majority of the studies used several outcomes to evaluate effectiveness (Figure 2A). Most of the studies did not explain how they addressed missing data on the covariates (70%) (Figure 2B). When addressed (30%), 44% used complete case analysis and 10% last observation carried forward (LOCF). 25% of studies did not adjust for confounding factors and there was no clear correlation between the number of factors used to adjust and the number of participants in the studies. An important number of studies selected covariates using bivariate screening and/or stepwise selection. 86% of the studies did not acknowledge attrition (Figure 2C). When trying to correct for attrition (14%), 38% used non-responder (NR) imputation, 24% used LUNDEX1, a form of NR imputation, and 21% LOCF.Conclusion:Most of studies used multiple outcomes. However, the vast majority did not acknowledge missing data and attrition, and a quarter did not adjust for any confounding factors. Moreover, when attempting to account for attrition, several studies used methods which potentially increase bias (LOCF, complete case analysis, bivariate screening…). This systematic review confirms the need for the development of recommendations for the assessment and reporting of comparative drug effectiveness in observational data in rheumatology.References:[1]Kristensen et al. A&R. 2006 Feb;54(2):600-6.Acknowledgments:Support of the Standing Committee on Epidemiology and Health Services ResearchDisclosure of Interests:Kim Lauper: None declared, Joanna KEDRA: None declared, Maarten de Wit Grant/research support from: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Consultant of: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Speakers bureau: Dr. de Wit reports personal fees from Ely Lilly, 2019, personal fees from Celgene, 2019, personal fees from Pfizer, 2019, personal fees from Janssen-Cilag, 2017, outside the submitted work., Bruno Fautrel Grant/research support from: AbbVie, Lilly, MSD, Pfizer, Consultant of: AbbVie, Biogen, BMS, Boehringer Ingelheim, Celgene, Lilly, Janssen, Medac MSD France, Nordic Pharma, Novartis, Pfizer, Roche, Sanofi Aventis, SOBI and UCB, Thomas Frisell: None declared, Kimme Hyrich Grant/research support from: Pfizer, UCB, BMS, Speakers bureau: Abbvie, Florenzo Iannone Consultant of: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Speakers bureau: Speaker and consulting fees from AbbVie, Eli Lilly, Novartis, Pfizer, Roche, Sanofi, UCB, MSD, Pedro M Machado Consultant of: PMM: Abbvie, Celgene, Janssen, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Speakers bureau: PMM: Abbvie, BMS, Lilly, MSD, Novartis, Pfizer, Roche and UCB, Lykke Midtbøll Ørnbjerg Grant/research support from: Novartis, Ziga Rotar Consultant of: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Speakers bureau: Speaker and consulting fees from Abbvie, Amgen, Biogen, Eli Lilly, Medis, MSD, Novartis, Pfizer, Roche, Sanofi., Maria Jose Santos Speakers bureau: Novartis and Pfizer, Tanja Stamm Grant/research support from: AbbVie, Roche, Consultant of: AbbVie, Sanofi Genzyme, Speakers bureau: AbbVie, Roche, Sanofi, Simon Stones Consultant of: I have been a paid consultant for Envision Pharma Group and Parexel. This does not relate to this abstract., Speakers bureau: I have been a paid speaker for Actelion and Janssen. These do not relate to this abstract., Anja Strangfeld Speakers bureau: AbbVie, BMS, Pfizer, Roche, Sanofi-Aventis, Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Axel Finckh Grant/research support from: Pfizer: Unrestricted research grant, Eli-Lilly: Unrestricted research grant, Consultant of: Sanofi, AB2BIO, Abbvie, Pfizer, MSD, Speakers bureau: Sanofi, Pfizer, Roche, Thermo Fisher Scientific, Sytske Anne Bergstra: None declared, Delphine Courvoisier: None declared
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