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Sökning: WFRF:(Rosa PG)

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  • Glasbey, JC, et al. (författare)
  • 2021
  • swepub:Mat__t
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  • Bravo, L, et al. (författare)
  • 2021
  • swepub:Mat__t
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  • Tabiri, S, et al. (författare)
  • 2021
  • swepub:Mat__t
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  • Cenit, MC, et al. (författare)
  • Influence of the IL6 gene in susceptibility to systemic sclerosis
  • 2012
  • Ingår i: The Journal of rheumatology. - : The Journal of Rheumatology. - 0315-162X .- 1499-2752. ; 39:12, s. 2294-2302
  • Tidskriftsartikel (refereegranskat)abstract
    • Systemic sclerosis (SSc) is a genetically complex autoimmune disease; the genetic component has not been fully defined. Interleukin 6 (IL-6) plays a crucial role in immunity and fibrosis, both key aspects of SSc. We investigated the influence of IL6 gene in the susceptibility and phenotype expression of SSc.Methods.We performed a large metaanalysis including a total of 2749 cases and 3189 controls from 6 white populations (Germany, The Netherlands, Norway, Spain, Sweden, and United Kingdom). Three IL6 single-nucleotide polymorphisms (SNP; rs2069827, rs1800795, and rs2069840) were selected by SNP tagging and genotyped using TaqMan® allele discrimination technology.Results.Individual SNP metaanalysis showed no evidence of association of the 3 IL6 genetic variants with the global disease. Phenotype analyses revealed a significant association between the minor allele of rs2069840 and the limited cutaneous SSc clinical form (Bonferroni p = 0.036, OR 1.14, 95% CI 1.04–1.25). A trend of association between the minor allele of the rs1800795 and the diffuse cutaneous SSc clinical form was also evident (Bonferroni p = 0.072, OR 0.86, 95% CI 0.77–0.96). In the IL6 allelic combination analyses, the GGC allelic combination rs2069827-rs1800795-rs2069840 showed an association with overall SSc (Bonferroni p = 0.016, OR 1.13, 95% CI 1.04–1.23).Conclusion.Our results suggest that the IL6 gene may influence the development of SSc and its progression.
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  • Nunes, A, et al. (författare)
  • Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group
  • 2020
  • Ingår i: Molecular psychiatry. - : Springer Science and Business Media LLC. - 1476-5578 .- 1359-4184. ; 25:9, s. 2130-2143
  • Tidskriftsartikel (refereegranskat)abstract
    • Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47–67.00, ROC-AUC = 71.49%, 95% CI = 69.39–73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70–60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen’s Kappa = 0.83, 95% CI = 0.829–0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.
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  • Niemi, MEK, et al. (författare)
  • 2021
  • swepub:Mat__t
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