Albertine Donker

Chapter 4 166 Hepcidin in chronic kidney disease: not an anaemia management tool, but promising as a cardiovascular biomarker. The Netherlands journal of medicine. 2015;73(3):108-118. 42. Gualdi R, Casalgrandi G, Montosi G, Ventura E, Pietrangelo A. Excess iron into hepatocytes is required for activation of collagen type I gene during experimental siderosis. Gastroenterology. 1994;107(4):1118-1124. 43. Pietrangelo A, Montosi G, Totaro A, et al. Hereditary hemochromatosis in adults without pathogenic mutations in the hemochromatosis gene. The New England journal of medicine. 1999;341(10):725-732. 44. Pietrangelo A, Caleffi A, Corradini E. Non- HFE hepatic iron overload. Seminars in liver disease 2011;31(3):302-318. 45. Yilmaz-Keskin E, Sal E, de Falco L, et al. Is the acronym IRIDA acceptable for slow responders to iron in the presence of TMPRSS6 mutations? The Turkish journal of pediatrics 2013;55(5):479-484. 46. Guillem F, Kannengiesser C, Oudin C, et al. Inactive matriptase-2 mutants found in IRIDA patients still repress hepcidin in a transfection assay despite having lost their serine protease activity. Human mutation 2012;33(9):1388-1396. 47. Origa R, Cazzola M, Mereu E, et al. Differences in the erythropoiesis-hepcidin-iron store axis between hemoglobin H disease and beta- thalassemia intermedia. Haematologica 2015;100(5):e169-171. 48. Nemeth E. Hepcidin and beta-thalassemia major. Blood 2013;122(1):3-4. 49. Gardenghi S, Ramos P, Marongiu MF, et al. Hepcidin as a therapeutic tool to limit iron overload and improve anemia in beta- thalassemic mice. The Journal of clinical investigation 2010;120(12):4466-4477. 50. Ginzburg Y, Rivella S. beta-thalassemia: a model for elucidating the dynamic regulation of ineffective erythropoiesis and iron metabolism. Blood 2011;118(16):4321-4330. 51. Clark MA, Goheen MM, Fulford A, et al. Host iron status and iron supplementation mediate susceptibility to erythrocytic stage Plasmodium falciparum. Nature communications 2014;5:4446. 52. Mathe E, Olivier M, Kato S, et al. Computational approaches for predicting the biological effect of p53 missense mutations: a comparison of three sequence analysis based methods. Nucleic acids research 2006;34(5):1317-1325. 53. Adzhubei I, Jordan DM, Sunyaev SR. Predicting functional effect of human missense mutations using PolyPhen-2. Curr Protoc Hum Genet 2013;Chapter 7:Unit7.20. 54. Flanagan SE, Patch AM, Ellard S. Using SIFT and PolyPhen to predict loss-of-function and gain-of-function mutations. Genet Test Mol Biomarkers 2010;14:533-537

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