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Critical Reviews™ in Immunology

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ISSN Imprimer: 1040-8401

ISSN En ligne: 2162-6472

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.3 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 2.6 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00079 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.24 SJR: 0.429 SNIP: 0.287 CiteScore™:: 2.7 H-Index: 81

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Pathophysiology, Etiology, Epidemiology of Type 1 Diabetes and Computational Approaches for Immune Targets and Therapy

Volume 39, Numéro 4, 2019, pp. 239-265
DOI: 10.1615/CritRevImmunol.2019033126
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RÉSUMÉ

Autoimmune diseases occur when the body's natural defense system fails to differentiate its own cells from the foreign cells and mistakenly attacks the healthy cells. Among the autoimmune diseases, the most common serious disease is the type 1 diabetes (T1D). Biomarkers like c-peptide, autoantibodies, and glycated molecules are now widely used for the early diagnosis of diabetes. However, the diverse nature of biomarkers and the available autoantibodies as biomarkers are not enough to differentiate the heterogeneity inherent in T1D. Novel biomarkers have allowed the introduction of bioinformatics for assimilating the new data into clinical tools. Computer-aided drug design contributes to the discovery of novel autoantibodies, and molecular docking promises to enhance it. Moreover, the study of the pathophysiology of diabetes via molecular simulation has been proposed. In this review article, we focus on the characterization of the etiology, epidemiological factors, and mechanisms of hyperglycemia that induce cellular damage due to oxidative stress and proinflammatory responses. We also decribe novel biomarkers used for the detection of β-cell destruction and diagnosis at early stages. Bioinformatics tools including molecular docking, sequence alignment, and homology modeling are also presented. This report supports researchers in drug design, in disease detection at an early phase, and in therapy development for T1D-associated complications.

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  1. Campanella Sara, Sabbatini Luisiana, Cherubini Valentino, Tiberi Valentina, Marino Monica, Pierleoni Paola, Belli Alberto, Boccolini Giada, Palma Lorenzo, Machine Learning Approach for Care Improvement of Children and Youth with Type 1 Diabetes Treated with Hybrid Closed-Loop System, Electronics, 11, 14, 2022. Crossref

  2. Ibrahim Sohayla A., El Hajj Maguy Saffouh, Owusu Yaw B., Al-Khaja Maryam, Khalifa Amel, Ahmed Dalia, Awaisu Ahmed, Adherence as a Predictor of Glycemic Control Among Adolescents With Type 1 Diabetes: A Retrospective Study Using Real-world Evidence, Clinical Therapeutics, 44, 10, 2022. Crossref

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