ArticlePloS one2020
Comprehensive analysis of mitochondrial and nuclear DNA variations in patients affected by hemoglobinopathies: A pilot study.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- Comparison between Sickle Cell Disease Patients and Healthy Donors: Untargeted Lipidomic Study of Erythrocytes.International journal of molecular sciences · 2023Article
- Mitochondria: Emerging Consequential in Sickle Cell Disease.Journal of clinical medicine · 2023Review
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Authors and funding
13 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The hemoglobin disorders are the most common single gene disorders in the world. Previous studies have suggested that they are deeply geographically structured and a variety of genetic determinants influences different clinical phenotypes between patients inheriting identical β-globin gene mutations. In order to get new insights into the heterogeneity of hemoglobin disorders, we investigated the molecular variations on nuclear genes (i.e. HBB, HBG2, BCL11A, HBS1L and MYB) and mitochondrial DNA control region. This pilot study was carried out on 53 patients belonging to different continents and molecularly classified in 4 subgroup: β-thalassemia (β+/β+, β0/β0 and β+/β0)(15), sickle cell disease (HbS/HbS)(20), sickle cell/β-thalassemia (HbS/β+ or HBS/β0)(10), and non-thalassemic compound heterozygous (HbS/HbC, HbO-Arab/HbC)(8). This comprehensive phylogenetic analysis provided a clear separation between African and European patients either in nuclear or mitochondrial variations. Notably, informing on the phylogeographic structure of affected individuals, this accurate genetic stratification, could help to optimize the diagnostic algorithm for patients with uncertain or unknown origin.
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