Characterization of the major histocompatibility complex locus association with Behçet’s disease in Iran

© 2015 Xavier et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Introduction: The aim of this study was to characterize the association of human leukocyte antigen (HLA) B alleles and major histocompatibility complex (MHC) single nucleotide polymorphisms (SNPs) with Behçet's disease (BD) in an Iranian dataset. Methods: The association of three SNPs in the MHC region previously identified as the most associated in high-density genotyping studies was tested in a case-control study on 973 BD patients and 825 controls from Iran, and the association of HLA-B alleles was tested in a subset of 681 patients and 414 controls. Results: We found that HLA-B*51 (P = 4.11 × 10(-41), OR [95% CI] = 4.63[3.66-5.85]) and B*15 confer risk for BD (P = 2.83 × 10(-2), OR [95% CI] = 1.75[1.08-2.84]) in Iranian, and in B*51 negative individuals, only the B*15 allele is significantly associated with BD (P = 2.51 × 10(-3), OR [95% CI] = 2.40[1.37-4.20]). rs76546355, formerly known as rs116799036, located between HLA-B and MICA (MHC class I polypeptide-related sequence A), demonstrated the same level of association with BD as HLA-B*51 (P adj = 1.78 × 10(-46), OR [95% CI] = 5.46[4.21-7.09], and P adj = 8.34 × 10(-48), OR [95% CI] = 5.44[4.20-7.05], respectively) in the HLA-B allelotyped subset, while rs2848713 was less associated (P adj = 7.14 × 10(-35), OR [95% CI] = 3.73[2.97-4.69]) and rs9260997 was not associated (P adj = 1.00 × 10(-1)). Additionally, we found that B*51 genotype-phenotype correlations do not survive Bonferroni correction, while carriers of the rs76546355 risk allele predominate in BD cases with genital ulcers, positive pathergy test and positive BD family history (2.31 × 10(-4) ≤ P ≤ 1.59 × 10(-3)). Conclusions: We found that the HLA-B*51 allele and the rs76546355/rs116799036 MHC SNP are independent genetic risk factors for BD in Iranian, and that positivity for the rs76546355/rs116799036 risk allele, but not for B*51, does correlate with specific demographic characteristics or clinical manifestations in BD patients.This work was supported by the Portuguese Fundação para a Ciência e a Tecnologia (grants PTDC/SAU-GMG/098937/2008, PTDC/IIM-GES/5015/2012 and CMUP-ERI/TPE/0028/2013, fellowships SFRH/BD/43895/2008 to JMX, SFRH/BPD/35737/2007 to PA, SFRH/BPD/70008/2010 to IS, a Ciência and an Investigator-FCT contract to SAO), and the Research Committee of the Tehran University of Medical Sciences (grant 132/714).info:eu-repo/semantics/publishedVersio

Revolutionizing Kidney Transplantation: Connecting Machine Learning and Artificial Intelligence with Next-Generation Healthcare—From Algorithms to Allografts

This review explores the integration of artificial intelligence (AI) and machine learning (ML) into kidney transplantation (KT), set against the backdrop of a significant donor organ shortage and the evolution of ‘Next-Generation Healthcare’. Its purpose is to evaluate how AI and ML can enhance the transplantation process, from donor selection to postoperative patient care. Our methodology involved a comprehensive review of current research, focusing on the application of AI and ML in various stages of KT. This included an analysis of donor–recipient matching, predictive modeling, and the improvement in postoperative care. The results indicated that AI and ML significantly improve the efficiency and success rates of KT. They aid in better donor–recipient matching, reduce organ rejection, and enhance postoperative monitoring and patient care. Predictive modeling, based on extensive data analysis, has been particularly effective in identifying suitable organ matches and anticipating postoperative complications. In conclusion, this review discusses the transformative impact of AI and ML in KT, offering more precise, personalized, and effective healthcare solutions. Their integration into this field addresses critical issues like organ shortages and post-transplant complications. However, the successful application of these technologies requires careful consideration of their ethical, privacy, and training aspects in healthcare settings