Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms.

Machine learning (ML) algorithms can handle complex genomic data and identify predictive patterns that may not be apparent through traditional statistical methods. They become popular tools for medical applications including prediction, diagnosis or treatment of complex diseases like rheumatoid arthritis (RA). RA is an autoimmune disease in which genetic factors play a major role.

SNP in , and but Not and Increase the Risk of Rheumatoid Arthritis in Polish Population.

Single nucleotide polymorphisms in non- genes are involved in the development of rheumatoid arthritis (RA). SNPS in genes: (), (), (), (), and () have been described as risk factors for the development of autoimmune diseases, including RA. This study aimed to assess the prevalence of polymorphisms of these genes in the Polish population of patients with rheumatoid arthritis as compared to healthy controls.

Polymorphisms in DNA Repair Genes and Association with Rheumatoid Arthritis in a Pilot Study on a Central European Population.

Rheumatoid arthritis (RA) is a chronic, multifactorial autoimmune disease characterized by chronic arthritis, a tendency to develop joint deformities, and involvement of extra-articular tissues. The risk of malignant neoplasms among patients with RA is the subject of ongoing research due to the autoimmune pathogenesis that underlies RA, the common etiology of rheumatic disease and malignancies, and the use of immunomodulatory therapy, which can alter immune system function and thus increase the risk of malignant neoplasms. This risk can also be increased by impaired DNA repair efficiency in individuals with RA, as reported in our recent study.

Construction of an In Vitro Air-Liquid Interface Exposure System to Assess the Toxicological Impact of Gas and Particle Phase of Semi-Volatile Organic Compounds.

Anthropogenic activities and industrialization render continuous human exposure to semi-volatile organic compounds (SVOCs) inevitable. Occupational monitoring and safety implementations consider the inhalation exposure of SVOCs as critically relevant. Due to the inherent properties of SVOCs as gas/particle mixtures, risk assessment strategies should consider particle size-segregated SVOC association and the relevance of released gas phase fractions.