Vaishali Bhagwani

VEXAS syndrome (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic syndrome) is a more recently described, acquired somatic mutation in the UBA1 gene, and is an autoinflammatory disorder of adults. Its clinical features include systemic inflammation, clonal hematopoiesis, abnormalities of the bone marrow and various hematological, rheumatological and dermatologic changes. This review discusses the molecular pathogenesis of VEXAS syndrome with emphasis on the UBA1 mutations, immune dysfunction and disease pathogenesis. In addition, it emphasizes the importance of artificial intelligence, next generation sequencing (NGS), integration of multi-omics and three-dimensional (3D) genomic fingerprinting in the improvement of diagnosis and molecular characterization. The current therapeutic options such as the use of corticosteroids, JAK inhibitors, azacitidine and biologic agents, as well as novel stem cell-based therapies are reviewed. Special focus is on the most promising potentially curative therapy, namely, allogeneic hematopoietic stem cell transplantation (HSCT). Although genomic medicine and precision diagnostics has come a long way, there are still issues to be addressed, particularly with respect to diagnosis at an early stage and longer-term management. The synergy of genomics, artificial intelligence (AI), and regenerative medicine could have a profound impact on the care of VEXAS syndrome patients in the future.

This current study focuses on the impact of Artificial Intelligence (AI) on the rapid identification of new molecules to suppress aging processes, which increases the proportion of healthspan relative to lifespan. The research approach taken involved a quantitative method, where artificial intelligence-based machine learning, bioinformatics, and statistical analysis were used alongside computational molecular docking. Biochemical information from databases such as PubChem, DrugBank, and ChEMBL was leveraged to screen and analyze molecular data. It was observed that AI-assisted predictive models, especially Deep Learning Neural Networks, offered highly accurate predictions concerning the biological activity of anti-aging compounds. The selected molecules showed considerable decreases in oxidative stress, inflammation, and cellular senescence markers, coupled with improved mitochondrial function and cell repair. Moreover, quantitative results showed that the use of AI for predicting the efficacy of anti-aging agents led to more significant healthspan enhancements than lifespan increases.