A team at the University of Iowa has developed a complex Bayesian joint model to better understand the progress of infection in Lizmania. The model integrates longitudinal data and event time data, thus providing a comprehensive approach to studying the disease. The study has been published in PLOS ONE.
Dr. Felix Pabon-Rodriguez and his team, including Dr. Grant Brown, Dr. Breanna Scorza and Dr. Christine Petersen, have used the Bayesian statistical framework to explore the pathogen load, immune response, including the immune responses of antibody levels and disease progression. interaction.
The Bayesian joint model developed by the researchers combines data from a cohort of dogs naturally exposed to Lejiman. The model considers a variety of factors, including inflammatory and regulatory immune responses, providing a dynamic and comprehensive view of disease progression. By including measurements such as CD4+ and CD8+ T cell proliferation, as well as cytokine expression (such as interleukin 10 (IL-10) and interferon-γ (IFN-γ), this model captures the complexity of immune responses during infection sex.
Dr. Pabon-Rodriguez, now an assistant professor of biostatistics and health data science at Indiana University School of Medicine, highlights the importance of their findings: “Our model helps not only understand Lizhmanny The progress of subinfection and can also predict individual disease trajectories. This may help develop targeted therapies for canine lejima disease. “He further stressed: “By integrating multiple immune response variables, we can more accurately Predicting the outcome of the disease is crucial for timely and effective interventions.”
Importantly, the researchers’ findings suggest that high levels of Levitra-specific antibodies were observed in subjects with severe forms of disease, and that there is accumulated evidence that B cells and antibodies are associated with disease pathology Related. “By simultaneously fusion of CD4+ and CD8+ T cell variables, such as proliferation and cytokine expression, we were able to closely model disease progression in the real world,” said Dr. Pabon-Rodriguez. This detailed modeling approach emphasizes the The importance of immune response elements in disease progression and potential therapeutic outcomes.
The model also utilizes the longitudinal autorotational motion mean (ARMA) method to explain variability and pathogen dynamics within the host. This gives people a more nuanced understanding of how various factors interact to influence disease progression and survival outcomes. By including inflammatory and regulatory immune responses, the model provides insights into the delicate balance of the immune system when managing chronic infections such as Lejimann.
Dr. Pabon-Rodriguez highlights the broader implications of his work: “Our approach can adapt to other chronic infectious diseases, providing valuable tools for researchers in the field of infectious disease models.” The study shows how advanced statistical modeling Enhanced understanding of complex disease processes ultimately helps develop better therapeutic strategies.
In summary, this study marks a significant advance in the field of infectious disease modeling, especially for diseases with complex immune responses such as Levitra. The Bayesian joint model developed by the University of Iowa team provides a powerful framework for understanding disease progression and improving predictions of individual disease outcomes.
Journal Reference
Pabon-Rodriguez, FM, Brown, GD, Scorza, BM, Petersen, CA, “Long-Dimensional and Factual Bayesian Joint Modeling within Leishmania Infection Data.” PLOS ONE (2024).
doi: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0297175
About the Author
Felix Pabon-Rodriguez is an assistant professor in the Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine (IUSM). He graduated with a Ph.D. In May 2023, he has a degree in biostatistics from the University of Iowa and joined IUSM in July 2023. Dr. Pabon-Rodriguez chose Indiana University because of the unique research opportunities between the School of Medicine and the Fairbanks School of Public Health.
Felix’s biomedical research helps to advance understanding of infectious diseases and immune responses by applying Bayesian statistical methods. Some of his research work includes estimation of epidemiological parameters of Zika virus, studies on the immune system dynamics of visceral rijima and Lyme disease, and Bayesian joint models through longitudinal and survival data for common Effects of infection. In addition, he is interested in addressing health differences with particular concerns with infectious and non-communicable diseases.
Other interests revolve around promoting diversity, equity and inclusion in STEM education. He is committed to addressing the underrepresentation of minority students in the STEM subject and improving statistics and data science education.
