@article{103687,
  keywords = {Genetic Variation, TLR2},
  author = {Mukherjee S and Ghosh S and Kumar Roy P},
  title = {Decoding TLR2-mediated Genetic Variability in Leprosy: A Stochastic Foundation Integrating Molecular, Topological and Deep Learning Insights to Investigate the Inherent Uncertainties},
  abstract = {<p>Leprosy exhibits substantial clinical variability, ranging from localized immune-controlled forms to progressive nerve-damaging disease. Although Toll-like receptor 2 (<em>TLR2</em>) variability has been associated with susceptibility to&nbsp;<em>Mycobacterium leprae</em>&nbsp;infection, its role in fluctuation-driven immune dynamics remains incompletely understood. In this study, we develop a nonlinear stochastic differential equation (SDE) framework to investigate&nbsp;<em>TLR2</em>-associated immune variability in leprosy progression. The proposed stochastic model incorporates multiplicative Wiener-driven perturbations to capture immune fluctuations during host–pathogen dynamics. Analytical results establish global positivity and ergodic stationary behavior of the system, indicating the possibility of long-term stochastic persistence. Numerical simulations reveal bimodal pathogen-load distributions and variability in cytokine and infected Schwann cell dynamics while recurrent topological immune patterns are identified through topological data analysis. Integrating stochastic analysis with topological, molecular, and neural-network-based approaches, the proposed framework further demonstrates strong performance in classifying clinical subtypes of leprosy. Overall, the study provides a systems-level computational framework for investigating immune variability and stochastic persistence in chronic infectious diseases.</p>
},
  year = {2026},
  journal = {Communications in Nonlinear Science and Numerical Simulation},
  month = {06/2026},
  publisher = {Elsevier BV},
  issn = {1007-5704},
  doi = {10.1016/j.cnsns.2026.110434},
  language = {ENG},
}