Quantification and clustering of immune states in hepatitis B Cirrhosis

Hepatitis B Cirrhosis, a severe progression of chronic Hepatitis B infection, requires a comprehensive understanding of the interplay among lymphocyte populations. This study aims to quantify and visualize the relationships among T cells, NK cells, and B cells to aid in assessing immune status, diagnosing the condition, and optimizing treatment strategies.

Peripheral blood samples were collected from 500 patients diagnosed with Hepatitis B Cirrhosis and 500 healthy controls. Sort visualization analysis and three-dimensional numerical fitting were performed to establish a mathematical model describing the relationships among lymphocyte subsets. Self-Organizing Feature Maps (SOFM) were employed for unsupervised clustering to identify distinct immune states.

Sort visualization analysis revealed a gradual decrease in T + NK cell levels as B cell levels increased, demonstrating a clear inverse relationship. SOFM clustering identified three distinct clusters with well-defined boundaries. In the 3D lymphocyte plane described by the eq. T percentage =  --0.9879 × B percentage - 1.041 × NK percentage + 97.66, a significant contrast was observed between Hepatitis B Cirrhosis samples and the healthy sample baseline. Analysis across Child-Pugh grades uncovered a cyclical pattern in immune states, reflecting the various stages of the viral infection process.

This study provides a quantitative mathematical model and visual representation of lymphocyte population dynamics in Hepatitis B Cirrhosis. The identification of distinct immune states associated with disease progression facilitates the assessment of immunological condition and the optimization of treatment strategies. The integration of immunological and clinical data opens new possibilities for more precise disease staging and personalized.