Bibliometric and LDA analysis of acute rejection in liver transplantation: Emerging trends, immunotherapy challenges, and the role of artificial intelligence.

Abstract

With the rising demand for liver transplantation (LT), research on acute rejection (AR) has become increasingly diverse, yet no consensus has been reached. This study presents a bibliometric and latent Dirichlet allocation (LDA) topic modeling analysis of AR research in LT, encompassing 1399 articles. The United States, Zhejiang University, and the University of California, San Francisco emerged as leading contributors, while Levitsky J and Uemoto SJ were key researchers. The most influential journals included the American Journal of Transplantation, Journal of Hepatology, and Transplantation. The analysis reveals a transition from traditional histological assessments to molecular diagnostics, genetic and epigenetic profiling, and noninvasive biomarkers such as donor-derived cell-free DNA (dd-cfDNA) and microRNAs. Advances in immune checkpoint inhibitors (ICIs), cell-based therapies (Tregs, mesenchymal stem cells (MSCs)), AI-guided immunosuppression, and nanoparticle-mediated drug delivery systems reflect a growing emphasis on precision medicine. In addition, recent exploration of microbiome-based therapies and regenerative medicine, including MSCs and their extracellular vesicles, offers promising new avenues for reducing long-term immunosuppressive drug dependency and enhancing graft survival. These developments not only improve early AR detection and personalized treatment but also reduce toxicity, foster immune tolerance, and expand the scope of individualized therapeutic options. Global collaboration, supported by cutting-edge research and AI-driven decision-making, remains essential for refining AR strategies, improving graft survival, and achieving better long-term patient outcomes.