Data-driven Atlas of Acinetobacter baumannii vaccine research: a multi-database bibliometric and visualization analysis.

Authors

Shan Li, Xinyu Ju, Xingyu Zhu, Fan Fei, Xiaoqin Jin, Xingran Du, Yuting Shi

Published in

Frontiers in cellular and infection microbiology. Volume 16. Pages 1922186. Epub Sep 11, 2026.

Abstract

Acinetobacter baumannii is a major multidrug-resistant pathogen for which effective preventive strategies are urgently needed. This study aimed to characterize the global research landscape, knowledge structure, and emerging trends in Acinetobacter baumannii vaccine research.
Publications from 2011 to 2025 were retrieved from the Web of Science Core Collection and Scopus. After integration, deduplication, and independent screening, 243 publications were included. Bibliometric and visualization analyses were performed using bibliometrix, CiteSpace, and VOSviewer.
Research output showed an overall nonlinear increase, with an annual growth rate of 18.68%. Iran, China, and the United States were the leading contributors, whereas international collaboration accounted for 19.75% of publications. Keyword analysis identified seven major thematic clusters centered on outer membrane vesicles, bacterial antigens, antimicrobial resistance, computational approaches, and immunization strategies. Temporal analyses showed increasing attention to bioinformatics, reverse vaccinology, peptide/epitope-based design, and virulence-associated targets. Citation analysis further highlighted OMV/OMC-based vaccination and several defined antigens as important components of the field's intellectual foundation.
Overall, Acinetobacter baumannii vaccine research has progressively diversified toward increasingly defined, multicomponent, and computationally guided strategies. However, antigenic heterogeneity, limited cross-strain validation, and variability in preclinical evaluation remain major translational challenges. Future efforts should emphasize conserved antigen prioritization, standardized validation, and broader international collaboration to advance promising candidates toward clinical application.

PMID:
42798310
Bibliographic data and abstract were imported from PubMed on 26 Sep 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.