Global trends and characteristics of metal–organic frameworks in cancer research: a machine-learning-based bibliometric analysis
Résumé fourni par la source
BACKGROUND: Cancer poses a significant health threat, causing millions of deaths annually. Although chemotherapy-based comprehensive therapies are common, their low accuracy and severe side effects limit effectiveness. Metal-organic frameworks (MOFs), with their superior biocompatibility and stability, show great promise for drug delivery and cancer treatment. This study aims to explore the potential and developmental trajectories of MOFs in cancer research through a bibliometric analysis. METHODS: The Web of Science Core Collection was searched for documents from its inception in 2009 to December 31, 2023. We analyzed and visualized document types, countries, institutions, authors, journals, references, and keywords using the Bibliometrix package, dplyr, sankeywheel, term extraction, and ggplot2. Additionally, the Latent Dirichlet Allocation (LDA) algorithm was employed for detailed semantic analysis, uncovering latent thematic distributions. RESULTS: A total of 7106 authors from 1591 institutions across 45 countries contributed 1955 papers on MOFs in cancer research, published in 327 journals. China leads in research output and international collaboration, with the Chinese Academy of Sciences as the top institution. Lin Wenbin from the University of Chicago is the most influential author, and ACS Applied Materials & Interfaces is the most active journal. MOFs are predominantly studied for breast cancer, followed by lung and liver cancers. Drug delivery remains a focal point for future research. CONCLUSIONS: This study provides a comprehensive overview of the research landscape on MOFs in cancer treatment, offering insights into key trends and future directions, particularly in drug delivery and disease-specific applications.
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Contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Global trends and characteristics of metal–organic frameworks in cancer research: a machine-learning-based bibliometric analysis
- Date Crossref
- 01/06/2025
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.
Institutions déclarées
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