ReviewPharmaceutical biology2024
Bibliometric analysis of research trends and topic areas in traditional Chinese medicine therapy for lymphoma.
Review in Pharmaceutical biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Data mining and experimental validation identify a core herb combination as a therapeutic agent for malignant lymphoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Mechanism identification of an herbal decoction for potential treatment of diffuse large B-cell lymphoma using network pharmacology and experimental validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Bibliometric analysis of anti-angiogenic therapy in gastric cancer: research hotspots, trends, and emerging frontiers.International journal of clinical pharmacy · 2026Article
- A scientometric study on research trends and characteristics of lymphoma involving the oral cavity.Journal of dental sciences · 2026Article
- Mapping the landscape and research trend of imaging diagnosis in lymphoma: a bibliometric analysis from 1976 to 2024.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
contextTraditional Chinese Medicine (TCM) is effective as a cancer treatment modality. However, this is the first bibliometric analysis of TCM in lymphoma treatment.
objectiveThis study explores the current trends and research topics of TCM in treating lymphoma from 2000 to 2023. MATERIALS AND
methodsWe searched within the Web of Science Core Collection (WoSCC) for publications on TCM in lymphoma treatment, spanning 2000 to 2023. Subsequently, we employed a comprehensive approach utilizing CiteSpace software and VOSviewer to visually analyze research trends, authors, institutions, co-cited references, and keywords.
resultsFrom January 1, 2000, to August 31, 2023, annual scientific publications on TCM for lymphoma treatment have steadily increased. Among the leading institutions in this field, the Beijing University of Chinese Medicine and the Fujian Medical University occupied the top positions. Regarding the authors, Jun Peng, Jiumao Lin, and Hongwei Chen emerged as the top three contributors. In the co-citation analysis of references, the top three co-cited references were authored by Hanahan D, Elmore S, and Livak KJ with citations numbered 13, 14, and 17, respectively. In particular, keywords reflecting current emerging trends included 'pathway', 'traditional Chinese medicine', 'oxidative stress', and 'macrophage polarization'. DISCUSSION AND
conclusionsThis bibliometric analysis provides a comprehensive overview of TCM for lymphoma treatment. This analysis identified the predominant trends and research topics in the field. The findings are expected to be of significant value for researchers who focus on TCM in lymphoma treatment, helping them better understand the development of this field.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.