Evidence map›Paper›PMID 41059088›Full record

ArticleJournal of multidisciplinary healthcare2025

A Bibliometric Analysis of Research on Natural Medicines for Depression from 2004 to 2024.

Linsun Lin, Ziyi Guo, Qiuzhong Zhan, Peigang Fang, Haojun Shi, Yanchen Feng, Sheng Tian, Lu Xiao, Min Chen, Tao Wang

Abstract read
In one paragraph

Article in Journal of multidisciplinary healthcare, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Linsun Lin *Faculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.ORCID 0009-0002-3295-0556
Ziyi Guo *Department of Rehabilitation Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, People's Republic of China.
Qiuzhong ZhanFaculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.
Peigang FangFaculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.
Haojun ShiFaculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.ORCID 0009-0008-4355-2039
Yanchen FengDepartment of Neuropsychiatry and Psychology, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, 450000, People's Republic of China.ORCID 0009-0006-7880-6903
Sheng TianFaculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.
Lu XiaoZhuhai Campus, Zunyi Medical University, Zhuhai, 519041, People's Republic of China.
Min ChenFaculty of Chinese Medicine and State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology, Macau, Macau SAR, 999078, People's Republic of China.
Tao WangEncephalopathy Center, The Second Affiliation Hospital of Anhui University of Chinese Medicine, Hefei, 230000, People's Republic of China.ORCID 0009-0005-7787-9943

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Depression imposes a severe global health burden. Natural medicines offer multi-target therapeutic potential, but a comprehensive bibliometric assessment of this field is lacking. This study analyzes the research landscape from 2004 to 2024 to map global contributions, collaboration networks, and evolving trends, thereby providing an evidence-based roadmap for future research. Methods: We systematically retrieved publications on natural product-based medicines for depression from the Web of Science Core Collection (WoSCC) between 2004 and 2024. Bibliometric analysis was performed using CiteSpace, VOSviewer, and the R package "bibliometrix" to examine publication trends, collaboration networks, co-citation patterns, and keyword hotspots. Results: Our analysis of 3,610 publications from 92 countries/regions revealed China's dominant leadership in both productivity and collaborative influence. The Beijing University of Chinese Medicine was identified as the most productive institution, while the Journal of Ethnopharmacology and Phytomedicine were the most cited journals. Key prolific authors included Qin Xuemei and Zhang Yi, and foundational works by researchers such as Porsolt and Sarris Jerome were the most frequently cited. Keyword clustering analysis identified three major research themes: mechanistic investigations (eg, "neuroinflammation", "oxidative stress"), phytochemical analysis (eg, "flavonoids", "alkaloids"), and clinical applications (eg, "randomized controlled trial"). The field has evolved from foundational preclinical models toward a stronger focus on clinical translational research. Conclusion: This bibliometric study highlights China's central role in the research on natural medicines for depression and delineates the evolution of major research themes. The findings underscore the necessity for enhanced international collaboration and more rigorous clinical trials to validate the efficacy of bioactive compounds, which is crucial for advancing novel antidepressant drug development.

Indexed as

bibliometriccitespacedepressionnatural medicinesVOSviewer

Identifiers

PMID41059088
PMCPMC12499365

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.