SynthesisFrontiers in immunology2025
Bibliometric analysis of traditional Chinese medicine for viral infections through immune modulation (2015-2025).
Synthesis in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled 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.
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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global trends and neurobiological frontiers of manual therapy in sleep disorders: integrating bibliometrics with clinical evidence.Frontiers in psychiatry · 2026Pooled it
- Mapping the scientific research on geriatric nutritional assessment (2016-2025): A bibliometric analysis.Asia Pacific journal of clinical nutrition · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: The immunomodulatory properties of traditional Chinese medicine (TCM) have attracted significant attention as a strategy for addressing viral infections. However, a comprehensive bibliometric analysis is still lacking. This study aims to systematically identify research trends, knowledge hotspots, and emerging themes in TCM applications for viral infections through immune modulation from 2015 to 2025. Methods: We collected publications from the Web of Science database from 2015 to 2025 and performed a comprehensive analysis using R, VOSviewer, and CiteSpace. In addition, clinical trial records published during this period were obtained from the PubMed database to assess clinical advancements in this field. Results: A total of 3,370 publications were analyzed in this study. Between 2015 and 2021, the number of publications in this field showed two distinct stepwise increases, separated by a period of relative stability, followed by a modest decline from 2021 to 2025. China contributed the highest volume of publications and demonstrated the broadest international collaborations, establishing itself as the leading country in this area. Frontiers in Immunology published the largest number of articles, while the Journal of Virology was the most frequently cited journal. Core topics included "Infection," "COVID-19," "Expression," "Antiviral," and "Protein." The primary research focus centered on TCM's antiviral effects and its modulation of immune responses, investigating its regulatory impact on inflammation and cytokine storms during viral infections, and examining TCM's role in modulating immune responses to viral vaccines. Clinical trials in this field focus on improving the management of viral infections, and immune reconstitution strategies for chronic infections. Conclusion: This study systematically analyzes the scientific literature in this field, providing valuable insights into current research trends and highlighting future directions in the application of TCM to the immunomodulation of viral infections.
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.