ReviewJournal of multidisciplinary healthcare2024
The Traditional Chinese Medicine in Treating Diabetic Nephropathy: A Bibliometric Analysis.
Review in Journal of multidisciplinary healthcare, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Article
- Visualization Analysis and Knowledge Structure Evolution of Hydrogel Applications in Dentistry.International dental journal · 2026Article
- Gut Microbiota Dysbiosis and Metabolite Imbalance Mediate Diabetic Kidney Disease Inflammation: Mechanisms and Intervention Strategies Targeting Gut-Kidney Axis and NF-κB/NLRP3 Pathways.Journal of inflammation research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Diabetic nephropathy (DN) is one of the most common complications of diabetes mellitus, causing a serious economic burden worldwide. Traditional Chinese Medicine (TCM) is playing an increasingly important role in the treatment of DN. The purpose of this study was to comprehend the main themes and developments in relevant research throughout the last 11 years. Methods: We looked for publications in the Web of Science Core Collection database (WOS) and the Chinese National Knowledge Infrastructure (CNKI) between 2013 and 2024. CiteSpace 6.3.R1 software was used to analyze the co-authorship of countries/regions, organizations, and co-occurrence of keywords. In addition, burst detection was applied to predict research hotspots and future trends. Results: A total of 530 articles were included. The overall trend of published articles is increasing. China is the country with the highest number of publications and the highest impact. The research institutions are relatively scattered, with Beijing University of Traditional Chinese Medicine having the highest number of publications. The cooperation among institutions is mainly between universities of traditional Chinese medicine and their affiliated hospitals, and the cross-regional cooperation is not common. The research hotspots are the efficacy of TCM on DN and its mechanism of action, the exploration of TCM and formulas related to the treatment of DN, and the identification and typing of DN in TCM. The research frontiers lie in the control of oxidative stress and the effects of TCM on gut microbiota. In addition, the use of network pharmacology to explore the targets of Chinese herbal formulas for the treatment of this disease has also become popular. Conclusion: TCM provides more possibilities for the treatment of DN. Researchers can refer to the research hotspots and trends in this paper for future research direction, on the one hand, they can focus on the study of the clinical efficacy of TCM and its improvement of renal function, on the other hand, they can also start from the pharmacological mechanism of TCM for the treatment of DN. Among them, improving oxidative stress in human body and regulating gut microbiota are the directions that can be studied.
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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.