Evidence map›Paper›PMID 38515855›Full record

ReviewFrontiers in pharmacology2024

Hyperuricemia research progress in model construction and traditional Chinese medicine interventions.

Hongyan Zhou, Jingyi Yang, Xiaoqing Yuan, Xinyu Song, Xingcai Zhang, Ting Cao, Jiayu Zhang

Abstract readReview
In one paragraph

Review in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing 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

26 citing papers in PubMed.

  1. Review
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  6. Review
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  8. Multiscale Construction, Evaluation, and Application of Organoids.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    Review
  9. Article
  10. Article
  11. Review
  12. Review
  13. Article
  14. Frontiers in nutrition · 2025
    Article
  15. Review
  16. Review
  17. Article
  18. Article
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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

7 authors.

Hongyan Zhou *Institute of Chinese Medicine, Binzhou Medical University, Yantai, Shandong, China.
Jingyi Yang *Institute of Chinese Medicine, Binzhou Medical University, Yantai, Shandong, China.
Xiaoqing YuanInstitute of Chinese Medicine, Binzhou Medical University, Yantai, Shandong, China.
Xinyu SongInstitute of Chinese Medicine, Binzhou Medical University, Yantai, Shandong, China.
Xingcai ZhangWorld Tea Organization, Cambridge, MA, United States.
Ting CaoThe First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China.
Jiayu ZhangInstitute of Chinese Medicine, Binzhou Medical University, Yantai, Shandong, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hyperuricemia (HUA), a severe metabolic disease derived from purine metabolism disorder, will lead to abnormally increased serum uric acid (SUA) levels in the body. Studies have shown that HUA is highly related to gout, hypertension, diabetes, coronary heart disease, chronic kidney diseases, and so on. Traditional Chinese medicine (TCM) shows excellent results in treating HUA because of its unique advantages of multi-metabolites and multi-targets. This article reports on the use of TCM components for uric acid (UA)-lowering activity with excellent efficacy and low side effects based on established HUA models. This work summarizes the advantages and limitations of various HUA disease models for efficacy evaluation. Applications of TCM in HUA treatment have also been discussed in detail. This paper reveals recent research progress on HUA in constructing evaluation models and systematic TCM interventions. It will provide a scientific reference for establishing the HUA model and suggest future TCM-related HUA studies.

Indexed as

efficacy evaluationhyperuricemiahyperuricemia modelstraditional Chinese medicineurate lowering

Identifiers

PMID38515855
PMCPMC10955118

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Registered trials

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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.