Evidence map›Paper›PMID 30524499›Full record

ReviewChinese medicine2018

Application of metabolomics in toxicity evaluation of traditional Chinese medicines.

Li Duan, Long Guo, Lei Wang, Qiang Yin, Chen-Meng Zhang, Yu-Guang Zheng, E-Hu Liu

Abstract readReview
In one paragraph

Review in Chinese medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.

0numbers the graph read from it
0cells of the map it votes in
23citing 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

23 citing papers in PubMed.

  1. Article
  2. Article
  3. Dynamic Chemical Profiling ofFoods (Basel, Switzerland) · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Frontiers in pharmacology · 2022
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Deciphering the mechanism of action of antitubercular compounds with metabolomics.Computational and structural biotechnology journal · 2021
    Review
  20. Review
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.

Li Duan *1College of Chemistry and Material Science, Hebei Normal University, Shijiazhuang, 050024 China.
Long Guo *2School of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang, 050200 China.
Lei Wang2School of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang, 050200 China.
Qiang YinDepartment of Management, Xinjiang Uygur Pharmaceutical Co., Ltd., Wulumuqi, 830001 China.
Chen-Meng Zhang1College of Chemistry and Material Science, Hebei Normal University, Shijiazhuang, 050024 China.
Yu-Guang Zheng2School of Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang, 050200 China.
E-Hu Liu3State Key Laboratory of Natural Medicines, China Pharmaceutical University, Nanjing, 210009 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese medicines (TCM) have a long history of use because of its potential complementary therapy and fewer adverse effects. However, the toxicity and safety issues of TCM have drawn considerable attention in the past two decades. Metabolomics is an "omics" approach that aims to comprehensively analyze all metabolites in biological samples. In agreement with the holistic concept of TCM, metabolomics has shown great potential in efficacy and toxicity evaluation of TCM. Recently, a large amount of metabolomic researches have been devoted to exploring the mechanism of toxicity induced by TCM, such as hepatotoxicity, nephrotoxicity, and cardiotoxicity. In this paper, the application of metabolomics in toxicity evaluation of bioactive compounds, TCM extracts and TCM prescriptions are reviewed, and the potential problems and further perspectives for application of metabolomics in toxicological studies are also discussed.

Indexed as

MetabolomicsToxicityToxicity mechanismsTraditional Chinese medicines

Identifiers

PMID30524499
PMCPMC6278008

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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.