Evidence map›Paper›PMID 28462138›Full record

ReviewIntegrative medicine research2017

Quantitative analysis of multi-components by single marker-a rational method for the internal quality of Chinese herbal medicine.

Chunsheng Zhu, Xiaoping Li, Bing Zhang, Zhijian Lin

Abstract readReview
In one paragraph

Review in Integrative medicine research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing 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

16 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Comparative Analysis of the Phenolic Profile ofMolecules (Basel, Switzerland) · 2022
    Article
  9. Article
  10. Article
  11. A Fast and Validated HPLC Method for the Simultaneous Analysis of Five 5-HTInternational journal of analytical chemistry · 2021
    Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Simultaneous Determination of Six Active Components inJournal of analytical methods in chemistry · 2017
    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

4 authors.

Chunsheng ZhuThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Xiaoping LiThe First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
Bing ZhangSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.
Zhijian LinSchool of Traditional Chinese Medicine, Beijing University of Chinese Medicine, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In order to achieve the comprehensive quality control of Chinese herbal medicine (CHM), the conventional practice of selecting a single marker for testing has been gradually replaced by the determination of multiple active components based on the characteristics of the synergistic interaction of CHM and applicability of sophisticated analytical techniques. However, with a huge number of CHM in the market and more complex preparations, the limited availability of various standard substances for quantitative analysis has been a major bottleneck in realizing the goal. To overcome these uprising problems, quantitative analysis of multi-components by single marker (QAMS) was proposed and accepted as a new method to reflect the internal quality of CHM. In this review, the current knowledge about QAMS is systematically summarized, including the general content of QAMS, current status, and general procedure. Additionally, speculation is proposed about the future applications of QAMS approaches in the modernization and standardization of CHM.

Indexed as

Chinese herbal medicinequantitative analysis of multi-components by single markerrelative correction factor

Identifiers

PMID28462138
PMCPMC5395686

What OpenQuestion holds

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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.