Evidence map›Paper›PMID 34277119›Full record

ArticleJournal of pharmaceutical analysis2021

Corydalis Rhizoma as a model for herb-derived trace metabolites exploration: A cross-mapping strategy involving multiple doses and samples.

Chanjuan Yu, Fengyun Wang, Xinyue Liu, Jiayan Miao, Siqi Tang, Qin Jiang, Xudong Tang, Xiaoyan Gao

Open access · goldAbstract read
In one paragraph

Article in Journal of pharmaceutical analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 22% of its field
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

6 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. The In Vitro Pharmacokinetics of Medicinal Plants: A Review.Pharmaceuticals (Basel, Switzerland) · 2025
    Review
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  4. 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

8 authors at 2 institutions in 1 country.

Chanjuan YuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Fengyun WangGastroenterology Department, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, PR China.
Xinyue LiuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Jiayan MiaoSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Siqi TangSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Qin JiangSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Xudong TangGastroenterology Department, Xiyuan Hospital, China Academy of Chinese Medical Sciences, Beijing, 100091, PR China.
Xiaoyan GaoSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, PR China.
Beijing University of Chinese Medicine · CNXiyuan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deciphering the metabolites of multiple components in herbal medicine has far-reaching significance for revealing pharmacodynamic ingredients. However, most chemical components of herbal medicine are secondary metabolites with low content whose in vivo metabolites are close to trace amounts, making it difficult to achieve comprehensive detection and identification. In this paper, an efficient strategy was proposed: herb-derived metabolites were predicted according to the structural characteristics and metabolic reactions of chemical constituents in Corydalis Rhizoma and chemical structure screening tables for metabolites were conducted. The fragmentation patterns were summarized from representative standards combining with specific cleavage behaviors to deduce structures of metabolites. Ion abundance plays an important role in compound identification, and high ion abundance can improve identification accuracy. The types of metabolites in different biological samples were very similar, but their ion abundance might be different. Therefore, for trace metabolites in biological samples, we used the following two methods to process: metabolites of high dose herbal extract were analyzed to characterize those of clinical dose herbal extracts in the same biological samples; cross-mapping of different biological samples was applied to identify trace metabolites based on the fact that a metabolite has different ion abundance in different biological samples. Compared with not using this strategy, 44 more metabolites of clinical dose herbal extract were detected. This study improved the depth, breadth, and accuracy of current methods for herb-derived metabolites characterization.

Indexed as

AlkaloidCharacteristic fragmentCorydalis yanhusuoIn vivo metabolismMetabolite

Identifiers

PMID34277119
PMCPMC8264384
OpenAlexW3011137467

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.