Evidence map›Paper›PMID 39474607›Full record

ArticleFrontiers in pharmacology2024

Rapid identification of chemical profiles

Xueyan Li, Fulu Pan, Lin Wang, Jing Zhang, Xinyu Wang, Dongying Qi, Xiaoyu Chai, Qianqian Wang, Zirong Yi, Yuming Ma and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Total saponins fromJournal of ginseng research · 2026
    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

13 authors.

Xueyan Li *School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Fulu Pan *School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Lin WangInstitute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Jing ZhangInstitute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Xinyu WangSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Dongying QiSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Xiaoyu ChaiSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Qianqian WangSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Zirong YiSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Yuming MaSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Yanli PanInstitute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
Yang LiuSchool of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, China.
Guopeng WangZhongcai Health (Beijing) Biological Technology Development Co., Ltd., Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Aging is marked by the gradual deterioration of cells, tissues, and organs and is a major risk factor for many chronic diseases. Considering the complex mechanisms of aging, traditional Chinese medicine (TCM) could offer distinct advantages. However, due to the complexity and variability of metabolites in TCM, the comprehensive screening of metabolites associated with pharmacology remains a significant issue. Methods: A reliable and integrated identification method based on UPLC-Q Exactive-Orbitrap HRMS was established to identify the chemical profiles of Huan Shao Dan (HSD). Then, based on the theory of sequential metabolism, the metabolic sites of HSD Results: This study identified 366 metabolites in HSD. Based on the results of sequential metabolism, 135 metabolites were then absorbed into plasma. A total of 178 peaks were identified from the sample after incubation with artificial gastric juice. In addition, 102 and 91 peaks were identified from the fecal and urine samples, respectively. Finally, based on the results of the deep learning model and bioactivity assay, ginsenoside Rg1, Rg2, and Rc, pseudoginsenoside F11, and jionoside B1 were selected as potential anti-aging metabolites. Conclusion: This study provides a valuable reference for future research on the material basis of HSD by describing the chemical profiles both

Indexed as

anti-aging metabolitesdeep learning modelHuan Shao Dansequential metabolismUPLC-Q Exactive-Orbitrap HRMS

Identifiers

PMID39474607
PMCPMC11518704

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.