Evidence map›Paper›PMID 34059101›Full record

ArticleChinese medicine2021

Identification of anti-inflammatory compounds from Zhongjing formulae by knowledge mining and high-content screening in a zebrafish model of inflammatory bowel diseases.

Yunru Yu, Jing Chen, Xiaohui Zhang, Yingchao Wang, Shufang Wang, Lu Zhao, Yi Wang

Open access · goldAbstract read
In one paragraph

Article in Chinese medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
1.4field-weighted citation impact, top 20% 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

14 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
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  3. DietaryFrontiers in microbiology · 2026
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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 at 3 institutions in 1 country.

Yunru Yu *Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Jing Chen *Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Xiaohui ZhangPharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Yingchao WangPharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Shufang WangPharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
Lu ZhaoPharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. lzhao@zju.edu.cn.
Yi WangPharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China. zjuwangyi@zju.edu.cn.ORCID http://orcid.org/0000-0002-3676-9183
Zhejiang University · CNTianjin University of Traditional Chinese Medicine · CNZhejiang Pharmaceutical College · CN

Funding

Key Technologies Research and Development Program 2018YFC1704502National Natural Science Foundation of China 31971088National Natural Science Foundation of China 81822047Zhejiang Key Agricultural Enterprise Institute 2017Y20001
6 · The paper itself

Abstract

backgroundInflammatory bowel diseases (IBD) are chronic relapsing intestinal inflammations with increasing global incidence, and new drug development remains in urgent demand for IBD management. To identify effective traditional Chinese medicine (TCM) formulae and compounds in IBD treatment, we innovatively combined the techniques of knowledge mining, high-content screening and high-resolution mass spectrometry, to conduct a systematic screening in Zhongjing formulae, which is a large collection of TCM prescriptions with most abundant clinical evidences.

methodsUsing Word2vec-based text learning, the correlations between 248 Zhongjing formulae and IBD typical symptoms were analyzed. Next, from the top three formulae with predicted relationship with IBD, TCM fractions were prepared and screened on a transgenic zebrafish IBD model for their therapeutic effects. Subsequently, the chemical compositions of the fraction hits were analyzed by mass spectrometry, and the major compounds were further studied for their anti-IBD effects and potential mechanisms.

resultsThrough knowledge mining, Peach Blossom Decoction, Pulsatilla Decoction, and Gegen Qinlian Decoction were predicted to be the three Zhongjing formulae mostly related to symptoms typical of IBD. Seventy-four fractions were prepared from the three formulae and screened in TNBS-induced zebrafish IBD model by high-content analysis, with the inhibition on the intestinal neutrophil accumulation and ROS level quantified as the screening criteria. Six herbal fractions showed significant effects on both pathological processes, which were subsequently analyzed by mass spectrometry to determine their chemical composition. Based on the major compounds identified by mass spectrometry, a second-round screen was conducted and six compounds (palmatine, daidzin, oroxyloside, chlorogenic acid, baicalin, aesculin) showed strong inhibitory effects on the intestinal inflammation phenotypes. The expression of multiple inflammatory factors, including il1β, clcx8a, mmp and tnfα, were increased in TNBS-treated fish, which were variously inhibited by the compounds, with aesculin showing the most potent effects. Moreover, aesculin and daidzin also upregulated e-cadherin's expression.

conclusionTaken together, we demonstrated the regulatory effects of several TCM formulae and their active compounds in the treatment of IBD, through a highly efficient research strategy, which can be applied in the discovery of effective TCM formulae and components in other diseases.

Indexed as

High-content screeningInflammatory bowel diseasesKnowledge miningMultimodal identificationZebrafishZhongjing formulae

Identifiers

PMID34059101
PMCPMC8166029
OpenAlexW3164814665

What OpenQuestion holds

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LicenceCC BY
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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.