Evidence map›Paper›PMID 37528477›Full record

ArticleChinese medicine2023

Discovery and evaluation of active compounds from Xuanfei Baidu formula against COVID-19 via SARS-CoV-2 M

Min Zhang, Liting Liu, Yao Zhao, Yipeng Cao, Yan Zhu, Lifeng Han, Qi Yang, Yu Wang, Changjian Wang, Han Zhang and 2 more

Open access · goldAbstract read
In one paragraph

Article in Chinese medicine, 2023. 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
4.2field-weighted citation impact, top 6% 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, 17 citations in OpenAlex.

  1. Article
  2. How does NMR support SARS-CoV-2 protein-ligand interaction studies?Analytical and bioanalytical chemistry · 2026
    Review
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  5. Comprehensive Review on Fruit ofMolecules (Basel, Switzerland) · 2024
    Review
  6. 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

12 authors at 4 institutions in 1 country.

Min Zhang *State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
Liting Liu *State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
Yao ZhaoShanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 200031, China.
Yipeng CaoNational Supercomputer Center in Tianjin, Tianjin, 300457, China.
Yan ZhuShanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 200031, China.
Lifeng HanState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
Qi YangGuangzhou Laboratory, Guangzhou, 510005, China.
Yu WangState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
Changjian WangState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China.
Han ZhangState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China. zhanghan0023@126.com.
Yuefei WangState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China. wangyf0622@tjutcm.edu.cn.ORCID http://orcid.org/0000-0003-0956-6046
Junhua ZhangState Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, West Area, Tuanbo New Town, Jinghai District, Tianjin, 301617, China. zjhtcm@foxmail.com.
Tianjin University of Traditional Chinese Medicine · CNShanghaiTech University · CNGuangzhou Experimental Station · CNNational Supercomputing Center of Tianjin · CN

Funding

Foundation of State Key Laboratory of Component-based Chinese Medicine CBCM2020105National Key R&D Program of China 2021YFE0200300National Natural Science Foundation of China 82104372National Natural Science Foundation of China 82141201
6 · The paper itself

Abstract

backgroundThe coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus (SARS-CoV-2) is still a widespread concern. As one of the effective traditional Chinese medicine (TCM) formulae, Xuanfei Baidu formula (XFBD) shows significant efficacy for treatment of COVID-19 patients. However, its antiviral active compounds and mechanism are still unclear. PURPOSE: In this study, we explored the bioactive compounds of XFBD and its antiviral mechanism by integrating computational analysis and experimental testing.

methodsFocusing on the SARS-CoV-2 main protease (M

resultsFrom a library of 83 natural compounds, acteoside, licochalcone B, licochalcone D, linoleic acid, and physcion showed the satisfactory inhibition effects on M

conclusionActeoside is regarded as a representative active natural compound in XFBD to inhibit replication of SARS-CoV-2, which provides the antiviral evidence and some insights into the identification of SARS-CoV-2 M

Indexed as

ActeosideMproSARS-CoV-2Xuanfei Baidu formula (XFBD)

Identifiers

PMID37528477
PMCPMC10394814
OpenAlexW4385476067

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.