Evidence map›Paper›PMID 34455301›Full record

ArticleBioorganic chemistry2021

Screening out anti-inflammatory or anti-viral targets in Xuanfei Baidu Tang through a new technique of reverse finding target.

Zixuan Wang, Jumei Zhang, Jiuyu Zhan, Hongwei Gao

Open access · greenAbstract read
In one paragraph

Article in Bioorganic chemistry, 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
1.7field-weighted citation impact, top 14% 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, 11 citations in OpenAlex.

  1. Review
  2. Bioactive compounds from Huashi Baidu decoction possess both antiviral and anti-inflammatory effects against COVID-19.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  3. Article
  4. Article
  5. Article
  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

4 authors at 1 institution in 1 country.

Zixuan WangSchool of Life Science, Ludong University, Yantai, Shandong 264025, China.
Jumei ZhangSchool of Life Science, Ludong University, Yantai, Shandong 264025, China.
Jiuyu ZhanSchool of Life Science, Ludong University, Yantai, Shandong 264025, China.
Hongwei GaoSchool of Life Science, Ludong University, Yantai, Shandong 264025, China. Electronic address: gaohongw369@ldu.edu.cn.
Ludong University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Traditional Chinese herbal compound prescription in Xuanfei Baidu Tang (XBT) has obvious effects in the treatment of COVID-19. However, its effective compounds and targets for the treatment of COVID-19 remain unclear. Computer-Aided Drug Design is used to virtually screen out the anti-inflammatory or anti-viral compounds in XBT, and predict the potential targets by Discovery Studio 2020. Then, we searched for COVID-19 targets using Genecards databases and Protein Data Bank (PDB) databases and compared them to identify targets that were common to both. Finally, the target we screened out is: TP53 (Tumor Protein P53). This article also shows that XBT in the treatment of COVID-19 works in a multi-link and overall synergistic manner. Our results will help to design the new drugs for COVID-19.

Indexed as

COVID-19 Drug TreatmentAnti-Inflammatory Agents, Non-SteroidalAntiviral AgentsDrug Evaluation, PreclinicalDrugs, Chinese HerbalHumansMedicine, Chinese TraditionalMolecular StructureSARS-CoV-2Tumor Suppressor Protein p53Anti-Inflammatory Agents, Non-SteroidalAntiviral AgentsDrugs, Chinese HerbalTumor Suppressor Protein p53Anti-inflammatoryAnti-viralCOVID-19Reverse finding targetThe traditional Chinese herbal medicine

Identifiers

PMID34455301
PMCPMC8373853
OpenAlexW3195918139

What OpenQuestion holds

Textmetadata
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.