Evidence map›Paper›PMID 40636086›Full record

ArticleiLIVER2024

Network pharmacology and single-cell transcriptomic analysis with molecular docking to elucidate the potential compounds and targets of

Wenze Wu, Yuzhu Shi, Yongzi Wu, Rui Zhang, Xinyan Wu, Weidi Zhao, Zhiyuan Chen, Gang Ye

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iLIVER, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

8 authors.

Wenze WuKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, China.
Yuzhu ShiKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, China.
Yongzi WuKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, China.
Rui ZhangKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, China.
Xinyan WuCollege of Veterinary Medicine, Sichuan Agricultural University, Chengdu 625014, China.
Weidi ZhaoSchool of Physical Education, Shanxi University, Taiyuan 030006, China.
Zhiyuan ChenKey Laboratory of Bioresource Research and Development of Liaoning Province, College of Life and Health Sciences, National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang 110169, China.
Gang YeCollege of Veterinary Medicine, Sichuan Agricultural University, Chengdu 625014, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and aims: Methods: We used network pharmacology and single-cell transcriptomic analysis with molecular docking to elucidate the active components and targets of Results: CDK1, ESR1, HSP90A11, and MAPK1 were shown to be the key targets of Conclusion: Our study provides novel insights into the anti-hepatocellular carcinoma pharmacological effects of

Indexed as

Hepatocellular carcinomaMolecular dockingMolecular dynamics simulationNetwork pharmacologyPolygonum cuspidatumSingle-cell transcriptomic analysis

Identifiers

PMID40636086
PMCPMC12212722

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.