Evidence map›Paper›PMID 36819522›Full record

ArticleAnnals of translational medicine2023

Bioinformatics analysis of differentially expressed genes in diabetic foot ulcer and preliminary experimental verification.

Fang Miao, Xixi Li, Chenglin Wang, Heju Yuan, Zhongming Wu

Open access · diamondAbstract read
In one paragraph

Article in Annals of translational medicine, 2023. 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
1.4field-weighted citation impact, top 18% 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

3 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Multi-omics Identify the Role of FGF7 Pathway in Diabetic Foot Ulcers.Iranian journal of pharmaceutical research : IJPR
    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

5 authors at 3 institutions in 1 country.

Fang MiaoNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Xixi LiShanxi Medical University, Taiyuan, China.
Chenglin WangDepartment of Endocrinology, Shanxi Provincial People's Hospital, Taiyuan, China.
Heju YuanDepartment of Endocrinology, Shanxi Provincial People's Hospital, Taiyuan, China.
Zhongming WuNHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital & Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, China.
Shaanxi Provincial People's Hospital · CNShanxi Medical University · CNShandong First Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Molecular changes are closely related to the pathogenesis and healing process of diabetic foot ulcers (DFUs), and are crucial for the early prediction and intervention of DFU. Methods: Bioinformatics analysis was performed in this study to identify the key differentially expressed genes (DEGs) in DFU, analyze their functions and function modes, and conduct preliminary experimental verification to determine the potential pivotal genes in the pathogenesis of DFU. Two datasets, GSE68183 and GSE80178, were obtained from the Gene Expression Omnibus (GEO). DEGs were obtained using GEO2R. Six co-expressed DEGs (co-DEGs) were obtained by R language analysis. The co-DEGs were constructed by using STRING and Cytoscape 3.7.2 to construct a protein-protein interaction (PPI) network, and two hub genes, Results: Conclusions: Our study found that the

Indexed as

bioinformaticsbiomarkerDiabetic foot ulcer (DFU)differentially expressed genes (DEGs)

Identifiers

PMID36819522
PMCPMC9929774
OpenAlexW4316668675

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.