Evidence map›Paper›PMID 37475857›Full record

ArticleFrontiers in immunology2023

Uncovering the potential role of oxidative stress in the development of periodontitis and establishing a stable diagnostic model via combining single-cell and machine learning analysis.

Guobin Song, Gaoge Peng, Jinhao Zhang, Binyu Song, Jinyan Yang, Xixi Xie, Siqi Gou, Jing Zhang, Guanhu Yang, Hao Chi and 1 more

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Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 1 pooled it
–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

41 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

11 authors.

Guobin SongSchool of Stomatology, Southwest Medical University, Luzhou, China.
Gaoge PengClinical Medical College, Southwest Medical University, Luzhou, China.
Jinhao ZhangSchool of Stomatology, Southwest Medical University, Luzhou, China.
Binyu SongDepartment of Plastic Surgery, Xijing Hospital, Fourth Military Medical University, Xi'an, China.
Jinyan YangSchool of Stomatology, Southwest Medical University, Luzhou, China.
Xixi XieSchool of Stomatology, Southwest Medical University, Luzhou, China.
Siqi GouClinical Medical College, Southwest Medical University, Luzhou, China.
Jing ZhangDivision of Basic Biomedical Sciences, The University of South Dakota Sanford School of Medicine, Vermillion, SD, United States.
Guanhu YangDepartment of Specialty Medicine, Ohio University, Athens, OH, United States.
Hao ChiClinical Medical College, Southwest Medical University, Luzhou, China.
Gang TianDepartment of Laboratory Medicine, The Affiliated Hospital of Southwest Medical University, Luzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The primary pathogenic cause of tooth loss in adults is periodontitis, although few reliable diagnostic methods are available in the early stages. One pathological factor that defines periodontitis pathology has previously been believed to be the equilibrium between inflammatory defense mechanisms and oxidative stress. Therefore, it is necessary to construct a model of oxidative stress-related periodontitis diagnostic markers through machine learning and bioinformatic analysis. Methods: We used LASSO, SVM-RFE, and Random Forest techniques to screen for periodontitis-related oxidative stress variables and construct a diagnostic model by logistic regression, followed by a biological approach to build a Protein-Protein interaction network (PPI) based on modelled genes while using modelled genes. Unsupervised clustering analysis was performed to screen for oxidative stress subtypes of periodontitis. we used WGCNA to explore the pathways correlated with oxidative stress in periodontitis patients. Networks. Finally, we used single-cell data to screen the cellular subpopulations with the highest correlation by scoring oxidative stress genes and performed a proposed temporal analysis of the subpopulations. Results: We discovered 3 periodontitis-associated genes ( Conclusion: This study identifies a diagnostic model of 3-OSRGs from which patients can benefit and explores the importance of oxidative stress genes in building an immune environment in patients with periodontitis.

Indexed as

Computational BiologyOxidative StressAdultCluster AnalysisGene Regulatory NetworksHumansMachine Learningdiagnostic signatureinflammationmachine learningoxidative stressperiodontitissingle-cell RNA-seqWGCNA

Identifiers

PMID37475857
PMCPMC10355807

What Socratic holds

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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 Socratic graph.