Evidence map›Paper›PMID 42412387›Full record

ArticleOdontology2026

Mitochondrial dysfunction in peri-implantitis: bioinformatics and machine learning analysis with in vivo experiment.

Xianqi Rao, Haoran Yang, Yuxiang Chen, Anna Zhao, Tingting Cheng, Lin Li, Yidan Zhang, Xinyang Li, Ziliang Li

Abstract read
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In one paragraph

Article in Odontology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Xianqi RaoYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Haoran YangYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Yuxiang ChenYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Anna ZhaoYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Tingting ChengYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Lin LiYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Yidan ZhangYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Xinyang LiYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China.
Ziliang LiYunnan Key Laboratory of Stomatology &Department of Oral Implantology,The Affiliated Stomatology Hospital, Kunming Medical University, Kunming, China. 1752114604@qq.com.

Funding

the National Natural Science Foundation of China 82360185
6 · The paper itself

Abstract

This study aimed to identify mitochondria-related hub genes in peri-implantitis and to detect their expression in a Sprague-Dawley (SD) rat model. Human peri-implantitis tissue datasets (GSE223924, GSE33774, and GSE106090) were obtained from the GEO database and cross-referenced with the MitoCarta3.0 database to identify mitochondria-related differentially expressed genes (MitoDEGs). Functional characterization was performed through protein-protein interaction (PPI) network analysis, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Hub genes were further selected using least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm. Their discriminative capacity was assessed via receiver operating characteristic (ROC) curve analysis. Finally, a peri-implantitis model was established in SD rats, and hub gene expression was detected by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 115 MitoDEGs were identified, among which 80 genes formed the core interaction network. Machine learning methods identified five hub genes (TSPO, THEM5, SARDH, COX4I2, and ACSM1), all of which exhibited favorable discriminative ability in both the training and testing sets. Animal experiments confirmed that, compared with the healthy group, the expression patterns of the hub genes in peri-implantitis tissues were consistent with the bioinformatics results. This study offers novel insights into the molecular mechanisms of peri-implantitis and presents potential targets for therapeutic intervention.

Indexed as

BioinformaticsImmune infiltrationMachine learningMitochondriaOxidative stressPeri-implantitis

Identifiers

PMID42412387

What Socratic 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 Socratic graph.