Evidence map›Paper›PMID 37495990›Full record

ArticleBMC oral health2023

The underlying molecular mechanisms and biomarkers between periodontitis and COVID-19.

Danlei Qin, Feiyan Yu, Dongchao Wu, Chong Han, Xuemin Yao, Lulu Yang, Xi Yang, Qianqian Wang, Dongning He, Bin Zhao

Open access · goldAbstract read
In one paragraph

Article in BMC oral health, 2023. 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, top 86% 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

0 citing papers in PubMed, 0 citations in OpenAlex.

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

10 authors at 1 institution in 1 country.

Danlei Qin *Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Feiyan Yu *Shanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Dongchao WuShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Chong HanShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Xuemin YaoShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Lulu YangShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Xi YangShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Qianqian WangShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China.
Dongning HeShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China. hdn00@163.com.
Bin ZhaoShanxi Province Key Laboratory of Oral Diseases Prevention and New Materials, Shanxi Medical University School and Hospital of Stomatology, No 63, New South Road, Yingze District, Taiyuan, 030001, Shanxi, China. sxmu0688@126.com.
Shanxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveEmerging evidence shows the clinical consequences of patient with COVID-19 and periodontitis are not promising, and periodontitis is a risk factor. Periodontitis and COVID-19 probably have a relationship. Hence, this study aimed to identify the common molecular mechanism that may help to devise potential therapeutic strategies in the future. MATERIAL AND

methodsWe analyzed two RNA-seq datasets for differential expressed genes, enrichment of biological processes, transcription factors (TFs) and deconvolution-based immune cell types in periodontitis, COVID-19 and healthy controls. Relationships between TFs and mRNA were established by Pearson correlation analysis, and the common TFs-mRNA regulatory network and nine co-upregulated TFs of the two diseases was obtained. The RT-PCR detected the TFs.

resultsA total of 1616 and 10201 differentially expressed gene (DEGs) from periodontitis and COVID-19 are found. Moreover, nine shared TFs and common biological processes associated with lymphocyte activation involved in immune response were identified across periodontitis and COVID-19. The cell type enrichment revealed elevated plasma cells among two diseases. The RT-PCR further confirmed the nine TFs up-regulation in periodontitis.

conclusionThe pathogenesis of periodontitis and COVID-19 is closely related to the expression of TFs and lymphocyte activation, which can provide potential targets for treatment.

Indexed as

COVID-19PeriodontitisBiomarkersGene Expression ProfilingGene Regulatory NetworksHumansRNA, MessengerBiomarkersRNA, MessengerCOVID-19PeriodontitisPlasma cellsSusceptibilityTranscription factors

Identifiers

PMID37495990
PMCPMC10369766
OpenAlexW4385294979

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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.