Evidence map›Paper›PMID 39301055›Full record

ArticlePeerJ2024

Min Wen, Marady Hun, Mingyi Zhao, Qingnan He

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in PeerJ, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

4 authors.

Min WenDepartment of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Marady HunDepartment of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Mingyi ZhaoDepartment of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Qingnan HeDepartment of Pediatrics, The Third Xiangya Hospital, Central South University, Changsha, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Lupus nephritis (LN) is an autoimmune-related kidney disease with a poor prognosis, however the potential pathogenic mechanism remains unclear and there is a lack of precise biomarkers. Therefore, a thorough screening and identification of renal markers in LN are immensely beneficial to the research on its pathogenic mechanisms and treatment strategies. Methods: We utilized bioinformatics to analyze the differentially expressed genes (DEGs) at the transcriptome level of three clusters: total renal, glomeruli, and renal tubulointerstitium in the GEO database to discover potential renal biomarkers of LN. We utilized NephroSeq datasets and measured mRNA and protein levels in the kidneys of MRL/lpr mice to confirm the expression of key DEGs. Results: Seven significantly differential genes ( Conclusions: This study identified seven key renal biomarkers through bioinformatics analysis using the GEO and NephroSeq databases. It was identified that

Indexed as

BiomarkersLupus NephritisMice, Inbred MRL lprAnimalsComputational BiologyFemaleGene Expression ProfilingHumansKidneyMiceTranscriptomeBiomarkersBioinformatics analysisLupus NephritisMMEPTPRCRenal Biomarkers

Identifiers

PMID39301055
PMCPMC11412223

What Socratic holds

Textmetadata
LicenceCC BY
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.