Evidence map›Paper›PMID 42544308›Full record

ArticlePeerJ2026

Ling Chen, Meiting Wu

Abstract read
In one paragraph

Article in PeerJ, 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

2 authors.

Ling ChenDepartment of Gynecology and Obstetrics, Fujian Medical University Union Hospital, Fuzhou, China.
Meiting WuDepartment of Gynecology and Obstetrics, Fujian Medical University Union Hospital, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The involvement of oxidative stress (OS) in pre-eclampsia (PE) has been reported, and the present study probed into the OS-related feature genes in PE. Methods: The dataset GSE60438 was used to identify the OS-related features in PE, and corresponding feature genes were identified using machine learning algorithms including weighted gene co-expression network analysis (WGCNA), Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and support vector machine-recursive feature elimination (SVM-RFE). The diagnostic value of the feature genes and their correlation with immune infiltration were explored. Gene set enrichment analysis (GSEA) was performed to identify enriched pathways of these genes. Furthermore, regulatory networks involving the feature genes were constructed. The role of these genes in hypoxia/reoxygenation (H/R)-induced trophoblasts was investigated. Results: Two WGCNA-identified modules (MEpurple and MEturquoise) were intersected with OS-related genes, yielding 359 common genes. Through machine learning algorithms and expression validation, three genes ( Conclusion: The present study examined the OS features in PE, hoping to contribute to the management of PE.

Indexed as

Carnitine O-PalmitoyltransferaseOxidative StressPre-EclampsiaBiomarkersFemaleGene Expression ProfilingGene Regulatory NetworksHumansPregnancyTrophoblastsBiomarkersCarnitine O-PalmitoyltransferaseBioinformaticsCarnitine palmitoyltransferase 1AOxidative stressPre-eclampsia

Identifiers

PMID42544308
PMCPMC13429108

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.