Evidence map›Paper›PMID 37139640›Full record

ArticleBiomolecules & biomedicine2023

Identification of new biomarkers and immune infiltration characteristics of sepsis in very low birth weight infants.

Yujia Luo, Zhou Jiang, Rui Gu, Xuandong Zhang, Li Wei, Yuanyuan Zhou, Songying Zhang

Open access · diamondAbstract read
In one paragraph

Article in Biomolecules & biomedicine, 2023. 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
0.3field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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

7 authors at 2 institutions in 1 country.

Yujia LuoDepartment of NICU, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Qiantang District, Hangzhou, China.
Zhou JiangDepartment of NICU, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Qiantang District, Hangzhou, China.
Rui GuDepartment of NICU, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Qiantang District, Hangzhou, China.
Xuandong ZhangDepartment of NICU, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Qiantang District, Hangzhou, China.
Li WeiDepartment of NICU, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Qiantang District, Hangzhou, China.
Yuanyuan ZhouDepartment of Reproductive Endocrinology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Songying ZhangDepartment of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Shangcheng District, Hangzhou, China.
Sir Run Run Shaw Hospital · CNWomen's Hospital, School of Medicine, Zhejiang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sepsis is a life-threatening condition, especially in very low birth weight (VLBW) infants, and its pathogenesis remains unclear. Effective biomarkers need to be found to diagnose and treat the disease at an early stage. The Gene Expression Omnibus (GEO) database was screened and analyzed for differentially expressed genes (DEGs) in VLBW infants with sepsis. DEGs were then analyzed for functional enrichment. A weighted gene co-expression network analysis (WCGNA) was performed to identify the key modules and genes. The optimal feature genes (OFGs) were created using three machine learning algorithms. The single-sample Gene Set Enrichment Analysis (ssGSEA) scored the degree of immune cell enrichment between septic and control patients, and the correlation between OFGs and immune cells was evaluated. A total of 101 DEGs were identified between the sepsis and control samples. DEGs were mainly associated with immune responses and inflammatory signaling pathways in the enrichment analysis. In the WGCNA analysis, the MEturquoise module was significantly correlated with sepsis in VLBW infants (cor = 0.57, P < 0.001). By intersecting OFGs derived from three machine learning algorithms, two biomarkers were identified: glycogenin 1 (GYG1) and resistin (RETN). The area under the curves of GYG1 and RETN was greater than 0.97 in the testing set. The ssGSEA indicated immune cells infiltration in septic VLBW infants, and GYG1 and RETN revealed close correlations with immune cells. New biomarkers offer promising insights into the diagnosis and treatment of sepsis in VLBW infants.

Indexed as

SepsisAlgorithmsBiomarkersDatabases, FactualHumansInfantInfant, NewbornInfant, Very Low Birth WeightBiomarkers

Identifiers

PMID37139640
PMCPMC10494841
OpenAlexW4367852772

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.