Evidence map›Paper›PMID 41592030›Full record

ArticlePloS one2026

Unveiling the molecular mechanisms of burn injury through integrated single-cell and bulk transcriptomic analysis.

Xiaoyu Zhu, Neng Huang, Yanyan Pan, Xin Le, Shengyong Cui, Jiliang Li, Youfen Fan

Abstract read
In one paragraph

Article in PloS one, 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
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0citing papers in PubMed
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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

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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

7 authors.

Xiaoyu ZhuSchool of Medicine, Ningbo University, Ningbo, Zhejiang, China.
Neng HuangNingbo No. 2 Hospital, Ningbo, Zhejiang Province, China.
Yanyan PanNingbo No. 2 Hospital, Ningbo, Zhejiang Province, China.
Xin LeNingbo No. 2 Hospital, Ningbo, Zhejiang Province, China.
Shengyong CuiNingbo No. 2 Hospital, Ningbo, Zhejiang Province, China.
Jiliang LiNingbo No. 2 Hospital, Ningbo, Zhejiang Province, China.
Youfen FanSchool of Medicine, Ningbo University, Ningbo, Zhejiang, China.ORCID https://orcid.org/0000-0001-7838-6247

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeBurn injuries are prevalent and have a significant effect on patients' quality of life and healthcare costs. Current treatment modalities, such as wound care and surgical interventions, often face challenges due to complications like infection and inadequate healing.

methodsThis study adopted single-cell RNA sequencing (scRNA-seq) to investigate the cellular landscape of the burn microenvironment. After rigorous quality control filtering, 9,248 cells were retained for analysis. Using UMAP dimensionality reduction, these cells were annotated into 14 subpopulations, including Neutrophils, Macrophages, and T cells. Differential gene analysis and machine learning techniques, such as LASSO regression and random forest selection, were employed to identify marker genes.

resultsMacrophages exhibited significant interactions with other cell types, indicating their pivotal role in immune signaling within the burn microenvironment. A total of 155 genes were identified as markers for Macrophages, with AP2A2, CCL7, and TF emerging as key features. Immune infiltration analysis also revealed notable differences in the proportions of immune cells, particularly Mast cells and Neutrophils, highlighting on their involvement in disease progression.

conclusionThis study provides novel insights into the immunological microenvironment of burn injuries. Despite limitations including a modest sample size and lack of experimental validation, our findings establish a foundation for future investigations into targeted immunotherapeutic strategies, potentially improving clinical outcomes and advancing personalized treatment approaches for burn patients.

Indexed as

BurnsSingle-Cell AnalysisTranscriptomeGene Expression ProfilingHumansMacrophagesNeutrophilsSingle-Cell Gene Expression Analysis

Identifiers

PMID41592030
PMCPMC12843517

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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.