Evidence map›Paper›PMID 40109658›Full record

ArticleJournal of inflammation research2025

Identification and Evaluation of Lipocalin-2 in Sepsis-Associated Encephalopathy via Machine Learning Approaches.

Jia Hu, Ziang Chen, Jinyan Wang, Aoxue Xu, Jinkai Sun, Wenyan Xiao, Min Yang

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. 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.

Jia Hu *Department of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.ORCID 0009-0004-7158-2316
Ziang Chen *Department of Urology, Huashan Hospital, Fudan University, Shanghai, People's Republic of China.ORCID 0009-0002-2007-3242
Jinyan WangDepartment of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.ORCID 0009-0004-1792-4768
Aoxue XuDepartment of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.
Jinkai SunDepartment of Anesthesiology and Perioperative Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.ORCID 0009-0003-5752-6257
Wenyan XiaoThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.
Min YangThe Second Department of Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Sepsis-associated encephalopathy (SAE) critically contributes to poor prognosis in septic patients. Identifying and screening key genes responsible for SAE, as well as exploring potential targeted therapies, are vital for improving the management of sepsis and advancing precision medicine. Patients and Methods: Single-cell RNA sequencing (scRNA-seq) was administrated to identify cell subpopulations related to poor prognosis in septic patients. Next, hierarchical dynamic weighted gene co-expression network analysis (hdWGCNA) was employed to identify genes associated with specific neutrophil subpopulations. Enrichment analysis revealed the biological functions of these genes. Subsequently, neuroinflammation-related genes were obtained to construct a neuroinflammation-related signature. The AddModuleScore algorithm was used to calculate neuroinflammation scores for each cell subpopulation, whereas the CellCall algorithm was used to assess the crosstalk between neutrophils and other cell subpopulations. To identify key genes accurately, four binary classification machine learning algorithms were utilized. Finally, Western blotting and behavioral tests were used to confirm the role of LCN2-related neuroinflammation in septic mice. Results: This study utilized scRNA-seq to reveal the critical role of peripheral neutrophils during sepsis, identifying these neutrophils as contributors to poor prognosis and associated with neuroinflammation. On the basis of various machine learning algorithms, we discovered that Lipocalin-2 (LCN2) may be the key gene involved in neutrophil-induced SAE. To prove these findings, we conducted in vivo experiments and an animal model. Increased LCN2 expression and cognitive dysfunction occurred in septic mice. Additionally, the levels of markers of astrocytes and microglia and inflammatory factors such as TNF-α and IL-6 were significantly increased. All these phenomena were reversed by the downregulation of LCN2. Conclusion: The upregulation of LCN2 expression on peripheral neutrophils is a critical step that triggers neuroinflammation in the central nervous system during SAE.

Indexed as

cognitive dysfunctionlipocalin-2machine learningneuroinflammationsepsis-associated encephalopathysingle-cell RNA sequencing

Identifiers

PMID40109658
PMCPMC11920642

What Socratic holds

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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 Socratic graph.