Evidence map›Paper›PMID 40555937›Full record

ArticleEuropean journal of pediatrics2025

Comprehensive analysis and experimental validation of BST1 as a novel diagnostic biomarker for pediatric sepsis using multiple machine learning algorithms.

Yang Yang, Genhao Zhang

Abstract readValidation Study
PubMed Publisher
In one paragraph

Article in European journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

3 citing papers in PubMed.

  1. Observational
  2. Breast cancer stem cell activity driven byWorld journal of stem cells · 2026
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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.

Yang YangDepartment of Nuclear Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Hospital of Henan University, People'sZhengzhou, 450003, Henan, China. roly-poly.yang@163.com.
Genhao ZhangDepartment of Blood Transfusion, Zhengzhou University First Affiliated Hospital, Zhengzhou, 450052, Henan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone marrow stromal cell antigen-1 (BST1) expression is elevated in a variety of human diseases, but its relationship with pediatric sepsis is unclear. This study aimed to investigate the expression of BST1 in pediatric sepsis patients and its value for diagnosing pediatric sepsis. Key neutrophil extracellular traps (NETs)-related genes that accurately diagnose pediatric sepsis patients were identified using various machine learning methods, including LASSO, RF, XGBoost, LightGBM, and Boruta algorithms. GSE13904, GSE26378, and GSE26440 datasets were used to examine BST1 expression in pediatric sepsis and to evaluate the suitability of BST1 for the diagnosis of pediatric sepsis using receiver operating characteristic curves. Additionally, the usefulness of diagnostic BST1 for identifying pediatric sepsis in actual clinical samples was evaluated by measuring serum BST1 levels using the enzyme-linked immunosorbent assay (ELISA) on 30 normal samples and 35 pediatric sepsis samples. Furthermore, a more thorough analysis of the connection between immune cells and BST1 expression was conducted utilizing the CIBERSORT database. Finally, we used ELISA to quantify myeloperoxidase (MPO)-DNA complex levels to assess the establishment of NETs. Integrating machine learning approaches, we identified BST1 as the most robust NET-associated diagnostic biomarker for pediatric sepsis. Comparative analysis revealed a significant elevation of BST1 expression in septic patients versus healthy controls, with pronounced discriminative capacity in a 65 clinical samples cohort (AUC value = 0.873), outperforming the effects of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell count (WBC), and C-reactive protein (CRP). Lastly, we discovered a substantial correlation between the BST1 level and the MPO-DNA complex level in the serum of pediatric sepsis children. Conclusion: Serum BST1 has the potential to be an effective diagnostic biomarker for pediatric sepsis. What is Known: • Pediatric sepsis is a major cause of morbidity, death, financial burden, and negative effects on health systems throughout the world. What is New: • Serum BST1 has the potential to be an effective diagnostic biomarker for pediatric sepsis.

Indexed as

ADP-ribosyl CyclaseAntigens, CDMachine LearningSepsisAdolescentAlgorithmsBiomarkersChildChild, PreschoolEnzyme-Linked Immunosorbent AssayFemaleGPI-Linked ProteinsHumansInfantIsoantigensMaleADP-ribosyl CyclaseADP-ribosyl cyclase 2Antigens, CDBiomarkersCD177 protein, humanGPI-Linked ProteinsIsoantigensReceptors, Cell SurfaceBST1MPONETsPediatric sepsisROC curves

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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 OpenQuestion graph.