ArticlePediatric health, medicine and therapeutics2025
Pyroptosis-Related Molecular Clusters and Immune Infiltration in Pediatric Sepsis.
Article in Pediatric health, medicine and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Pediatric sepsis is a complex and heterogeneous condition resulting from a dysregulated immune response to infection. Pyroptosis, a newly recognized form of programmed cell death, has been implicated in the progression of various inflammatory diseases. However, the role of pyroptosis-related genes in pediatric sepsis remains unclear. Methods: Based on the GSE13904 dataset, we explored the pyroptosis-related differentially expressed genes (DEGs) in pediatric sepsis. We analyzed the molecular clusters based on pyroptosis-related DEGs. The WGCNA algorithm was performed to identify cluster-specific DEGs. The optimal machine model was identified by multiple machine learning methods (RF, SVM, GLM, XGB). The diagnostic value of hub genes in pediatric sepsis was verified in the training (GSE13904) and validation set (GSE26440) through ROC. qRT-PCR was used to verify the expression levels of 5 hub genes in whole blood between the pediatric sepsis and the control. Results: The dysregulated pyroptosis-related DEGs were identified in pediatric sepsis. Three pyroptosis-related molecular clusters were determined in pediatric sepsis. SVM presented the best discriminative performance with relatively lower residual and root mean square error. The nomogram, calibration curve, and decision curve analysis indicated the accuracy of SVM model to predict pediatric sepsis. 5 hub genes based on SVM presented satisfactory performance in the training and validation sets. These hub genes expression levels in pediatric sepsis were significantly higher than those in healthy controls in clinical samples. Conclusion: Our study systematically analyzed the relationship between pyroptosis and pediatric sepsis, and constructed a promising predictive model to evaluate the risk of pediatric sepsis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.