ArticleFrontiers in immunology2026
Computational discovery of
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Neonatal sepsis is a life-threatening condition with high mortality. Ferroptosis and cuproptosis, oxidative stress-related cell death pathways, are implicated in sepsis pathogenesis, but their role in neonatal sepsis remains unclear. This study aimed to identify and validate diagnostic biomarkers for neonatal sepsis associated with ferroptosis and cuproptosis pathways using integrated bioinformatics and machine learning approaches, and to explore potential therapeutic targets. Methods: Transcriptomic data from neonatal sepsis patients (GSE69686, GSE25504) were analyzed. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) networks were performed to identify cuproptosis- and ferroptosis-related genes (CFRGs). Three machine learning algorithms-LASSO, SVM-RFE, and XGBoost-were applied for feature selection. Immune infiltration was assessed via CIBERSORT. Molecular docking was used to screen FDA-approved drugs against candidate targets. Results: Three biomarkers- Conclusion:
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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.