ArticleScientific reports2025
Advancing sepsis diagnosis and immunotherapy machine learning-driven identification of stable molecular biomarkers and therapeutic targets.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
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Who cites it
6 citing papers in PubMed.
- Artificial intelligence in biomarker discovery for diseases: diagnostic and therapeutic prospects.Signal transduction and targeted therapy · 2026Review
- From Glycocalyx Shedding to Microvascular Collapse in Sepsis: Endothelial Pathophysiology, Organ Dysfunction, and Mechanistic Biomarkers.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Machine Learning Models for Sepsis: From Early Detection to Short- and Long-Term Prognosis.International journal of molecular sciences · 2026Article
- Non-specific Protein and Peptide Antibacterial Factors of Mammals.The protein journal · 2026Review
- Immunodynamic Disruption in Sepsis: Mechanisms and Strategies for Personalized Immunomodulation.Biomedicines · 2025Review
- Upadacitinib Attenuates Lipopolysaccharide- and Cecal Ligation and Puncture-Induced Inflammatory Responses by Inhibiting NF-κB and Its Downstream Cytokines.Journal of inflammation research · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis represents a significant global health challenge, necessitating early detection and effective treatment for improved outcomes. While traditional inflammatory markers facilitate the diagnosis of sepsis, the aspect of immune suppression remains poorly addressed. This study aimed to identify critical immune-related genes (IIRGs) associated with sepsis through genomic analysis and machine learning techniques, thereby enhancing diagnostic and treatment response predictions. Analyses of two extensive datasets were conducted, identifying significant immune genes using the ESTIMATE algorithm, Weighted Gene Correlation Network Analysis (WGCNA), and five machine learning methods. Prediction models were constructed and validated using six machine learning algorithms, achieving high accuracy (AUC > 0.75). Eleven key IIRGs were identified as active in immune pathways, such as the JAK-STAT signaling pathway, and were significantly correlated with immune cell infiltration in sepsis. Additionally, drug sensitivity analysis indicated that IIRGs correlated with responses to anticancer drugs. These results underscore the potential of these genes in enhancing sepsis diagnosis and treatment, highlighting the imperative for further validation across diverse populations.
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Registered trials
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