ArticleCell reports. Medicine2024
Risk assessment with gene expression markers in sepsis development.
Article in Cell reports. Medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- A dataset of paired blood mRNA and microRNA sequencing across acute septic shock and recovery.Scientific data · 2026Article
- Testosterone and androgen receptor pathway modulation in sepsis: immunometabolic mechanisms and therapeutic implications - a scoping review.Frontiers in endocrinology · 2026Article
- Sepsis biomarkers: recent advances and future perspectives.Frontiers in immunology · 2026Review
- RAPGEF6 is a key biomarker with temporal characteristics in sepsis and exacerbates inflammation and oxidative stress by NF-κB-mediated necroptosis.Functional & integrative genomics · 2025Article
- Multi-Omics and -Organ Insights into Energy Metabolic Adaptations in Early Sepsis Onset.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques.Frontiers in immunology · 2025Article
- A Self-Assembled Metabolic Regulator Reprograms Macrophages to Combat Cytokine Storm and Boost Sepsis Immunotherapy.Research (Washington, D.C.) · 2025Article
- Machine learning tools for deciphering the regulatory logic of enhancers in health and disease.Frontiers in genetics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Infection is a commonplace, usually self-limiting, condition but can lead to sepsis, a severe life-threatening dysregulated host response. We investigate the individual phenotypic predisposition to developing uncomplicated infection or sepsis in a large cohort of non-infected patients undergoing major elective surgery. Whole-blood RNA sequencing analysis was performed on preoperative samples from 267 patients. These patients developed postoperative infection with (n = 77) or without (n = 49) sepsis, developed non-infectious systemic inflammatory response (n = 31), or had an uncomplicated postoperative course (n = 110). Machine learning classification models built on preoperative transcriptomic signatures predict postoperative outcomes including sepsis with an area under the curve of up to 0.910 (mean 0.855) and sensitivity/specificity up to 0.767/0.804 (mean 0.746/0.769). Our models, confirmed by quantitative reverse-transcription PCR (RT-qPCR), potentially offer a risk prediction tool for the development of postoperative sepsis with implications for patient management. They identify an individual predisposition to developing sepsis that warrants further exploration to better understand the underlying pathophysiology.
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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.