ArticleScientific reports2024
Identification of sepsis-associated encephalopathy biomarkers through machine learning and bioinformatics approaches.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- The Neuroimmune Axis in Sepsis: From Pathophysiological Circuits to Precision Neuromodulation.Journal of inflammation research · 2026Review
- Development and validation of an interpretable machine learning model for predicting cognitive impairment in patients with sepsis.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Sepsis-associated encephalopathy (SAE) is common in septic patients, characterized by acute and long-term cognitive impairment, and is associated with higher mortality. This study aimed to identify SAE-related biomarkers and evaluate their diagnostic potential. We analyzed three SAE-related sequencing datasets, using two as training sets and one as a validation set. Weighted Gene Co-expression Network Analysis and four machine learning methods-Elastic Net regression, LASSO, random forest, and XGBoost-were employed, dentifying 18 biomarkers with significant expression changes. External validation and in vitro experiments confirmed the differential expression of these biomarkers. These findings provide insights into SAE pathogenesis and suggest potential therapeutic targets.
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Registered trials
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