ArticleAPL bioengineering2026
Integrating Mendelian randomization and multi-omics analysis unravels gut microbiota-driven metabolic mechanisms in sepsis and identifies diagnostic biomarkers through experimental validation.
Article in APL bioengineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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8 authors.
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Abstract
Sepsis, a life-threatening systemic inflammatory syndrome, remains a leading cause of global mortality due to its complex pathophysiology and the lack of specific diagnostic biomarkers. Recent evidence highlights intricate interactions between the gut microbiota, metabolites, and host inflammatory responses; however, the causal relationships and underlying mechanisms remain poorly understood. We integrated Mendelian randomization (MR) with multi-omics approaches (including transcriptomics, untargeted metabolomics, and single-cell transcriptomics) to elucidate the causal relationships and underlying mechanisms between gut microbiota and their associated metabolites in the inflammatory response of sepsis. Building on this analysis, we employed machine learning algorithms to identify sepsis-specific diagnostic biomarkers derived from
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