ReviewFrontiers in immunology2026
Neutrophil extracellular traps and acute kidney injury: a narrative review.
Review in Frontiers in immunology, 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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Authors and funding
9 authors.
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Abstract
Neutrophil extracellular traps (NETs) are complex, network-like structures comprising chromatin filaments bound to various antimicrobial proteins. While they play a role in host defence under physiological conditions, there is evidence to suggest that excessive production or impaired clearance of NETs may contribute to the pathological progression of acute kidney injury (AKI). This review analyses the heterogeneous roles of NETs composed of various components, such as histones, DNA and associated enzymes, in mediating renal tissue damage. It also explores their involvement in ischaemia-reperfusion injury, haemolytic uraemic syndrome, sepsis and autoimmune-associated AKI. Furthermore, this article reviews existing NET intervention strategies and considers the potential for personalised treatment strategies in different disease contexts. However, translating NET-related research into clinical practice faces numerous challenges, including the lack of standardised biomarkers for real-time monitoring of NET levels and preserving the host's essential anti-infective immune pathways while inhibiting tissue damage. Future research should therefore focus on elucidating the precise regulatory mechanisms underlying NET formation, as well as exploring methods for the targeted delivery of drugs to the kidneys. This would enhance the specificity and safety of treatment.
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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 Socratic graph.