ArticleInternational journal of nanomedicine2025
Post-Transplant Liver Monitoring Utilizing Integrated Surface-Enhanced Raman and AI in Hepatic Ischemia-Reperfusion Injury Animal Model.
Article in International journal of nanomedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
- Serum miR-381-3p: Diagnostic Role and Mechanisms in Liver Transplant Ischemia-reperfusion Injury.Journal of clinical and translational hepatology · 2026Article
- Damage-associated molecular patterns in hepatic ischemia-reperfusion injury: spatiotemporal signatures, biomarker potential, and clinical translation.Frontiers in immunology · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: While liver transplantation saves lives from irreversible liver damage, it poses challenges such as graft dysfunction due to factors like ischemia-reperfusion (IR) injury, which can lead to significant cellular damage and systemic complications. Current diagnostic tools for detecting IR injury have limitations, necessitating advanced methods for timely intervention. This study explores the integration of surface-enhanced Raman spectroscopy (SERS) with artificial intelligence (AI) to improve diagnostic accuracy for liver IR injury. Materials and Methods: IR injury was induced using a mouse model, and histopathological and hepatic functional evaluations were conducted alongside SERS measurements. Raman signals obtained via SERS chips, which selectively filter nano-biomarkers and enhance signals, were analyzed using machine learning algorithms. Results: The PC-LDA derived from spectra achieved an accuracy of 93.13%, while a machine learning algorithm based on PC-derived PLS-DA improved accuracy to 98.75%. Discussion: Our findings emphasize the potential of combining SERS with AI to detect and specifically identify dysfunction due to liver damage early, potentially advancing patient management in liver transplantation.
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Registered trials
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