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3D-printed MOF-hydrogel microarray for multiplexed miRNA detection and early herb-induced liver injury warning
Yuzhi Liang1, Lu Wang1, Hongjun Wu1
1School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210023, PR China.
A novel 3D-printed microarray detects specific microRNAs (miRNAs) for early herb-induced liver injury (HILI) detection. This advanced platform offers a faster, more sensitive warning than traditional tests, improving patient outcomes.
Area of Science:
- Biomedical Engineering
- Molecular Diagnostics
- Toxicology
Background:
- Herb-induced liver injury (HILI) lacks sensitive and timely biomarkers beyond ALT/AST.
- Circulating microRNAs (miRNAs) like miR-122 and miR-194 show promise for liver injury detection.
- Detecting low-abundance analytes requires advanced sensing technologies.
Purpose of the Study:
- To develop a highly sensitive and rapid diagnostic platform for early HILI detection.
- To validate a novel circulating miRNA biomarker panel (miR-122 and miR-194) for HILI.
- To integrate advanced materials and fabrication techniques for a high-throughput sensor array.
Main Methods:
- Fabrication of a 3D-printed multifunctional metal-organic framework (MOF)-hydrogel microarray (3D-MHMA).
- Integration of UiO-66-NH2 MOF nanoparticles for target enrichment and fluorescence quenching.
- Utilized a catalytic hairpin assembly-hybridization chain reaction (CHA-HCR) cascade for signal amplification.
- 3D bioprinting for creating a high-throughput, spatially encoded sensor array.
Main Results:
- Achieved ultra-sensitive detection of miR-122 and miR-194 with a limit of detection of 2.61 fM.
- Demonstrated reliable miRNA profiling in cellular and animal models of triptolide-induced liver injury.
- Showcased that miRNA release significantly preceded ALT/AST elevation, providing an early diagnostic window (AUC >0.9).
Conclusions:
- The 3D-MHMA platform coupled with miR-122/miR-194 biomarkers enables early HILI detection.
- This integrated solution offers a superior diagnostic window compared to conventional methods.
- Presents a promising approach for early warning systems in clinical toxicology and personalized medicine.
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