Vessel-Like Microtunnels with Biomimetic Octopus Tentacles for Seizing and Detecting Exosomes to Diagnose Pancreatic...

Microchip-based exosome analysis has emerged as a promising approach for liquid biopsy in cancer diagnosis, treatment monitoring, and prognostic evaluation. However, current microchips for exosome analysis typically rely on planar, 2D channel structures with affinity properties, which require complex fabrication but deliver suboptimal separation and detection performance. This study presents a novel vessel-like microtunnel chip, integrated with biomimetic octopus tentacles, achieving an exosome isolation efficiency of 90.4%. The innovative design incorporates interwoven, 3D micropathways, enhancing fluid dynamics and promoting efficient mixing between exosomes and microchannels. Nanofiber-coated silicon microspheres, functionalized with synthetic peptides, mimic octopus tentacles to anchor the microtunnels, dynamically extending under fluid shear forces to specifically recognize lipid bilayer structures for exosome capture. This platform incorporates enzyme-catalyzed signal amplification using Au nanoprobes for colorimetric detection to sensitively analyze four protein markers on plasma-derived exosomes from 60 clinical samples. Machine learning is used to develop a diagnostic model, achieving an area under the curve (AUC) of 0.9888 in distinguishing pancreatic cancer from pancreatitis and healthy controls. This approach provides a rapid, sensitive, accurate, and user-friendly method for pancreatic cancer diagnosis, addressing the clinical challenges of early detection and the frequent misdiagnosis of pancreatic cancer as pancreatitis.