Evaporative self-assembling bioconcentrators onto superhydrophobic micropyramidal arrays as rapid and intelligent blood cancer filtering platforms

Abstract

Harnessing the power of artificial intelligence (AI) for rapid and accurate cancer diagnosis is invaluable for medical practice, but technically challenging due to the low sensitivity and low selectivity of traditional bio-analytical methods. Here, we explored an AI-driven filtering method integrated with a plasmonic bioconcentrator for rapid, sensitive, and accurate blood cancer diagnosis. The bioconcentrator were sequentially dual-assembled by sequentially depositing multi-walled carbon nanotubes and core-shell gold silicon dioxide nanoparticles onto a laser-fabricated superhydrophobic surface with micropyramidal arrays. The laser spectroscopic sensing technique, nanoparticle-enhanced laser-induced breakdown spectroscopy, was applied to obtain the fingerprint information of serum samples from four types blood cancers and one health control. Furthermore, the principal component analysis and support vector machine algorithms were performed to discriminate the serum samples for AI-driven filtering. The results demonstrated that the integration of AI-driven filtering with bioconcentrator enabled sensitive and accurate diagnosis of blood cancers with several minutes. Rapid, cost-effective, and personalized cancer treatment could be envisioned by the AI-driven cancer filtering with the plasmonic bioconcentrator.