An ultrasensitive electrochemical immunosensor for SLC2A3 detection enables prognostic prediction in acute ischemic stroke.

Acute ischemic stroke (AIS) presents significant challenges in biomarker discovery and detection. This study addresses these hurdles through an integrated strategy encompassing multi-omics screening, experimental validation, machine learning-based prognostic assessment, and the development of a high-performance electrochemical immunosensor. We identified solute carrier family 2 member 3 (SLC2A3) as a novel candidate biomarker through combined transcriptomic and proteomic analysis. Its upregulation was confirmed in a mouse middle cerebral artery occlusion followed by reperfusion (MCAO I/R) model and in a prospective clinical cohort of 449 AIS patients. A Gradient Boosting Machine (GBM) model, incorporating SLC2A3 levels, effectively predicted 90-day functional outcomes (validation AUC = 0.933). For ultra-sensitive detection, a sandwich-type electrochemical immunosensor was fabricated. The sensing platform utilized a carboxylated single-walled carbon nanotubes (SWCNT) integrated with gold nanoparticles (AuNPs)-modified electrode. Signal amplification was achieved using a novel NH2-MIL-88B@PAA@PtCu nanocomposite probe, which combined the high catalytic activity of PtCu nanoflowers with the excellent loading capacity of the Fe-metal-organic framework (MOF). The immunosensor demonstrated outstanding performance for SLC2A3 detection: a wide linear range (0.078-10 ng mL-1), an ultra-low detection limit (9.3 pg mL-1), high specificity, and good reproducibility. Recovery rates in spiked human serum were 100.5-104.0%. This work establishes a complete pipeline from biomarker identification to practical sensing device development, offering a promising translational approach for prognostic assessment in AIS.