Single-Protein Determinations by Magnetofluorescent Qubit Imaging with Artificial-Intelligence Augmentation at the Point-Of-Care.
Journal:
ACS nano
Published Date:
May 19, 2025
Abstract
Conventional point-of-care testing (POCT) has limitations in sensitivity with high risks of missed detection or false positive, which restrains its applications for routine outpatient care analysis and early clinical diagnosis. By merits of the cutting-edge quantum precision metrology, this study devised a mini quantum sensor via magnetofluorescent qubit tagging and tunning on core-shelled fluorescent nanodiamond FND@SiO. Comprehensive characterizations confirmed the formation of FND biolabels, while spectroscopies secured no degradation in spin-state transition after surface modification. A methodical parametrization was deliberated and decided, accomplishing a wide-field modulation depth ≥15% in ∼ zero field, which laid foundation for supersensitive sensing at single-FND resolution. Using viral nucleocapsid protein as a model marker, an ultralow limit of detection (LOD) was obtained by lock-in analysis, outperforming conventional colorimetry and immunofluorescence by > 2000 fold. Multianalyte and affinity assays were also enabled on this platform. Further by resort to artificial-intelligence (AI) augmentation in the Unet-ConvLSTM-Attention architecture, authentic qubit dots were identified by pixelwise survey through pristine qubit queues. Such processing not just improved pronouncedly the probing precision but also achieved deterministic detections down to a single protein in human saliva with an ultimate LOD as much as 7800-times lower than that of colloidal Au approach, which competed with the RT-qPCR threshold and the certified critical value of SIMOA, the gold standard. Hence, by AI-aided digitization on optic qubits, this REASSURED-compliant contraption may promise a next-generation POCT solution with unparalleled sensitivity, speed, and cost-effectiveness, which in whole confers a conclusive proof of the prowess of the burgeoning quantum metrics in biosensing.
