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A dye-assisted paper-based point-of-care assay for fast and reliable blood grouping
Hong Zhang1,2, Xiaopei Qiu1,2, Yurui Zou1,2
1Center for Nanomedicine, Southwest Hospital, Third Military Medical University, Chongqing, China.
Fast and simultaneous forward and reverse blood grouping has long remained elusive. Forward blood grouping detects antigens on red blood cells, whereas reverse grouping identifies specific antibodies present in plasma. We developed a paper-based assay using immobilized antibodies and bromocresol green dye for rapid and reliable blood grouping, where dye-assisted color changes corresponding to distinct blood components provide a visual readout. ABO antigens and five major Rhesus antigens could be detected within 30 s, and simultaneous forward and reverse ABO blood grouping using small volumes (100 μl) of whole blood was achieved within 2 min through on-chip plasma separation without centrifugation. A machine-learning method was developed to classify the spectral plots corresponding to dye-based color changes, which enabled reproducible automatic grouping. Using optimized operating parameters, the dye-assisted paper assay exhibited comparable accuracy and reproducibility to the classical gel-card assays in grouping 3550 human blood samples. When translated to the assembly line and low-cost manufacturing, the proposed approach may be developed into a cost-effective and robust universal blood-grouping platform.
Fast and simultaneous forward and reverse blood grouping has long remained elusive. Forward blood grouping detects antigens on red blood cells, whereas reverse grouping identifies specific antibodies present in plasma. We developed a paper-based assay using immobilized antibodies and bromocresol green dye for rapid and reliable blood grouping, where dye-assisted color changes corresponding to distinct blood components provide a visual readout. ABO antigens and five major Rhesus antigens could be detected within 30 s, and simultaneous forward and reverse ABO blood grouping using small volumes (100 μl) of whole blood was achieved within 2 min through on-chip plasma separation without centrifugation. A machine-learning method was developed to classify the spectral plots corresponding to dye-based color changes, which enabled reproducible automatic grouping. Using optimized operating parameters, the dye-assisted paper assay exhibited comparable accuracy and reproducibility to the classical gel-card assays in grouping 3550 human blood samples. When translated to the assembly line and low-cost manufacturing, the proposed approach may be developed into a cost-effective and robust universal blood-grouping platform.

