Paper-Based Screen-Printed Ionic-Liquid/Graphene Electrode Integrated with Prussian Blue/MXene Nanocomposites Enabled Electrochemical Detection for Glucose Sensing.

As glucose biosensors play an important role in glycemic control, which can prevent the diabetic complications, the development of a glucose sensing platform is still in needed. Herein, the first proposal on the in-house fabricated paper-based screen-printed ionic liquid/graphene electrode (SPIL-GE) modified with MXene (Ti3C2Tx), prussian blue (PB), glucose oxidase (GOx), and Nafion is reported. The concentration of PB/Ti3C2Tx was optimized and the optimal detection potential of PB/Ti3C2Tx/GOx/Nafion/SPIL-GE is -0.05 V. The performance of PB/Ti3C2Tx/GOx/Nafion modified SPIL-GE was characterized by cyclic voltammetry and chronoamperometry technique. This paper-based platform integrated with nanomaterial composites were realized for glucose in the range of 0.0-15.0 mM with the correlation coefficient R2 = 0.9937. The limit of detection method and limit of quantification were 24.5 µM and 81.7 µM, respectively. In the method comparison, this PB/Ti3C2Tx/GOx/Nafion/SPIL-GE exhibits a good correlation with the reference hexokinase method. This novel glucose sensing platform can potentially be used for the good practice to enhance the sensitivity and open the opportunity to develop paper-based electroanalytical devices.

Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation.

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or absence of hemagglutination in the sample zones after immobilizing the antibodies targeting each Rh antigen. The PAD was optimized in terms of filter paper type, antibodies, and distance of the visualization zone. In this study, the optimal conditions were Whatman filter paper Grade 4; anti-D, -C, -E, -c, and -e antibodies; RBC suspension of 30%; and a visualization zone of 1 cm above the sample zone. The accuracy of simultaneously phenotyping the five Rh RBC antigens in the blood samples (n = 4692) was 99.19%, comparable with the accuracy of the gold-standard tube method used by blood bank laboratories in several regions of Thailand. Furthermore, decision making based on this method can be assisted by deep learning. After implementing a two-stage objective detection algorithm (YOLO v4-tiny) and classification model (DenseNet-201), the ambiguous images (n = 48) were interpreted with 100% accuracy. The PAD integrated with customized-region convolutional neural networks can reduce the interpretation discrepancies in RBC antigen phenotyping in any laboratory.