Microbead-based suspension immuoassay in a lab-on-a-disc

A portable, disc-based, and fully automated enzyme-linked immuno-sorbent assay (ELISA) system is developed to test infectious diseases from whole blood. The innovative laser irradiated ferrowax microvalves and centrifugal microfluidics were utilized for the full integration of the microbead-based suspension ELISA assays on a disc starting from whole blood. The concentrations of the antigen and the antibody of Hepatitis B virus (HBV), HBsAg and Anti-HBs respectively, were measured using the lab-on-a-disc (LOD). All the necessary reagents are preloaded on the disc and the total process of the plasma separation, incubation with target specific antigen or antibody coated microbeads, multiple steps of washing, enzyme reaction with substrates, and the absorbance detection could be finished within 30 minutes. Compared to the conventional ELISA, the operation time was dramatically reduced from over 2 hours to less than 30 minutes while the limit of detection was kept similar; e.g. the limit of detection of Anti-HBs tests were 8.6 mIU mL-1 and 10 mIU mL-1 for the disc-based and the conventional ELISA respectively

Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification

Recently, deep learning for automated sleep stage classification has been introduced with promising results. However, as many challenges impede their routine application, automatic sleep scoring algorithms are not widely used. Typically, polysomnography (PSG) uses multiple channels for higher accuracy; however, the disadvantages include a requirement for a patient to stay one or more nights in the lab wearing uncomfortable sensors and wires. To avoid the inconvenience caused by the multiple channels, we aimed to develop a deep learning model for use in clinical decision support systems (CDSSs) and combined convolutional neural networks and a transformer for the supervised learning of three classes of sleep stages only with single-channel EEG data (C4-M1). The data for training, validation, and test were derived from 1590, 341, and 343 polysomnography recordings, respectively. The developed model yielded an overall accuracy of 91.4%, comparable with that of human experts. Based on the severity of obstructive sleep apnea, the model’s accuracy was 94.3%, 91.9%, 91.9%, and 90.6% in normal, mild, moderate, and severe cases, respectively. Our deep learning model enables accurate and rapid delineation of three-class sleep staging and could be useful as a CDSS for application in real-world clinical practice