Yolk-shell structured nanoreactor Au@Co3O4/CeO2@mSiO2 with superior peroxidase-like activity as nanozyme for ultra-sensitive colorimetric biosensing.

For yolk-shell structured nanoreactors, multiple active components can be precisely positioned on core and/or shell that can afford more exposed accessible active sites, and the internal voids can guarantee sufficient contact of reactants and catalysts. In this work, a unique yolk-shell structured nanoreactor Au@Co3O4/CeO2@mSiO2 was fabricated and applied as nanozyme for biosensing. The Au@Co3O4/CeO2@mSiO2 exhibited superior peroxidase-like activity with a lower Michaelis constant (Km) and a higher affinity to H2O2. The enhanced peroxidase-like activity was attributed to the unique structure and the synergistic effects between the multiple active components. Colorimetric essays were developed based on Au@Co3O4/CeO2@mSiO2 for the ultra-sensitive sensing of glucose in the range of 3.9 nM-1.03 mM with the limit of detection as low as 3.2 nM. In the detection of glucose-6-phosphate dehydrogenase (G6PD), the cooperation between G6PD and Au@Co3O4/CeO2@mSiO2 triggered the redox cycling between NAD+ and NADH, thereby achieving the amplification of the signal and enhancing the sensitivity of the assay. The assay showed superior performance as compared to other methods with linear response of 5.0 × 10-3-15 mU mL-1 and lower detection limit of 3.6 × 10-3 mU mL-1. The fabricated novel multi-enzyme catalytical cascade reaction system allowed rapid and sensitive biodetection, demonstrating its potential in biosensors and biomedical applications.

Graph-generative neural network for EEG-based epileptic seizure detection via discovery of dynamic brain functional connectivity.

Dynamic complexity in brain functional connectivity has hindered the effective use of signal processing or machine learning methods to diagnose neurological disorders such as epilepsy. This paper proposed a new graph-generative neural network (GGN) model for the dynamic discovery of brain functional connectivity via deep analysis of scalp electroencephalogram (EEG) signals recorded from various regions of a patient's scalp. Brain functional connectivity graphs are generated for the extraction of spatial-temporal resolution of various onset epilepsy seizure patterns. Our supervised GGN model was substantiated by seizure detection and classification experiments. We train the GGN model using a clinically proven dataset of over 3047 epileptic seizure cases. The GGN model achieved a 91% accuracy in classifying seven types of epileptic seizure attacks, which outperformed the 65%, 74%, and 82% accuracy in using the convolutional neural network (CNN), graph neural networks (GNN), and transformer models, respectively. We present the GGN model architecture and operational steps to assist neuroscientists or brain specialists in using dynamic functional connectivity information to detect neurological disorders. Furthermore, we suggest to merge our spatial-temporal graph generator design in upgrading the conventional CNN and GNN models with dynamic convolutional kernels for accuracy enhancement.