A target-triggered biosensing platform for detection of HBV DNA based on DNA walker and CHA.

Hepatitis B virus (HBV), one of the causative agents of viral hepatitis, may lead to chronic hepatitis, cirrhosis, and liver cancer. In this work, we designed a sensitive and modular biosensing platform for detecting HBV DNA based on a DNA walker that hangs on to surfaces and a catalyst-triggered catalyzed hairpin assembly (CHA). In the presence of HBV DNA, strand displacement reaction between target and double-stranded complex caused the release of walker strand to trigger the DNA walker. Then, a catalyst was free to open the trapped hairpins to form a new double-strand complex, driving the CHA reaction. Thus, a powerful cascade amplification reaction realized in DNA walker and CHA based on toehold-mediated strand displacement reaction in this system. To achieve quantitative detection of HBV DNA, a fluorescent-quencher signaling pair was employed, the turn-on fluorescence provided an analytical signal. A wide detection range from 0.5 nM to 50 nM with a detection limit as low as 0.20 nM was reached on the condition of acceptable specificity and reproducibility. We could also further apply it to multiple different bioanalysis by changing adjustable elements. This reported biosensor opened a new avenue for sensitivity and modularity of DNA detection.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.