Detection of saxitoxin by a SERS aptamer sensor based on enzyme cycle amplification technology.

Saxitoxin (STX) is a typical toxic guanidinium neurotoxin, one of the paralytic shellfish poisons (PSP), which poses a serious threat to human health. In this paper, a simple and sensitive SERS aptamer sensor (abbreviated as AuNP@4-NTP@SiO2) for the quantitative determination of STX was developed. Hairpin aptamers of saxitoxin are modified on magnetic beads and used as recognition elements. In the presence of STX, DNA ligase, and the rolling circle template (T1), a rolling circle amplification reaction was triggered to produce long single-stranded DNA containing repetitive sequences. The sequence can be hybridized with the SERS probe to realize the rapid detection of STX. Due to the inherent merits of its components, the obtained AuNP@4-NTP@SiO2 SERS aptamer sensor manifests excellent sensing performance for STX detection with a wide linear range from 2.0 × 10-10 mol L-1 to 5.0 × 10-4 mol L-1 and a lower detection limit of 1.2 × 10-11 mol L-1. This SERS sensor can provide a strategy for the micro-detection of other biological toxins by changing the aptamer sequence.

Method for puncture trajectory planning in liver tumors thermal ablation based on NSGA-III.

BACKGROUND: Thermal ablation of liver tumors is a conventional mode for treating liver tumors. In order to reduce the damage to normal tissue endangered by thermal ablation, the physician needs to plan the puncture path before surgery. OBJECTIVE: In this paper, a puncture trajectory planning method for thermal ablation of liver tumor based on NSGA-III is proposed. This method takes the clinical hard constraints and soft constraints into account. METHOD: The feasible puncture region is solved by the hard constraints, and after that the pareto front points are obtained under the soft constraints. When accessing the feasible puncture region, an adaptive morphological closing operation method based on K-means algorithm is adopted to process the spherical angle binary image of obstacles that might be encountered in the puncture process. RANSAC is performed to fit the tangent plane of liver surface when calculating the angle between the puncture trajectory and liver surface. In order to evaluate the puncture path obtained by this method, 6 tumors are selected as experimental subjects, and Hausdorff distance and Overlap Rate of Pareto front points with manually recommend points are calculated respectively. RESULTS: The average value of Hausdorff distance is 24.91 mm, and the mean value of the overlap rate is 86.43%. CONCLUSION: The proposed method can provide high safety and clinical practice of the puncture route.

Multi-scale brain tumor segmentation combined with deep supervision.

PURPOSE: Fully convolutional neural networks (FCNNs) have achieved good performance in the field of medical image segmentation. FCNNs that use multimodal images and multi-scale feature extraction have higher accuracy for brain tumor segmentation. Therefore, we have made some improvements to U-Net for fully automated segmentation of gliomas using multimodal images. And we named it multi-scale dilate network with deep supervision (MSD-Net). METHODS: MSD-Net is a symmetrical structure composed of a down-sampling process and an up-sampling process. In the down-sampling process, we use the multi-scale feature extraction block (ME) to extract multi-scale features and focus on primary features. Unlike other methods, ME consists of dilate convolution and standard convolution. Dilate convolution extracts multi-scale informations and standard convolution merges features of different scales. Hence, the output of the ME contains local information and global information. During the up-sampling process, we add a deep supervision block (DSB), which can shorten the length of back-propagation. In this paper, we pay more attention to the importance of shallow features for feature restoration. RESULTS: Our network validated in the BraTS17's validation dataset. The DSC scores of MSD-Net for complete tumor, tumor core and enhancing tumor were 0.88, 0.81 and 0.78, respectively, which outperforms most networks. CONCLUSION: This study shows that ME enhances the feature extraction ability of the network and improves the accuracy of segmentation results. DSB speeds up the convergence of the network. In addition, we should also pay attention to the contribution of shallow features to feature restoration.