Due to the significance of hydrogen peroxide (H(2)O(2)) in biological systems and its practical applications, the development of efficient electrochemical H(2)O(2) sensors holds a special attraction for researchers. Various materials such as Prussian blue (PB), heme proteins, carbon nanotubes (CNTs) and transition metals have been applied to the construction of H(2)O(2) sensors. In this article, the electrocatalytic H(2)O(2) determinations are mainly focused on because they can provide a superior sensing performance over non-electrocatalytic ones. The synergetic effect between nanotechnology and electrochemical H(2)O(2) determination is also highlighted in various aspects. In addition, some recent progress for in vivo H(2)O(2) measurements is also presented. Finally, the future prospects for more efficient H(2)O(2) sensing are discussed.

Various retinal vessel segmentation methods based on convolutional neural networks were proposed recently, and Dense U-net as a new semantic segmentation network was successfully applied to scene segmentation. Retinal vessel is tiny, and the features of retinal vessel can be learned effectively by the patch-based learning strategy. In this study, we proposed a new retinal vessel segmentation framework based on Dense U-net and the patch-based learning strategy. In the process of training, training patches were obtained by random extraction strategy, Dense U-net was adopted as a training network, and random transformation was used as a data augmentation strategy. In the process of testing, test images were divided into image patches, test patches were predicted by training model, and the segmentation result can be reconstructed by overlapping-patches sequential reconstruction strategy. This proposed method was applied to public datasets DRIVE and STARE, and retinal vessel segmentation was performed. Sensitivity (Se), specificity (Sp), accuracy (Acc), and area under each curve (AUC) were adopted as evaluation metrics to verify the effectiveness of proposed method. Compared with state-of-the-art methods including the unsupervised, supervised, and convolutional neural network (CNN) methods, the result demonstrated that our approach is competitive in these evaluation metrics. This method can obtain a better segmentation result than specialists, and has clinical application value.

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In vertebrate embryos Hedgehog (Hh) is expressed by notochord and floorplate cells, and ventral neural cells are patterned by the activities of Hh-regulated transcription factors. Hh signalling is antagonised by signals from the dorsal neural tube, and loss of Hh leads to loss of ventral patterning in the neural tube as the dorsal pattern expands. These mechanisms are critical for producing the neurons that implement motor responses to sensory inputs, but understanding how they evolved has been hindered by lack of insight from commonly-studied invertebrates where nervous system morphology and genetic mechanisms are not conserved with those of vertebrates. The invertebrate chordate amphioxus, which expresses Hh in its notochord and floorplate, provides a window into the pre-vertebrate condition. We have examined amphioxus neural development by manipulating function of Hh and downstream genes involved in neural pattern and cell identity. We show that Hh signalling regulates the differentiation of some neurons in amphioxus, including a subset of motor neurons. This demonstrates some conservation of mechanism between vertebrates and amphioxus. However other aspects of neural patterning differ between the two lineages, with amphioxus lacking aspects important in vertebrates. We suggest the complexity of Hhdependent neural patterning in vertebrates evolved in a step-wise manner. Initial recruitment of Hh occurred in an ancestor to the chordates to regulate the differentiation of a subset of neurons. This was followed, in the vertebrate lineage, by additional changes to the gene regulatory network downstream of Hh, which gave Hh a broader role in dorsal-ventral neural patterning.