Peripheral nerve regeneration remains one of the greatest challenges in regenerative medicine. Deprivation of sensory and/or motor functions often occurs with severe injuries even treated by the most advanced microsurgical intervention. Although electrical stimulation represents an essential nonpharmacological therapy that proved to be beneficial for nerve regeneration, the postoperative delivery at surgical sites remains daunting. Here, a fully biodegradable, self-electrified, and miniaturized device composed of dissolvable galvanic cells on a biodegradable scaffold is achieved, which can offer both structural guidance and electrical cues for peripheral nerve regeneration. The electroactive device can provide sustained electrical stimuli beyond intraoperative window, which can promote calcium activity, repopulation of Schwann cells, and neurotrophic factors. Successful motor functional recovery is accomplished with the electroactive device in behaving rodent models. The presented materials options and device schemes provide important insights into self-powered electronic medicine that can be critical for various types of tissue regeneration and functional restoration.
BackgroundMutT-related proteins, including MTH1, MTH2, MTH3 and NUDT5, can effectively degrade 8-oxoGua-containing nucleotides. The MTH1 expression is elevated in many types of human tumors and MTH1 overexpression correlates with the tumor pathological stage and poor prognosis. However, the expression of other MutT-related proteins in human cancers remains unknown. The present study systematically investigated the expression of MTH1, MTH2, MTH3 and NUDT5 in human colorectal cancer to establish its clinical significance.MethodsAmounts of MutT-related mRNA and protein in CRC cell lines were assessed by qRT-PCR and Western blotting, respectively. Furthermore, the MutT-related protein expression was evaluated by immunohistochemical staining of tissue microarrays containing 87 paired CRC tissues and by Western blotting of 44 CRC tissue samples. Finally, the effect of knockdown of MutT-related proteins on CRC cell proliferation was investigated.ResultsThe expression of MTH1, MTH2, MTH3 and NUDT5 was significantly higher in CRC cells and CRC tissues than normal cells and tissues, and this phenomenon was significantly associated with AJCC stage and lymph node metastasis of CRC specimens. CRC patients with high expression of MTH1, MTH2 or NUDT5 had an extremely poor overall survival after surgical resection. Notably, NUDT5 was an independent prognostic factor of CRC patients. We found that knockdown of MutT-related proteins inhibited CRC cell proliferation.ConclusionsWe showed for the first time that MutT-related proteins play an important role in CRC progression and prognosis. Further investigations are needed to elucidate the role of these proteins in CRC progression and their potential use for therapeutic targets.
The Didi Dache app is China’s biggest taxi booking mobile app and is popular in cities. Unsurprisingly, short-term traffic demand forecasting is critical to enabling Didi Dache to maximize use by drivers and ensure that riders can always find a car whenever and wherever they may need a ride. In this paper, a short-term traffic demand forecasting model, Wave SVM, is proposed. It combines the complementary advantages of Daubechies5 wavelets analysis and least squares support vector machine (LS-SVM) models while it overcomes their respective shortcomings. This method includes four stages: in the first stage, original data are preprocessed; in the second stage, these data are decomposed into high-frequency and low-frequency series by wavelet; in the third stage, the prediction stage, the LS-SVM method is applied to train and predict the corresponding high-frequency and low-frequency series; in the last stage, the diverse predicted sequences are reconstructed by wavelet. The real taxi-hailing orders data are applied to evaluate the model’s performance and practicality, and the results are encouraging. The Wave SVM model, compared with the prediction error of state-of-the-art models, not only has the best prediction performance but also appears to be the most capable of capturing the nonstationary characteristics of the short-term traffic dynamic systems.
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