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The traditional named entity disambiguation technology usually relies on rich context and knowledge of external entities. However, many emerging entities lack knowledge bases and the text containing entities is short. These limitations make traditional algorithms unable to make full use of contextual semantic information. At the same time,due to the limitation of the number of effective samples, the final application scenarios of the algorithm are very limited. Based on the above defects, this paper proposes a deep learning-based entity disambiguation method combining bidirectional encoder representation from transformers(BERT) model and long short-term memory neural network.The main work are the following parts: 1) A word vector based on the BERT model is designed to obtain more information through fewer data samples. 2) In order to allow the long short-term memory neural networks to retain useful information and verify that the short text applies to the method of this article, this method segments the sentence samples. 3) This article uses the neural network intelligence(NNI) technology proposed by Microsoft, which makes it possible to quickly and efficiently obtain the optimal neural network hyperparameter. This study compares other different types of word vectors and neural network technology, confirming that the F-Measure value of the entity disambiguation technology based on deep learning used in this paper is higher.

Background: Spinal cord injury (SCI) causes devastating neurological damage, including secondary injuries dominated by neuroinflammation. The role of apelin, an endogenous ligand that binds the G protein-coupled receptor angiotensin-like receptor 1, in SCI remains unclear. Thus, our aim was to investigate the effects of apelin in inflammatory responses and activation of endogenous neural stem cells (NSCs) after SCI.Methods: Apelin expression was detected in normal and injured rats, and roles of apelin in primary NSCs were examined. In addition, we used induced pluripotent stem cells (iPSCs) as a carrier to prolong the effective duration of apelin and evaluate its effects in a rat model of SCI.Results: Co-immunofluorescence staining suggested that apelin was expressed in both astrocytes and neurons. Following SCI, apelin expression decreased from 1–14 d and was re-upregulated at 28 d. Apelin promoted NSC proliferation and differentiation into neurons and oligodendrocytes. In vivo, lentiviral-transfected iPSCs were used as a carrier to prolong the effective duration of apelin. Transplantation of transfected iPSCs in situ immediately after SCI reduced activation of microglia and A1 astrocytes, facilitated recovery of motor function, and promoted the proliferation and differentiation of endogenous NSCs in rats.Conclusion: Apelin alleviated neuroinflammation and promoted the proliferation and differentiation of endogenous NSCs after SCI, suggesting that it might be a promising target for treatment of SCI.