Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the current COVID-19 pandemic, resulting in a public health crisis that required immediate action. The SARS-CoV-2 virus enters human cells via three receptors, namely cathepsin, angiotensin-converting enzyme 2 (ACE2) and SARS-CoV receptors. Cathepsin destroys the spike protein (S protein), thereby allowing the entry of viral nucleic acid into human host cells.Methods: Utilizing single-cell transcriptome analysis of brain tissues, the vulnerability of the central nervous system to infection with SARS-CoV-2 in humans was investigated.Results: ACE2 is mainly expressed in endothelial cells, with the highest levels found in ageing endothelial cells. Drug prediction suggests that (-)-catechin reduces the effects of COVID-19 on the nervous system. Immunohistochemistry analysis showed that ACE2 was mainly expressed in cerebral vessels. Immunofluroscenceresults showed the co-expression of CD31 and ACE2 in human tissues. Western blot further showed that ACE2 expression was higher in old rats than in young rats.Conclusion: This study provides insight into the mechanism of SARS-CoV-2 brain invasion. Accordingly, patients with neurological symptoms who are infected with SARS-CoV-2 should be given individualised care.

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With the development of Internet of Things (IOT), outsourcing data and tasks to a cloud server has become a popular and economical way for small devices with restricted ability. The k-nearest neighbors (KNNs) classification algorithm have been commonly applied to medical image classification, abnormal detection, defective product identification, and so on. The previous privacy-preserving KNN algorithms are based on two cloud servers, which have high computational and communication costs. In this paper, we design a privacy-preserving KNN classification (PPKC) algorithm with single cloud server for smart grid. Specifically, each smart meter and control center encrypted their data with Paillier cryptosystem and the cloud server does some calculation on the encrypted data. We prove that PPKC can protect the privacy of both smart meters and control centers, and the classification results are also private for the server. Besides, both smart meters and control centers can stay offline after uploading their data. The experiment results demonstrate that the proposed PPKC algorithm is more efficient than the previous algorithms and it can obtain almost the same accuracy as the original KNN, which means the PPKC algorithm is more applicable for small devices in IOT.

Nervus intermedius neuralgia (NIN) is a rare craniofacial neuralgia with features of paroxysmal pain in the deep ear. Because of sensory nerves overlap in the ear, the diagnosis of NIN is often difficult and not definitive. Here, we present the case of a 70-year-old woman who had deep-ear pain for more than 4 years and was diagnosed with trigeminal neuralgia and treated with carbamazepine without relief in another hospital. Magnetic resonance tomographic angiography revealed no neurovascular conflict with the trigeminal nerve, whereas the anterior inferior cerebellar artery (AICA) was close to the VII/VIII complex.We performed left-sided suboccipital retrosigmoid craniotomy. Surgical exploration under endoscopy clearly showed that the nervus intermedius was compressed by the AICA from behind. The ear pain was completely relieved immediately after nervus intermedius sectioning. The intraoperative findings and postoperative results confirmed that the compression of the nervus intermedius by the AICA caused the otalgia. A patient's specific pain, combined with preoperative imaging examination, is useful in the diagnosis of NIN.Neuroendoscopy has the advantages of enabling a clear field of view and close observation, thus aiding in the identification and accurate cutting of the nervus intermedius during the operation.