Since December 2019, the infection of the new coronavirus (COVID-19) caused an outbreak of new coronavirus pneumonia in Wuhan, China, and caused great public concern. Both COVID-19 and SARS-CoV belong to the coronavirus family and both invade target cells through ACE2. An in-depth understanding of ACE2 and a series of physiological and physiological changes caused by the virus invading the human body may help to discover and explain the corresponding clinical phenomena and then deal with them timely. In addition, ACE2 is a potential therapeutic target. This article will summarize the role of ACE2 in multiple organ damage caused by COVID-19 and SARS-CoV, targeted blocking drugs against ACE2, and drugs that inhibit inflammation in order to provide the basis for subsequent related research, diagnosis and treatment, and drug development.

Diaphragmatic dysfunction refers to the limited ability of the diaphragm to produce inspiratory pressure. The involved patients include newborns, children performed with cardiopulmonary surgery, mechanical ventilators and children with neuromuscular diseases. Missing a diaphragmatic dysfunction diagnosis is easy due to the lack of specificity in clinical manifestations, resulting in serious consequences in children who are less tolerant to it. Ultrasound, which can observe diaphragmatic movement in two-dimensional and M-mode, has gradually replaced chest X-ray and fluoroscopy in diaphragmatic dysfunction diagnosis. Moreover, diaphragmatic plication is the most used method of surgical treatment, which can improve the affected diaphragm function. The quantitative diaphragm monitoring by ultrasound is of clinical significance for the recovery of critically ill children.

A new hydrodynamic artificial intelligence (AI) detection method is proposed to realize the accurate detection of internal solitary waves (ISWs) by the underwater vehicle. Two deep convolution neural network structures are established to predict the relative position between the underwater vehicle and ISW and the flow field around the underwater vehicle. By combining field observation data and the computational fluid dynamics (CFD) method, accurate numerical simulation of the motion of the underwater vehicle in a real ISW environment is achieved. The training process for the neural network is implemented by building a dataset from the above results. It is shown that the position prediction accuracy of the network for ISW is larger than 95%. For the prediction of the flow field around the underwater vehicle, it is found that the addition of the convolutional block attention module (CBAM) can increase the prediction accuracy. Moreover, the reduction of the number of sensors by the dynamic mode decomposition (DMD) method and k-means clustering method is realized. The accuracy can still reach 92% even when the number of sensors is reduced. This study is the first to use hydrodynamic signals for the detection of ISW, which can enhance the navigation safety of underwater vehicles.

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