Transfer learning based cascaded deep learning network and mask recognition for COVID-19.

The COVID-19 is still spreading today, and it has caused great harm to human beings. The system at the entrance of public places such as shopping malls and stations should check whether pedestrians are wearing masks. However, pedestrians often pass the system inspection by wearing cotton masks, scarves, etc. Therefore, the detection system not only needs to check whether pedestrians are wearing masks, but also needs to detect the type of masks. Based on the lightweight network architecture MobilenetV3, this paper proposes a cascaded deep learning network based on transfer learning, and then designs a mask recognition system based on the cascaded deep learning network. By modifying the activation function of the MobilenetV3 output layer and the structure of the model, two MobilenetV3 networks suitable for cascading are obtained. By introducing transfer learning into the training process of two modified MobilenetV3 networks and a multi-task convolutional neural network, the ImagNet underlying parameters of the network models are obtained in advance, which reduces the computational load of the models. The cascaded deep learning network consists of a multi-task convolutional neural network cascaded with these two modified MobilenetV3 networks. A multi-task convolutional neural network is used to detect faces in images, and two modified MobilenetV3 networks are used as the backbone network to extract the features of masks. After comparing with the classification results of the modified MobilenetV3 neural network before cascading, the classification accuracy of the cascading learning network is improved by 7%, and the excellent performance of the cascading network can be seen.

Therapeutic prospects of ceRNAs in COVID-19

Since the end of 2019, COVID-19 caused by SARS-CoV-2 has spread worldwide, and the understanding of the new coronavirus is in a preliminary stage. Currently, immunotherapy, cell therapy, antiviral therapy, and Chinese herbal medicine have been applied in the clinical treatment of the new coronavirus;however, more efficient and safe drugs to control the progress of the new coronavirus are needed. Long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and circular RNAs (circRNAs) may provide new therapeutic targets for novel coronavirus treatments. The first aim of this paper is to review research progress on COVID-19 in the respiratory, immune, digestive, circulatory, urinary, reproductive, and nervous systems. The second aim is to review the body systems and potential therapeutic targets of lncRNAs, miRNAs, and circRNAs in patients with COVID-19. The current research on competing endogenous RNA (ceRNA) (lncRNA-miRNA-mRNA and circRNA-miRNA-mRNA) in SARS-CoV-2 is summarized. Finally, we predict the possible therapeutic targets of four lncRNAs, MALAT1, NEAT1, TUG1, and GAS5, in COVID-19. Importantly, the role of PTEN gene in the ceRNA network predicted by lncRNA MALAT1 and lncRNA TUG1 may help in the discovery and clinical treatment of effective drugs for COVID-19.