The E3 ligase RNF5 restricts SARS-CoV-2 replication by targeting its envelope protein for degradation.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a severe global health crisis; its structural protein envelope (E) is critical for viral entry, budding, production, and induction of pathology which makes it a potential target for therapeutics against COVID-19. Here, we find that the E3 ligase RNF5 interacts with and catalyzes ubiquitination of E on the 63rd lysine, leading to its degradation by the ubiquitin-proteasome system (UPS). Importantly, RNF5-induced degradation of E inhibits SARS-CoV-2 replication and the RNF5 pharmacological activator Analog-1 alleviates disease development in a mouse infection model. We also found that RNF5 is distinctively expressed in different age groups and in patients displaying different disease severity, which may be exploited as a prognostic marker for COVID-19. Furthermore, RNF5 recognized the E protein from various SARS-CoV-2 strains and SARS-CoV, suggesting that targeting RNF5 is a broad-spectrum antiviral strategy. Our findings provide novel insights into the role of UPS in antagonizing SARS-CoV-2 replication, which opens new avenues for therapeutic intervention to combat the COVID-19 pandemic.

COVID-19 ground-glass opacity segmentation based on fuzzy c-means clustering and improved random walk algorithm.

Accurate segmentation of ground-glass opacity (GGO) is an important premise for doctors to judge COVID-19. Aiming at the problem of mis-segmentation for GGO segmentation methods, especially the problem of adhesive GGO connected with chest wall or blood vessel, this paper proposes an accurate segmentation of GGO based on fuzzy c-means (FCM) clustering and improved random walk algorithm. The innovation of this paper is to construct a Markov random field (MRF) with adaptive spatial information by using the spatial gravity Model and the spatial structural characteristics, which is introduced into the FCM model to automatically balance the insensitivity to noise and preserve the effectiveness of image edge details to improve the clustering accuracy of image. Then, the coordinate values of nodes and seed points in the image are combined with the spatial distance, and the geodesic distance is added to redefine the weight. According to the edge density of the image, the weight of the grayscale and the spatial feature in the weight function is adaptively calculated. In order to reduce the influence of edge noise on GGO segmentation, an adaptive snowfall model is proposed to preprocess the image, which can suppress the noise without losing the edge information. In this paper, CT images of different types of COVID-19 are selected for segmentation experiments, and the experimental results are compared with the traditional segmentation methods and several SOTA methods. The results suggest that the paper method can be used for the auxiliary diagnosis of COVID-19, so as to improve the work efficiency of doctors.

Asymptomatic COVID-19 CT image denoising method based on wavelet transform combined with improved PSO.

The quality of asymptomatic corona virus disease 2019 (COVID-19) computed tomography (CT) image is reduced due to interference from Gaussian noise, which affects the subsequent image processing. Aiming at the problem that asymptomatic COVID-19 CT image often have small flake ground-glass shadow in the early lesions, and the density is low, which is easily confused with noise. A denoising method of wavelet transform with shrinkage factor is proposed. The threshold decreases with the increase of decomposition scale, and it reduces the misjudgment of signal points. In the advanced stage, the range of lesions increases, with consolidation and fibrosis in different sizes, which have similar gray value to the CT images of suspected cases. Aiming at the problems of low contrast and fuzzy boundary in the traditional wavelet transform, the threshold function based on the optimization of parameters combined with the improved particle swam optimization (PSO) is proposed, so that the parameters of wavelet threshold function can change adaptively according to the lung lobe and ground-glass lesions with fewer iterations. The simulation results show that the paper method is significantly better than other algorithms in peak signal-to-noise ratio (PSNR), signal-to-noise ratio (SNR) and mean absolute error (MSE). For example, aiming at the early asymptomatic COVID-19, compared with the comparison methods, the PSNR under the proposed method has increased by about 5 dB, the MSE has been greatly reduced, and the SNR has increased by about 6.1 dB. It can be seen that the denoising effect under the proposed method is the best.