Bovine lactoferrin inhibits SARS-CoV-2 and SARS-CoV-1 by targeting the RdRp complex and alleviates viral infection in the hamster model.

Breast milk has been found to inhibit coronavirus infection, while the key components and mechanisms are unknown. We aimed to determine the components that contribute to the antiviral effects of breastmilk and explore their potential mechanism. Lactoferrin (Lf) and milk fat globule membrane (MFGM) inhibit SARS-CoV-2 related coronavirus GX_P2V and SARS-CoV-2 trVLP in vitro and block viral entry into cells. We confirmed that bovine lactoferrin (bLf) blocked the binding between human angiotensin-converting enzyme 2 (hACE2) and SARS-CoV-2 spike protein by combining receptor binding domain (RBD). Importantly, bLf inhibited RNA-dependent RNA polymerase (RdRp) activity of both SARS-CoV-2 and SARS-CoV in vitro in the nanomolar range. So far, no biological macromolecules have been reported to inhibit coronavirus RdRp. Our result indicated that bLf plays a major role in inhibiting viral replication rather than viral entry, which has been widely explored. bLf treatment reduced viral load in lungs and tracheae and alleviated pathological damage. Our study provides evidence that bLf prevents SARS-CoV-2 infection by combining SARS-CoV-2 spike protein RBD and inhibiting coronaviruses' RdRp activity, and may be a promising candidate for the treatment of COVID-19. This article is protected by copyright. All rights reserved.

Spatiotemporal Feature Learning Based on Two-Step LSTM and Transformer for CT Scans (preprint)

Computed tomography (CT) imaging could be very practical for diagnosing various diseases. However, the nature of the CT images is even more diverse since the resolution and number of the slices of a CT scan are determined by the machine and its settings. Conventional deep learning models are hard to tickle such diverse data since the essential requirement of the deep neural network is the consistent shape of the input data. In this paper, we propose a novel, effective, two-step-wise approach to tickle this issue for COVID-19 symptom classification thoroughly. First, the semantic feature embedding of each slice for a CT scan is extracted by conventional backbone networks. Then, we proposed a long short-term memory (LSTM) and Transformer-based sub-network to deal with temporal feature learning, leading to spatiotemporal feature representation learning. In this fashion, the proposed two-step LSTM model could prevent overfitting, as well as increase performance. Comprehensive experiments reveal that the proposed two-step method not only shows excellent performance but also could be compensated for each other. More specifically, the two-step LSTM model has a lower false-negative rate, while the 2-step Swin model has a lower false-positive rate. In summary, it is suggested that the model ensemble could be adopted for more stable and promising performance in real-world applications.

Understanding the Secret of SARS-CoV-2 Variants of Concern/Interest and Immune Escape.

The global pandemic of the coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), places a heavy burden on global public health. Four SARS-CoV-2 variants of concern including B.1.1.7, B.1.351, B.1.617.2, and P.1, and two variants of interest including C.37 and B.1.621 have been reported to have potential immune escape, and one or more mutations endow them with worrisome epidemiologic, immunologic, or pathogenic characteristics. This review introduces the latest research progress on SARS-CoV-2 variants of interest and concern, key mutation sites, and their effects on virus infectivity, mortality, and immune escape. Moreover, we compared the effects of various clinical SARS-CoV-2 vaccines and convalescent sera on epidemic variants, and evaluated the neutralizing capability of several antibodies on epidemic variants. In the end, SARS-CoV-2 evolution strategies in different transmission stages, the impact of different vaccination strategies on SARS-CoV-2 immune escape, antibody therapy strategies and COVID-19 epidemic control prospects are discussed. This review will provide a systematic and comprehensive understanding of the secret of SARS-CoV-2 variants of interest/concern and immune escape.

Visual Transformer with Statistical Test for COVID-19 Classification (preprint)

With the massive damage in the world caused by Coronavirus Disease 2019 SARS-CoV-2 (COVID-19), many related research topics have been proposed in the past two years. The Chest Computed Tomography (CT) scans are the most valuable materials to diagnose the COVID-19 symptoms. However, most schemes for COVID-19 classification of Chest CT scan is based on a single-slice level, implying that the most critical CT slice should be selected from the original CT scan volume manually. We simultaneously propose 2-D and 3-D models to predict the COVID-19 of CT scan to tickle this issue. In our 2-D model, we introduce the Deep Wilcoxon signed-rank test (DWCC) to determine the importance of each slice of a CT scan to overcome the issue mentioned previously. Furthermore, a Convolutional CT scan-Aware Transformer (CCAT) is proposed to discover the context of the slices fully. The frame-level feature is extracted from each CT slice based on any backbone network and followed by feeding the features to our within-slice-Transformer (WST) to discover the context information in the pixel dimension. The proposed Between-Slice-Transformer (BST) is used to aggregate the extracted spatial-context features of every CT slice. A simple classifier is then used to judge whether the Spatio-temporal features are COVID-19 or non-COVID-19. The extensive experiments demonstrated that the proposed CCAT and DWCC significantly outperform the state-of-the-art methods.