Permethrin as a Potential Furin Inhibitor through a Novel Non-Competitive Allosteric Inhibition.

Furin is a potential target protein associated with numerous diseases; especially closely related to tumors and multiple viral infections including SARS-CoV-2. Most of the existing efficient furin inhibitors adopt a substrate analogous structure, and other types of small molecule inhibitors need to be discovered urgently. In this study, a high-throughput screening combining virtual and physical screening of natural product libraries was performed, coupled with experimental validation and preliminary mechanistic assays at the molecular level, cellular level, and molecular simulation. A novel furin inhibitor, permethrin, which is a derivative from pyrethrin I generated by Pyrethrum cinerariifolium Trev. was identified, and this study confirmed that it binds to a novel allosteric pocket of furin through non-competitive inhibition. It exhibits a very favorable protease-selective inhibition and good cellular activity and specificity. In summary, permethrin shows a new parent nucleus with a new mode of inhibition. It could be used as a highly promising lead compound against furin for targeting related tumors and various resistant viral infections, including SARS-CoV-2.

Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review.

Since 2020, novel coronavirus pneumonia has been spreading rapidly around the world, bringing tremendous pressure on medical diagnosis and treatment for hospitals. Medical imaging methods, such as computed tomography (CT), play a crucial role in diagnosing and treating COVID-19. A large number of CT images (with large volume) are produced during the CT-based medical diagnosis. In such a situation, the diagnostic judgement by human eyes on the thousands of CT images is inefficient and time-consuming. Recently, in order to improve diagnostic efficiency, the machine learning technology is being widely used in computer-aided diagnosis and treatment systems (i.e., CT Imaging) to help doctors perform accurate analysis and provide them with effective diagnostic decision support. In this paper, we comprehensively review these frequently used machine learning methods applied in the CT Imaging Diagnosis for the COVID-19, discuss the machine learning-based applications from the various kinds of aspects including the image acquisition and pre-processing, image segmentation, quantitative analysis and diagnosis, and disease follow-up and prognosis. Moreover, we also discuss the limitations of the up-to-date machine learning technology in the context of CT imaging computer-aided diagnosis.

FAM: focal attention module for lesion segmentation of COVID-19 CT images.

The novel coronavirus pneumonia (COVID-19) is the world's most serious public health crisis, posing a serious threat to public health. In clinical practice, automatic segmentation of the lesion from computed tomography (CT) images using deep learning methods provides an promising tool for identifying and diagnosing COVID-19. To improve the accuracy of image segmentation, an attention mechanism is adopted to highlight important features. However, existing attention methods are of weak performance or negative impact to the accuracy of convolutional neural networks (CNNs) due to various reasons (e.g. low contrast of the boundary between the lesion and the surrounding, the image noise). To address this issue, we propose a novel focal attention module (FAM) for lesion segmentation of CT images. FAM contains a channel attention module and a spatial attention module. In the spatial attention module, it first generates rough spatial attention, a shape prior of the lesion region obtained from the CT image using median filtering and distance transformation. The rough spatial attention is then input into two 7 × 7 convolution layers for correction, achieving refined spatial attention on the lesion region. FAM is individually integrated with six state-of-the-art segmentation networks (e.g. UNet, DeepLabV3+, etc.), and then we validated these six combinations on the public dataset including COVID-19 CT images. The results show that FAM improve the Dice Similarity Coefficient (DSC) of CNNs by 2%, and reduced the number of false negatives (FN) and false positives (FP) up to 17.6%, which are significantly higher than that using other attention modules such as CBAM and SENet. Furthermore, FAM significantly improve the convergence speed of the model training and achieve better real-time performance. The codes are available at GitHub (https://github.com/RobotvisionLab/FAM.git).

Potential inhibitors for blocking the interaction of the coronavirus SARS-CoV-2 spike protein and its host cell receptor ACE2.

BACKGROUND: The outbreak of SARS-CoV-2 continues to pose a serious threat to human health and social. The ongoing pandemic of COVID-19 caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has made a serious threat to public health and economic stability worldwide. Given the urgency of the situation, researchers are attempting to repurpose existing drugs for treating COVID-19. METHODS: We first established an anti-coronavirus drug screening platform based on the Homogeneous Time Resolved Fluorescence (HTRF) technology and the interaction between the coronavirus spike protein and its host receptor ACE2. Two compound libraries of 2,864 molecules were screened with this platform. Selected candidate compounds were validated by SARS-CoV-2_S pseudotyped lentivirus and ACE2-overexpressing cell system. Molecular docking was used to analyze the interaction between S protein and compounds. RESULTS: We identified three potential anti-coronavirus compounds: tannic acid (TA), TS-1276 (anthraquinone), and TS-984 (9-Methoxycanthin-6-one). Our in vitro validation experiments indicated that TS-984 strongly inhibits the interaction of the coronavirus S protein and the human cell ACE2 receptor. Additionally, tannic acid showed moderate inhibitory effect on the interaction of S protein and ACE2. CONCLUSION: This platform is a rapid, sensitive, specific, and high throughput system, and available for screening large compound libraries. TS-984 is a potent blocker of the interaction between the S-protein and ACE2, which might have the potential to be developed into an effective anti-coronavirus drug.

Rapid, ultrasensitive and highly specific biosensor for the diagnosis of SARS-CoV-2 in clinical blood samples

In this study, a lateral flow combined IgG-IgM immunochromatographic assay is developed for the rapid, simultaneous detection of IgM and IgG antibodies against SARS-CoV-2 in clinical blood samples within 15 min. The clinical detection sensitivity and specificity of the assay strips is investigated in samples of blood from inpatients with COVID-19. The sensitivity and specificity of this assay are 85.29% and 100.00%, respectively. Compared with a single IgG and IgM test, the combined IgG-IgM immunochromatographic strip test has higher sensitivity. Our results demonstrate that the combined IgG-IgM immunochromatographic strip is suitable for the rapid screening of SARS-CoV-2 infection, among confirmed COVID-19 patients, suspect patients and asymptomatic SARS-CoV-2 carriers.