ABSTRACT
Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2. © 2022 Institute of Biophysics,Chinese Academy of Sciences. All rights reserved.
ABSTRACT
Objective To detect the active ingredients in the traditional Chinese medicine prescription and its molecular mechanisms against SARS-CoV-2 by prescription mining and molecular dynamics simulations. Methods Herein, prescription mining and virtual screening of drugs were performed to screen the potential inhibitors against SARS-CoV-2. Molecular docking and molecular dynamics (MDs) simulations were further performed to explore the molecular recognition and inhibition mechanism between the potential inhibitors and SARS-CoV-2. Results The natural compounds library was constructed by 143 prescriptions of traditional Chinese medicine, which contained 640 natural compounds. Ten compounds were screened out from the natural compounds library. Among the 10 compounds, 23-trans-p-coumaryhormentic acid, the main active constituent of the Loquat leaf, showed the best binding affinity targeting the recognizing interface of SARS-CoV-2 S protein/ACE2. Upon binding 23-trans-p-coumaryhormentic acid, the key interactions between SARS-CoV-2 S protein and ACE2 were almost interrupted. Conclusion Ten compounds targeting SARS-CoV-2 S protein/ACE2 interface were screened out from natural compound library. And we inferred that 23-trans-p-coumaryhormentic acid is a potential inhibitor against SARS-CoV-2, which would contribute to the development of the antiviral drug for SARS-CoV-2.
ABSTRACT
Although the engineering education has been widely developed in China, the training for engineers cannot completely satisfy the requirements from the society. The output-based education (OBE) designs education process according to the results of education so that students can have a certain level of ability when they graduate. However, influenced by the COVID-19 pandemic, plenty of offline practical activities have changed to online ones. Thus, the digital image processing course is taken as an example, and the online class video is taken as the test data for students to do practical experiments. Moreover, an algorithm extracting harr-like features to detect face and recognize expression is applied. The eyes are further extracted from the detected face images to calculate the ratio of width and height, and then the students’ studying state can be determined. The experimental results demonstrate that the algorithm can help teachers understand the students’ state and improve the teaching efficiency. Moreover, the OBE online teaching mode can improve the students’ practical ability. © 2021, Springer Nature Switzerland AG.
ABSTRACT
An outbreak of COVID-19 in Wuhan, China, at the end of 19 was followed by a national and global epidemic. On March 11, 2020, the World Health Organization declared COVID-19 a global pandemic. The COVID-19 virus is transmitted from person to person by droplets or direct contact, and there is no possibility of symptomatic transmission. It is a great threat to human life and health and affects the development of the global economy. It is at the background, a system is needed to predict COVID-19, so a clinical decision-making system based on deep learning for COVID-19 was proposed. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
A novel intelligent navigation technique for accurate image-guided COVID-19 lung biopsy is addressed, which systematically combines AR, customized haptic-enabled surgical tools and deep neural network to achieve customized surgical navigation. Clinic data from 341 COVID-19 positive patients, with 1598 negative control group has collected for the model synergy and evaluation. Biomechanics force data from the experiment is applied a WPD-CNN-LSTM to learn a new patient-specific COVID-19 surgical model, and the ResNet was employed for the intraoperative force classification. To boost the user immersion and promote the user experience, intro-operational guiding images have combined with the Haptic-AR navigational view. Furthermore, a 3DUI, including all requisite surgical details, was developed with a real-time response guaranteed. 24 thoracic surgeons were invited to the objective and subjective experiments for performance evaluation. The RMSE results of our proposed WCL model is 0.0128, and the classification accuracy is 97%, which demonstrated that the innovative AR with DL intelligent model outperforms the existing perception navigation techniques with significantly higher performance. This study shows a novel framework in the interventional surgical integration for COVID-19, and opens the new research about the integration of AR, haptic rendering, and deep learning for the surgical navigation. IEEE