Prediction of drug-likeness using graph convolutional attention network.

MOTIVATION: The drug-likeness has been widely used as a criterion to distinguish drug-like molecules from non-drugs. Developing reliable computational methods to predict the drug-likeness of compounds is crucial to triage unpromising molecules and accelerate the drug discovery process. RESULTS: In this study, a deep learning method was developed to predict the drug-likeness based on the graph convolutional attention network (D-GCAN) directly from molecular structures. Results showed that the D-GCAN model outperformed other state-of-the-art models for drug-likeness prediction. The combination of graph convolution and attention mechanism made an important contribution to the performance of the model. Specifically, the application of the attention mechanism improved accuracy by 4.0%. The utilization of graph convolution improved the accuracy by 6.1%. Results on the dataset beyond Lipinski's rule of five space and the non-US dataset showed that the model had good versatility. Then, the billion-scale GDB-13 database was used as a case study to screen SARS-CoV-2 3C-like protease inhibitors. Sixty-five drug candidates were screened out, most substructures of which are similar to these of existing oral drugs. Candidates screened from S-GDB13 have higher similarity to existing drugs and better molecular docking performance than those from the rest of GDB-13. The screening speed on S-GDB13 is significantly faster than screening directly on GDB-13. In general, D-GCAN is a promising tool to predict the drug-likeness for selecting potential candidates and accelerating drug discovery by excluding unpromising candidates and avoiding unnecessary biological and clinical testing. AVAILABILITY AND IMPLEMENTATION: The source code, model and tutorials are available at https://github.com/JinYSun/D-GCAN. The S-GDB13 database is available at https://doi.org/10.5281/zenodo.7054367. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Relationship Between Symptoms of Anxiety and Somatic Symptoms in Health Professionals During the Coronavirus Disease 2019 Pandemic.

BACKGROUND: The pandemic of coronavirus disease 2019 (COVID-19) has posed a threat to global health. Increasing studies have shown that the mental health status of health professionals is very poor during the COVID-19 epidemic. At present, the relationship between somatic symptoms and symptoms of anxiety of health professionals during the COVID-19 has not been reported. The purpose of this study was to explore the frequency of somatic symptoms and its related factors in health professionals with symptoms of anxiety during COVID-19 in China. METHODS: A total of 606 health professionals were assessed online with the Chinese version of the 7-item Generalized Anxiety Disorder (GAD-7) scale, 7-item Insomnia Severity Index (ISI) and the somatization subscale of Symptom Checklist 90 (SCL-90). RESULTS: The percentage of symptoms of anxiety, somatic symptoms and insomnia in all health professionals was 45.4%, 12.0%, and 32%, respectively. The frequency of somatic symptoms in health professionals with symptoms of anxiety was 22.9%. The SCL-90 somatization subscale score was significantly positively correlated with history of somatic diseases, GAD-7 score and ISI score in participants with symptoms of anxiety. CONCLUSION: During the COVID-19, symptoms of anxiety, insomnia, and somatic symptoms are commonly observed in health professionals. Insomnia and symptoms of anxiety are independently associated with somatic symptoms of health professionals with symptoms of anxiety.

Advances in Research on Antiviral Activities of Sulfated Polysaccharides from Seaweeds.

In recent years, various viral diseases have suddenly erupted, resulting in widespread infection and death. A variety of biological activities from marine natural products have gradually attracted the attention of people. Seaweeds have a wide range of sources, huge output, and high economic benefits. This is very promising in the pharmaceutical industry. In particular, sulfated polysaccharides derived from seaweeds, considered a potential source of bioactive compounds for drug development, have shown antiviral activity against a broad spectrum of viruses, mainly including common DNA viruses and RNA viruses. In addition, sulfated polysaccharides can also improve the body's immunity. This review focuses on recent advances in antiviral research on the sulfated polysaccharides from seaweeds, including carrageenan, galactan, fucoidan, alginate, ulvan, p-KG03, naviculan, and calcium spirulan. We hope that this review will provide new ideas for the development of COVID-19 therapeutics and vaccines.

Outcomes of extracorporeal membrane oxygenation in influenza versus COVID-19 during the first wave of COVID-19.

PURPOSE: Extracorporeal membrane oxygenation (ECMO) is a refractory treatment for acute respiratory distress syndrome (ARDS) due to influenza and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, also referred to as coronavirus disease 2019 [COVID-19]). We conducted this study to compare the outcomes of influenza patients treated with veno-venous-ECMO (VV-ECMO) to COVID-19 patients treated with VV-ECMO, during the first wave of COVID-19. METHODS: Patients in our institution with ARDS due to COVID-19 or influenza who were placed on ECMO between August 1, 2010 and September 15, 2020 were included in this comparative, retrospective study. To improve homogeneity, only VV-ECMO patients were analyzed. The clinical characteristics and outcomes were extracted and analyzed. RESULTS: A total of 28 COVID-19 patients and 17 influenza patients were identified and included. ECMO survival rates were 68% (19/28) in COVID-19 patients and 94% (16/17) in influenza patients (p = .04). Thirty days survival rates after ECMO decannulation were 54% (15/28) in COVID-19 patients and 76% (13/17) in influenza patients (p = .13). COVID-19 patients spent a longer time on ECMO compared to flu patients (21 vs. 12 days; p = .025), and more COVID-19 patients (26/28 vs. 2/17) were on immunomodulatory therapy before ECMO initiation (p < .001). COVID-19 patients had higher rates of new infections during ECMO (50% vs. 18%; p = .03) and bacterial pneumonia (36% vs. 6%; p = .024). CONCLUSIONS: COVID-19 patients who were treated in our institution with VV-ECMO had statistically lower ECMO survival rates than influenza patients. It is possible that COVID-19 immunomodulation therapies may increase the risk of other superimposed infections.

Teaching explorations in medical immunology online education during COVID-19 pandemic

As a compulsory course for medical undergraduates, "Medical Immunology" is regarded with strong systematic and theoretical features. Under the circumstances of "classes suspended but learning continues" during COVID-19 pandemic, the teaching team of "Medical Immunology" has organized a team integrated with teachers of basic and clinical medicine, based on the similarities and differences of online and offline teaching. We mainly adopted with PPT recording mode, supplemented with MOOC online. Besides, the education of ideological and political elements has been merged to the course teaching and we evaluated the teaching efficiency by questionnaire investigation. This program which treats students as the center, is of great help to the future blended learning mode of medical immunology.

Clinical characteristics and risk factors for in-hospital mortality of lung cancer patients with COVID-19: A multicenter, retrospective, cohort study.

BACKGROUND: Data on clinical, laboratory, and radiographic characteristics and risk factors for in-hospital mortality of lung cancer patients with COVID-19 are scarce. Here, we aimed to characterize the early clinical features of lung cancer patients with COVID-19 and identify risk factors associated with in-hospital mortality. METHODS: All consecutive lung cancer patients with laboratory-confirmed COVID-19 admitted to 12 hospitals in Hubei province, China, from 3 January to 6 May 2020 were included in the study. Patients without definite clinical outcomes during the period were excluded. Data on initial clinical, laboratory and radiographic findings were compared between survivors and nonsurvivors. Univariable and multivariable logistic regression analyses were used to explore the risk factors associated with in-hospital mortality. RESULTS: Of the 45 lung cancer patients (median [interquartile range] age, 66 [58-74] years; 68.9% males) included, 34 (75.6%) discharged and 11 (24.4%) died. Fever (73.3%) and cough (53.3%) were the dominant initial symptoms, and respiratory symptoms were common. Lung cancer patients also presented atypical appearances of COVID-19. In the multivariable analysis, prolonged prolongation prothrombin time (PT) (OR = 2.1, 95% CI: 1.00-4.41, P = 0.0497) and elevated high sensitivity cardiac troponin I (hs-TNI) (OR = 7.65, 95% CI: 1.24-47.39, P = 0.0287) were associated with an increased risk of in-hospital mortality. CONCLUSIONS: Lung cancer patients with COVID-19 have high in-hospital mortality. Prolonged PT and elevated hs-TNI are independent risk factors for in-hospital mortality of lung cancer patients with COVID-19. KEY POINTS: SIGNIFICANT FINDINGS OF THE STUDY: Lung cancer patients with COVID-19 have atypical early symptoms and imaging features. The prolonged prothrombin time and elevated high sensitivity cardiac troponin I are independent risk factors for in-hospital mortality of lung cancer patients with COVID-19. WHAT THIS STUDY ADDS: This study characterizes the early clinical features of lung cancer patients with COVID-19 in China, and identifies the risk factors associated with in-hospital mortality of lung cancer patients with COVID-19.