Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System (preprint)

The coronavirus disease 2019 (COVID-19) continues to have a negative impact on healthcare systems around the world, though the vaccines have been developed and national vaccination coverage rate is steadily increasing. At the current stage, automatically segmenting the lung infection area from CT images is essential for the diagnosis and treatment of COVID-19. Thanks to the development of deep learning technology, some deep learning solutions for lung infection segmentation have been proposed. However, due to the scattered distribution, complex background interference and blurred boundaries, the accuracy and completeness of the existing models are still unsatisfactory. To this end, we propose a boundary guided semantic learning network (BSNet) in this paper. On the one hand, the dual-branch semantic enhancement module that combines the top-level semantic preservation and progressive semantic integration is designed to model the complementary relationship between different high-level features, thereby promoting the generation of more complete segmentation results. On the other hand, the mirror-symmetric boundary guidance module is proposed to accurately detect the boundaries of the lesion regions in a mirror-symmetric way. Experiments on the publicly available dataset demonstrate that our BSNet outperforms the existing state-of-the-art competitors and achieves a real-time inference speed of 44 FPS.

Cultivating resilience during the COVID-19 pandemic: A socioecological perspective

The coronavirus disease 2019 (COVID-19) pandemic poses wide-ranging impacts on the physical and mental health of people around the world, increasing attention from both researchers and practitioners on the topic of resilience. In this article, we review previous research on resilience from the past several decades, focusing on how to cultivate resilience during emerging situations such as the COVID-19 pandemic at the individual, organizational, community, and national levels from a socioecological perspective. Although previous research has greatly enriched our understanding of the conceptualization, predicting factors, processes, and consequences of resilience from a variety of disciplines and levels, future research is needed to gain a deeper and comprehensive understanding of resilience, including developing an integrative and interdisciplinary framework for cultivating resilience, developing an understanding of resilience from a life span perspective, and developing scalable and cost-effective interventions for enhancing resilience and improving pandemic preparedness. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Where translation impacts: The non-professional community on Chinese online social media – A descriptive case study on the user-generated translation activity of Bilibili content creators

This study explored the user-generated translation activity in the context of the Chinese online social media. It focused on Bilibili content creators dedicated to translating public comments on China-related videos posted on international social media platforms such as YouTube, and creating videos featuring Chinese translated comments. Viewing their translation actions as events in a collective activity system, the authors collected data from 30 participants through a questionnaire and follow-up interviews with two participants who have recently worked on videos about China?s fight against Covid-19. All the data were analysed by using Engeström?s activity theory model to create an activity model showing how their user-generated translation activity was conducted. In this study, we observed that the participants, as the non-professional translation community on the Chinese online social media, were breaking down linguistic borders for fans and viewers, and postulated the possible interaction between the user-generated translation work and their better understanding of how the world saw China through grassroots expression of opinions.

Network Pharmacology Integrated Molecular Docking Reveals the Anti-COVID-19 Mechanism of Xingnaojing Injection

In the process of fighting against COVID-19 in China, Xingnaojing injection has been recommended for its clinical treatment, but the information about its active components and mechanism is still lacking. Therefore, in this work, using network pharmacology and molecular docking, we studied the active components of Xingnaojing injection having anti-COVID-19 properties. Using the DL parameter, TCMSP and CNKI databases were used to screen the active components of the Xingnaojing injection. Then, the SwissTargetPrediction webserver was used to collect the corresponding gene targets, and the gene targets related to COVID-19 were searched in the Genecards database. The DAVID database was used to enrich the function of gene targets, and the KOBAS3.0 database for the annotation of related KEGG pathways. The ?components?targets?pathways? network of Xingnaojing injection was constructed with Cytoscape 3.6.1 software. The protein?protein interaction networks were analyzed using the String database. Specific proteins, SARS-COV-2 3 Cl, ACE2, and the active components were imported into Discovery Studio 2016 Client for molecular docking studies. From the Xingnaojing injection, a total of 58 active components, including Divanillalaceton and Q27139023, were screened. These were linked to 53 gene targets including mitogen-activated protein kinase 1 (MAPK1), tumor necrosis factorTNF, epidermal growth factor receptor, MAPK3, and 196 signaling pathways related to COVID-19, such as apoptosis, C-type lectin receptor signaling pathway, and hypoxia-inducible factor 1 signaling pathway. Furthermore, molecular docking studies were performed to study potential binding between the key targets and selected active components. Xingnaojing injection exhibits anti-COVID-19 effects via multiple components, multiple targets, and multiple pathways. These results set a scientific basis for further elucidation of the anti-COVID-19 mechanism of Xingnaojing injection.

Perioperative Mortality and Complications in Patients with COVID-19 Undergoing Surgery: A Meta-Regression and Meta-Analysis of 8 Cohort Studies (preprint)

Objectives: The novel coronavirus pneumonia (COVID-19)，spread rapidly world wide, was first reported in December 2019. Meanwhile, there are still a large number of patients who need to undergo various surgical treatments. However, the consensus on whether patients with COVID-19 receive emergency or elective surgery will influence their perioperative mortality and complications still cannot be reached. Therefore, we used meta-analysis to explore the impact of patients with COVID-19 perioperative mortality and complications, aiming to provide evidence for clinical decision-making.Methods: We searched PubMed, Embase, Web of Science, Wan Fang database, date from December 2019 to July 2020 for collecting clinical trail on the impact of patients with COVID-19 perioperative mortality and complications. According to the Cochrane system evaluation method, the data is meta-analyzed with RevMan5.3 software.Results: Eight studies involving 2037 patients, 261 (12.81%) patients with COVID-19 and 1776(87.19%) without COVID-19, were included. The results of meta-analysis showed: the COVID-19 group vs Non-COVID-19 group , perioperative mortality and postoperative pneumonia syndrome increased in COVID-19 group（OR:3.84，95%CI：2.10-7.02，I2 =46%， P <0.0001）, (OR: 33.42，95%CI：15.49-72.07，I 2 =0%， P <0.00001), The number of postoperative fever were significantly higher in COVID-19 , There were no significant difference in postoperative complications and ICU admission between the two groups.Conclusions: In our study, The risk of perioperative death and postoperative pulmonary is significantly increased in patients with COVID-19. These data suggested that consideration should be taken for postponing non-critical procedures and promoting nonoperative treatment to delay or avoid the need for surgery during the pandemic of COVID-19.Funding Statement: Natural Science Foundation of China, Grant number: 31760327/ 81760191Declaration of Interests: The authors declare no competing interests.

Evaluation of α-Interferon Applied in COVID-19 Therapy Regimen

OBJECTIVE: To use IFNα injection as inhalant for there is no proper dosage form for aerosol inhalation and evaluate the feasibility of IFN-α off-label use. METHODS: The clinical studies in SARS and MERS were reviewed to discuss the efficacy and safety of IFN-α application in COVID-19. Meanwhile, the features of aerosol inhalation, characteristics of IFN-α and related clinical researches were analyzed to argue the possibility of IFN-α aerosol inhalation. RESULTS: IFN-α seems to be effective in relieving early symptoms but likely invalid in reducing mortality of severe patients, however, the exact therapeutic effect calls for further clinical tests. Proper atomization of IFN-α injection won't reduce biological activity of the protein, but absorption and utilization of IFN-α in lung may be unsatisfactory for the lack of sorbefacient. More than that, ingredients in IFN-α injection may increase risk of adverse reaction. CONCLUSION: The aerosol inhalation of IFN-α injection should be fully evaluated on the side of patients. If there is a lack of proper atomization device or operation staff, subcutaneous injection of IFN-α may be a tentative administration.

Cohort Profile: Chinese Color Nest Project (preprint)

The ongoing CCNP was established to plot normative growth curves for brain structure and function across the human lifespan, and link age-related changes in brain imaging measures with psycho-behavioral functions at the behavioral, cognitive and emotional levels using an accelerated longitudinal design. It comprises three phases: developmental CCNP (devCCNP: 6-20 years), standardizing CCNP (stdCCNP: 20-60 years) and aging CCNP (ageCCNP: 60-90 years). The devCCNP started in 2013 and has successfully acquired CCNP-SWU data with three repeated measurements ( the trial stage of devCCNP), and accumulated CCNP-CAS baseline data (the second stage of devCCNP). CCNP-SWU consists of 201 age-sex stratified schoolchildren at enrollment (100 children followed up for 2.5 years at 1.25-year intervals) while CCNP-CAS has been recruiting participants since July 2018, and has collected data from 133 eligible children so far. A T1-weighted MRI and two resting functional MRI scans were acquired across three waves in CCNP-SWU with the same imaging protocols on a Siemens Trio 3T scanner at Southwest University in Chongqing, China. CCNP-SWU obtained longitudinal biophysical, social, behavioral and cognitive data via parent-reported questionnaires, self-reported questionnaires, behavioral assessment, as well as E-Prime computer tasks. Data were collected on the impact of COVID-19 on children’s learning and daily life from 46 children between March and May 2020. Children’s emotional states during COVID-19 pandemic were also measured. Data are accessed by researchers and collaborators of CCNP upon agreement with the principal investigator.

Diagnostic classification of coronavirus disease 2019 (COVID-19) and other pneumonias using radiomics features in CT chest images (preprint)

We propose a classification method using the radiomics features of CT chest images to identify patients with coronavirus disease 2019 (COVID-19) and other pneumonias. The chest CT images of two groups of participants (90 COVID-19 patients and 90 other pneumonias patients) were collected, and the two groups of data were manually drawn to outline the region of interest (ROI) of pneumonias. The radiomics method was used to extract textural features and histogram features of the ROI and obtain a radiomics features vector from each sample. Finally, using the radiomics features as an input, a support vector machine (SVM) model was constructed to classify patients with COVID-19 and patients with other pneumonias. This model used 20 rounds of 10-fold cross-validation for training and testing. In the COVID-19 patients, correlation analysis (multiple comparison correction—Bonferroni correction, p<0.05/7) was also conducted to determine whether the textural and histogram features were correlated with the laboratory test index of blood, i.e., blood oxygen, white blood cell, lymphocytes, neutrophils, C-reactive protein, hypersensitive C-reactive protein, and erythrocyte sedimentation rate. The results showed that the proposed method had a classification accuracy as high as 88.33%, sensitivity of 83.56%, specificity of 93.11%, and an area under the curve of 0.947. This proved that the radiomics features were highly distinguishable, and this SVM model can effectively identify and diagnose patients with COVID-19 and other pneumonias. The correlation analysis results showed that some texture features were positively correlated with WBC, NE, and CRP and also negatively related to SPO2H and NE.