In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Interferon lambda 4 (IFN4) has been recently known and studied for its role in hepatitis C virus (HCV) infection, but its clinical potential is significantly hampered due to its poor expression in vitro. Our study reports the successful production of IFN4 from a mammalian cell line through a glycoengineering and structure-based approach. We introduced de novo N-glycosylation of IFN4, guided by structural analysis, and produced IFN4 variants in Expi293F that displayed improved expression and potency. To preserve the structure and functionality of IFN4, the model structure of the IFN4 signaling complex was analyzed and the N-glycosylation candidate sites were selected. The receptor binding activity of engineered IFN4 variants and their receptormediated signaling pathway were similar to the E. coli version of IFN4 (eIFN4), while the antiviral activity and induction levels of interferon-stimulated gene (ISG) were all more robust in our variants. Our engineered IFN4 variants may be further developed for clinical applications and utilized in basic research to decipher the immunological roles of IFN4. Previously, the transient expression of wild-type IFN4 in mammalian cells failed to produce significant amounts of recombinant
Structure-based glycoengineering of interferon lambda 4 enhances its productivity and anti-viral potency

Bacterial and viral infections occur early and recurrently in life and thereby impose a substantial disease burden. Besides causing clinical symptoms, a potential role of infection in the development of the asthma syndrome later in life has also been suggested. However, whether bacterial and viral infections unmask host factors in children at risk of asthma or whether they directly cause asthma remains unclear; both viewpoints could be justifi ed, but the underlying mechanisms are complex and poorly understood. Recently, the role of the bacterial microbiome has been emphasised. But data are still sparse and future studies are needed for defi nitive conclusions to be made. In this Review, we discuss present knowledge of viruses and bacteria that infect and colonise the respiratory tract and mucosal surfaces, including their timepoint of action, host factors related to infection, and their eff ect on childhood asthma. Childhood asthma could be the result of a combination of altered host susceptibility and infectious agents.
Prenatal and childhood infections: implications for the development and treatment of childhood asthma

Background: To control the spread of Corona Virus Disease , 30 screening large numbers of suspected cases for appropriate quarantine and 31 treatment is a priority. Pathogenic laboratory testing is the diagnostic gold standard 32 but it is time consuming with significant false negative results. Fast and accurate 33 diagnostic methods are urgently needed to combat the disease. Based on 34 COVID-19 radiographical changes in CT images, we aimed to develop a deep 35 learning method that could extract COVID-19's graphical features in order to provide 36 a clinical diagnosis ahead of the pathogenic test, thus saving critical time for 37 disease control. 38 Methods and Findings: We collected 1,065CT images of pathogen-confirmed 39 COVID-19 cases (325 images) along with those previously diagnosed with typical 40 viral pneumonia (740 images). We modified the Inception transfer-learning model 41 to establish the algorithm, followed by internal and external validation. The internal 42 validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity 43 of 0.87. The external testing dataset showed a total accuracy of 79.3% with 44 specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that 45 first two nucleic acid test results were negative, 46 were predicted as COVID-19 46 positive by the algorithm, with the accuracy of 85.2%. 47 Conclusion: These results demonstrate the proof-of-principle for using 48 artificial intelligence to extract radiological features for timely and accurate 49 COVID-19 diagnosis. 50
Breast Cancer Prevention and Therapy, Ministry of Education