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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.
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Hepatitis C virus (HCV) infects more than 2 % of the world population with highest prevalence in parts of Africa and Asia. Past standard of care using interferon and ribavirin had adverse effects and showed modest efficacy for some HCV genotypes spurring the development of direct acting antivirals (DAAs). Such DAAs target viral proteins and are thus better tolerated but they suffer from emergence of vial resistance. Furthermore, DAAs are often HCV genotype specific. Novel drug candidates targeting host factors required for HCV propagation, so called host-targeting antivirals (HTAs), promise to overcome both caveats. The genetic barrier to resistance is usually considered to be high for HTAs and all HCV genotypes presumably use the same host factors. Recent data, however, challenge these assumptions, at least for some HTAs. Here, we highlight the most important hosttargeting strategies against hepatitis C and critically discuss their opportunities and risks.
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Opportunities and Risks of Host-targeting Antiviral Strategies for Hepatitis C

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were reviewed. Results: Three cases of recrudescence were suspected to have been caused by sexual contact with survivors, one to be due to international migration, and one was linked to a potentially immunocompromised mother. The three sexual transmission events involving survivors-the first two in Liberia and one in Sierra Leone-required 164 days, >150 days, and approximately 180 days, respectively, from discharge of the survivors to confirmation of the recrudescent case. The events of recrudescence were associated with relatively uncommon routes of transmission other than close contact during burial or care-giving, including sexual transmission, possible immunocompromise, and migration. Recognition of the sexual transmission risk among survivors could potentially involve discrimination, which may lead to under-ascertainment.
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Recrudescence of Ebola virus disease outbreak in West Africa, 2014- 2016

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Background: Viral infectious diseases are the serious threat for human health. The receptor-binding is the first step for the viral infection of hosts. To more effectively treat human viral infectious diseases, the hidden virus-receptor interactions must be discovered. However, current computational methods for predicting virus-receptor interactions are limited. Result: In this study, we propose a new computational method (IILLS) to predict virus-receptor interactions based on Initial Interaction scores method via the neighbors and the Laplacian regularized Least Square algorithm. IILLS integrates the known virus-receptor interactions and amino acid sequences of receptors. The similarity of viruses is calculated by the Gaussian Interaction Profile (GIP) kernel. On the other hand, we also compute the receptor GIP similarity and the receptor sequence similarity. Then the sequence similarity is used as the final similarity of receptors according to the prediction results. The 10-fold cross validation (10CV) and leave one out cross validation (LOOCV) are used to assess the prediction performance of our method. We also compare our method with other three competing methods (BRWH, LapRLS, CMF). The experiment results show that IILLS achieves the AUC values of 0.8675 and 0.9061 with the 10-fold cross validation and leave-one-out cross validation (LOOCV), respectively, which illustrates that IILLS is superior to the competing methods. In addition, the case studies also further indicate that the IILLS method is effective for the virus-receptor interaction prediction.
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IILLS: predicting virus-receptor interactions based on similarity and semi-supervised learning