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.

Example Input: The emergence of the severe acute respiratory syndrome coronavirus (SARS-CoV) has led to a renewed interest in studying the role of accessory proteins in regulating coronavirus infections in the natural host. A significant body of evidence has accumulated in the area of SARS-CoV and host interactions that indicate that the accessory proteins might play an important role in modulating the host response to virus infection and thereby, contribute to pathogenesis. In this review, we have compiled the current knowledge about SARS-CoV accessory proteins, obtained from studies in cell culture systems, reverse genetics and animal models, to shed some light into the possible role of these proteins in the propagation and virulence of SARS-CoV in its natural host. We conclude by providing some questions for future studies that will greatly advance our knowledge about the biological significance and contributions of the accessory proteins in the development of SARS in humans.
Example Output: SARS coronavirus accessory proteins

Example Input: Objective: The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. Methods: A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. Results: 76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR = 5.20, 95% CI 1.41-12.52, p = 0.010; 5 points), lymphocyte ≤ 0.8 * 10 9 /L (OR = 4.53, 95% CI 2.55-8.05, p < 0.001; 4 points), bacterial coinfection (OR = 3.71, 95% CI 2.11-6.51, p < 0.001; 4 points), acute-smoker (OR = 3.19, 95% CI 1.34-6.26, p = 0.001; 3 points), quit-smoker (OR = 2.18, 95% CI 0.99-4.82, p = 0.054; 2 points), hypertension (OR = 2.39, 95% CI 1.55-4.26, p = 0.003; 2 points) and age ≥60 years (OR = 2.14, 95% CI 1.04-4.39, p = 0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs. 0.717, p < 0.001). Conclusion: Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.
Example Output: Citation: Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score

Example Input: We need to apply lessons learned from previous influenza pandemics to continuously update preparedness and response plans. It has become evident that strengthening networks of international referral laboratories coupled with scaling-up efforts to expand epidemiological surveillance networks is critical for responding and mitigating the impact of influenza pandemics. The current swine-related influenza A (H1N1) pandemic has also shown that international collaboration remains a critical component to effectively respond to influenza pandemics in the current globalized world. Ó 2009 IMSS. Published by Elsevier Inc.
Example Output:
What Have We Learned from the Novel Influenza A (H1N1) Pandemic in 2009 for Strengthening Pandemic Influenza Preparedness?