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: Influenza infection is a major cause of morbidity and mortality worldwide, especially during pandemics outbreaks. Emerging data indicate that phase II antioxidant enzyme pathways could play a role in virusassociated inflammation and immune clearance. While Nrf2-dependent gene expression is known to modify inflammation, a mechanistic role in viral susceptibility and clearance has yet to be elucidated. Therefore, we utilized differentiated human nasal epithelial cells (NEC) and an enzymatic virus-like particle entry assay, to examine the role Nrf2-dependent gene expression has on viral entry and replication. Herein, lentiviral vectors that express Nrf2-specific short hairpin (sh)-RNA effectively decreased both Nrf2 mRNA and Nrf2 protein expression in transduced human NEC from healthy volunteers. Nrf2 knockdown correlated with a significant increase in influenza virus entry and replication. Conversely, supplementation with the potent Nrf2 activators sulforaphane (SFN) and epigallocatechin gallate (EGCG) significantly decreased viral entry and replication. The suppressive effects of EGCG on viral replication were abolished in cells with knocked-down Nrf2 expression, suggesting a causal relationship between the EGCG-induced activation of Nrf2 and the ability to protect against viral infection. Interestingly, the induction of Nrf2 via nutritional supplements SFN and EGCG increased antiviral mediators/responses: RIG-I, IFN-β, and MxA at baseline in the absence of infection. Our data indicate that there is an inverse relationship between the levels of Nrf2 expression and the viral entry/ replication. We also demonstrate that supplementation with Nrf2-activating antioxidants inhibits viral replication in human NEC, which may prove to be an attractive therapeutic intervention. Taken together, these data indicate potential mechanisms by which Nrf2-dependent gene expression regulates susceptibility to influenza in human epithelial cells.
Example Output: Nrf2 expression modifies influenza A entry and replication in nasal epithelial cells ☆

Example Input: Background: Nonadditivity in fitness effects from two or more mutations, termed epistasis, can result in compensation of deleterious mutations or negation of beneficial mutations. Recent evidence shows the importance of epistasis in individual evolutionary pathways. However, an unresolved question in molecular evolution is how often and how significantly fitness effects change in alternative genetic backgrounds. Results: To answer this question, we quantified the effects of all single mutations and double mutations between all positions in the IgG-binding domain of protein G (GB1). By observing the first two steps of all possible evolutionary pathways using this fitness profile, we were able to characterize the extent and magnitude of pairwise epistasis throughout an entire protein molecule. Furthermore, we developed a novel approach to quantitatively determine the effects of single mutations on structural stability (DDG U ). This enabled determination of the importance of stability effects in functional epistasis. Conclusions: Our results illustrate common biophysical mechanisms for occurrences of positive and negative epistasis. Our results show pervasive positive epistasis within a conformationally dynamic network of residues. The stability analysis shows that significant negative epistasis, which is more common than positive epistasis, mostly occurs between combinations of destabilizing mutations. Furthermore, we show that although significant positive epistasis is rare, many deleterious mutations are beneficial in at least one alternative mutational background. The distribution of conditionally beneficial mutations throughout the domain demonstrates that the functional portion of sequence space can be significantly expanded by epistasis.
Example Output: Article A Comprehensive Biophysical Description of Pairwise Epistasis throughout an Entire Protein Domain

Example Input: Background: In April 2009, the pandemic influenza A(H1N1) virus emerged and spread globally. The objective of this study was to describe the independent risk factors for hospital mortality and the treatment effect of corticosteroids among patients with 2009 influenza A(H1N1) infection. Methods: We retrospectively obtained clinical data of 155 adult patients with confirmed infection of 2009 influenza A(H1N1) in 23 hospitals in Beijing, China from October 1 to December 23, 2009. Risk factors for hospital mortality were identified with multivariate logistic regression analysis. Results: Among the 155 patients, 90 (58.1%) were male, and mean age was 43.0 ± 18.6 years, and comorbidities were present in 81 (52.3%) patients. The most common organ dysfunctions included acute respiratory failure, altered mental status, septic shock, and acute renal failure. Oseltamivir was initiated in 125 patients (80.6%), only 16 patients received antiviral therapy within 48 hours after symptom onset. Fifty-two patients (33.5%) were treated with systemic corticosteroids, with a median daily dose of 80 mg. Twenty-seven patients (17.4%) died during hospital stay. Diabetes [odds ratio (OR) 8.830, 95% confidence interval [CI] 2.041 to 38.201, p = 0.004) and lactate dehydrogenase (LDH) level (OR 1.240, 95% CI 1.025 to 1.500, p = 0.027) were independent risk factors of hospital death, as were septic shock and altered mental status. Corticosteroids use was associated with a trend toward higher hospital mortality (OR 3.668, 95% CI 0.987 to 13.640, p = 0.052). Conclusions: Hospitalized patients with 2009 H1N1 influenza had relative poor outcome. The risk factors at hospitalization may help clinicians to identify the high-risk patients. In addition, corticosteroids use should not be regarded as routine pharmacologic therapy.
Example Output:
Hospitalized adult patients with 2009 influenza A (H1N1) in Beijing, China: risk factors for hospital mortality