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.

Input: Consider Input: Recent studies suggest an association between the Interferon Inducible Transmembrane 3 (IFITM3) rs12252 variant and the course of influenza infection. However, it is not clear whether the reported association relates to influenza infection severity. The aim of this study was to estimate the hospitalization risk associated with this variant in Influenza Like Illness (ILI) patients during the H1N1 pandemic influenza. A case-control genetic association study was performed, using nasopharyngeal/oropharyngeal swabs collected during the H1N1 pandemic influenza. Laboratory diagnosis of influenza infection was performed by RT-PCR, the IFITM3 rs12252 was genotyped by RFLP and tested for association with hospitalization. Conditional logistic regression was performed to calculate the confounder-adjusted odds ratio of hospitalization associated with IFITM3 rs12252. We selected 312 ILI cases and 624 matched non-hospitalized controls. Within ILI Influenza A(H1N1)pdm09 positive patients, no statistical significant association was found between the variant and the hospitalization risk (Adjusted OR: 0.73 (95%CI: 0.33-1.50)). Regarding ILI Influenza A(H1N1)pdm09 negative patients, CT/CC genotype carriers had a higher risk of being hospitalized than patients with TT genotype (Adjusted OR: 2.54 (95%CI: 1.54-4.19)). The IFITM3 rs12252 variant was associated with respiratory infection hospitalization but not specifically in patients infected with Influenza A(H1N1)pdm09.

Output: Hospitalization Risk Due to Respiratory Illness Associated with Genetic Variation at IFITM3 in Patients with Influenza A(H1N1)pdm09 Infection: A Case-Control Study


Input: Consider Input: Background: Respiratory syncytial virus (RSV), human Rhinovirus (HRV) and human Metapneumo Virus (HMPV) are important viral pathogens causing acute respiratory tract infections in the hospitalized patients. Sensitive and accurate detection of RSV, HRV and HMPV is necessary for clinical diagnosis and treatment. Results: A locked nucleic acid (LNA)-based multiplex closed one-tube nested real-time RT-PCR (mOTNRT-PCR) assay was developed for simultaneous detection of RSV, HRV and HMPV. The sensitivity, specificity, reproducibility and clinical performance of mOTNRT-PCR were evaluated and compared with individual real time PCR (RT-qPCR) assay using clinical samples. The analytical sensitivity of mOTNRT-PCR assay was 5 copies/reaction for RSV, HRV and HMPV, respectively, and no cross-reaction with other common respiratory viruses was observed. The coefficients of variation (CV) of intra-assay and inter-assay were between 0.51 to 3.67%. Of 398 nasopharyngeal aspirates samples tested, 109 (27.39%), 150 (37.69%) and 44 (11.06%) were positive for RSV, HRV and HMPV, respectively, whereas 95 (23.87%), 137 (34.42%) and 38 (9.55%) were positive for RSV, HRV and HMPV, respectively, by individual RT-qPCR assay. Thirty three samples that were positive by mOTNRT-PCR but negative by RT-qPCR were confirmed as true positives by sequencing using reported traditional two-step nested PCR assay. Conclusion: mOTNRT-PCR assay reveals extremely higher sensitivity than that of RT-qPCR assay for detecting RSV, HRV and HMPV in clinical settings.

Output: A multiplex one-tube nested real time RT- PCR assay for simultaneous detection of respiratory syncytial virus, human rhinovirus and human metapneumovirus


Input: Consider Input: The outbreak of the 2019 Novel Coronavirus (SARS-CoV-2) rapidly spread from Wuhan, China to more than 150 countries, areas or territories, causing staggering number of infections and deaths. A systematic profiling of the immune vulnerability landscape of SARS-CoV-2, which can bring critical insights into the immune clearance mechanism, peptide vaccine development, and antiviral antibody development, is lacking. In this study, we investigated the potential of the SARS-CoV-2 viral proteins to induce class I and II MHC presentation and to form linear antibody epitopes. We created an online database to broadly share the predictions as a resource for the research community. Using this resource, we showed that genetic variations in SARS-CoV-2, though still few for the moment, already follow the pattern of mutations in related coronaviruses, and could alter the immune vulnerability landscape of this virus. Importantly, we discovered evidence that SARS-CoV-2, along with related coronaviruses, used mutations to evade attack from the human immune system. Overall, we present an immunological resource for SARS-CoV-2 that could promote both therapeutic development and mechanistic research. : bioRxiv preprint Chinese and European populations. (c) The B cell epitope profiles of the SARS-CoV-2. The Y axis shows the predicted B cell epitope score. Only showing residues with predicted epitope score >0.6. (d) BLASTing the motif binding domain of the SARS-CoV-2 S protein and the SARS-CoV S protein. (e) The T cell and B cell epitope profiles of SARS-CoV. (f) The T cell and B cell epitope profiles of MERS-CoV. Fig. 2 Genetic drifts in SARS-CoV-2 are influenced by host immune pressure. (a-c) The relative mutational rate profiles of the three coronaviruses: (a) SARS-CoV-2, (b) SARS-CoV, and (c) MERS-CoV. The relative mutational rates are calculated by the percentage of nucleotides (nts) from all strains of each virus (n=114 for SARS-CoV-2, 19 for SARS-CoV-, and 519 for MERS-CoV) in each 60-nt bin that are different from the reference genome. The semi-transparent boxes mark the regions of high mutational rates due to artefacts of incomplete sequencing. These regions are shielded from calculations of gain and loss of immunogenicity. (d-f) The "lost" immunogenicity and "gained" immunogenicity due to mutations in each SARS-CoV-2 isolate compared with the reference genome. (d) CD8 + T epitopes, (e) CD4 + T epitopes, and (f) B cell epitopes. The red line indicates the total number of predicted B cell epitope-encoding amino acids in the reference sequence. (g-i) The same immunogenicity change analyses as in (d-f), but for SARS-CoV. (j-l) The same analysis for MERS-CoV. Sup. Fig. 1 The T cell epitope profiles of the European population. (a) SARS-CoV-2, (b) SARS-CoV, and (c) MERS-CoV. Sup. Fig. 2 The variation of T cell epitope profiles for SARS-CoV-2, SARS-CoV and MERS-CoV across populations. The heatmap represents the number of immunogenic binding epitopes across the binned genomes (500bp) of (a) SARS-CoV-2, (b) SARS-CoV and (c) MERS-CoV for the major HLA-A alleles shown as examples (allele frequency larger than 1%) in the European American (EA) and Hongkong Chinese (HK) populations. These major alleles are colored in black, blue or red if they are common to both EA and HK population, unique to EA population, or unique to HK population, respectively. On the right, the band of strength represents the cumulative number of immunogenic peptides, and the bands of EA and HK represent the HLA allele frequency of EA and HK populations, respectively. Fig. 3 Part of the multiple alignment results of all the SARS-CoV-2 strains. Fig. 4 B cell epitope-encoding amino acids in contiguous stretches of >10 epitope-encoding amino acids. (a) SARS-CoV-2, (b) SARS-CoV, and (c) MERS-CoV. Red lines denote the total numbers of epitope-encoding amino acids of the reference sequences. Sup. Sup. Fig. 5 The trend of immunogenicity to SARS-CoV-2 over time. (a) "Gained/lost" T cell immunogenicity (class I). (b) "Gained/lost" T cell immunogenicity (class II). (c) Changes in B cell immunogenicity (all epitope-encoding residues). (d) Changes in B cell immunogenicity (epitope-encoding residues in stretches of >10 epitope-encoding residues). The strains were grouped according to their collection time of


Output: The immune vulnerability landscape of the 2019 Novel Coronavirus, SARS- CoV-2