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: ('filoviruses') that cause severe hemorrhagic fever with human case fatality rates of up to 90%. Filovirus infection requires fusion of the host cell and virus membranes, a process that is mediated by the envelope glycoprotein (GP). GP contains two subunits, the surface subunit (GP1), which is responsible for cell attachment, and the transmembrane subunit (GP2), which catalyzes membrane fusion. The GP2 ectodomain contains two heptad repeat regions, N-terminal and C-terminal (NHR and CHR, respectively) that adopt a six-helix bundle during the fusion process. The refolding of this six-helix bundle provides the thermodynamic driving force to overcome barriers associated with membrane fusion. Here we report the crystal structure of the MARV GP2 core domain in its post-fusion (six-helix bundle) conformation at 1.9 Å resolution. The MARV GP2 core domain backbone conformation is virtually identical to that of EBOV GP2 (reported previously), and consists of a central NHR core trimeric coiled-coil packed against peripheral CHR α-helices and an intervening loop/helix-turn-helix segment. We previously reported that the stability of the MARV GP2 post-fusion structure is highly pH-dependent, with increasing stability at lower pH [Harrison, J.S.; Koellhoffer, J. K.; Chandran, K.; and Lai, J. R. Biochemistry, 2012Biochemistry, , 51, 2515Biochemistry, -2525. We hypothesized that this pH-dependent stability provides a mechanism for conformational control such that the post-fusion six helix bundle is promoted in the environments of appropriately matured endosomes. In this report, a structural rationale for this pH-dependent stability is described, and involves a high-density array of core and surface acidic side chains at the midsection of the structure, termed the 'anion stripe.' In addition, many surface-exposed salt bridges likely contribute to stabilizing the post-fusion structure at low pH. These results provide structural insights into the mechanism of MARV GP2-mediated membrane fusion.
Example Output: Crystal Structure of the Marburg Virus GP2 Core Domain in its Post-Fusion Conformation †

Example Input: Electron microscopy (EM) allows fast visualization of viruses in a wide range of clinical specimens. Viruses are grouped into families based on their morphology. Viruses from various families look distinctly and these morphological variances are the basis for identification of viruses by EM. The identification to the family level is often sufficient for the clinician or recognition of an unknown infectious agent. Diagnostic EM has two advantages over enzyme-linked immunosorbent assay and nucleic acid amplification tests. After a simple and fast negative staining, EM allows fast morphological identification and differential diagnosis of infectious agents contained in the specimen without the need for special considerations and/or reagents. Nevertheless, EM has the disadvantage of being unsuitable as a screening method. Abbreviations CSF cerebrospinal fluid PBS phosphate-buffered saline ELISA enzyme-linked immunosorbent assay PCR polymerase chain reaction EM electron microscopy PTA phosphotungstic acid IEM immune electron microscopy SARS severe acute respiratory syndrome NAT nucleic acid amplification (techniques) SPIEM solid phase immune electron microscopy NRL/ELM
Example Output: The Role of Electron Microscopy in the Rapid Diagnosis of Viral Infections -review and Foreign Cell in Clinical Specimens CONTENTS

Example Input: Background: Infections are often associated to comorbidity that increases the risk of medical conditions which can lead to further morbidity and mortality. SARS is a threat which is similar to MERS virus, but the comorbidity is the key aspect to underline their different impacts. One UK doctor says "I'd rather have HIV than diabetes" as life expectancy among diabetes patients is lower than that of HIV. However, HIV has a comorbidity impact on the diabetes. Results: We present a quantitative framework to compare and explore comorbidity between diseases. By using neighbourhood based benchmark and topological methods, we have built comorbidity relationships network based on the OMIM and our identified significant genes. Then based on the gene expression, PPI and signalling pathways data, we investigate the comorbidity association of these 2 infective pathologies with other 7 diseases (heart failure, kidney disorder, breast cancer, neurodegenerative disorders, bone diseases, Type 1 and Type 2 diabetes). Phenotypic association is measured by calculating both the Relative Risk as the quantified measures of comorbidity tendency of two disease pairs and the φ-correlation to measure the robustness of the comorbidity associations. The differential gene expression profiling strongly suggests that the response of SARS affected patients seems to be mainly an innate inflammatory response and statistically dysregulates a large number of genes, pathways and PPIs subnetworks in different pathologies such as chronic heart failure (21 genes), breast cancer (16 genes) and bone diseases (11 genes). HIV-1 induces comorbidities relationship with many other diseases, particularly strong correlation with the neurological, cancer, metabolic and immunological diseases. Similar comorbidities risk is observed from the clinical information. Moreover, SARS and HIV infections dysregulate 4 genes (ANXA3, GNS, HIST1H1C, RASA3) and 3 genes (HBA1, TFRC, GHITM) respectively that affect the ageing process. It is notable that HIV and SARS similarly dysregulated 11 genes and 3 pathways. Only 4 significantly dysregulated genes are common between SARS-CoV and MERS-CoV, including NFKBIA that is a key regulator of immune responsiveness implicated in susceptibility to infectious and inflammatory diseases. Our method presents a ripe opportunity to use data-driven approaches for advancing our current knowledge on disease mechanism and predicting disease comorbidities in a quantitative way.
Example Output:
Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies