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: To combat the threat of many emerging infectious diseases, DNA immunization offers a unique and powerful approach to the production of high-quality monoclonal antibodies (mAbs) against various pathogens. Compared with traditional protein-based immunization approaches, DNA immunization is efficient for testing novel immunogen designs, does not require the production or purification of proteins from a pathogen or the use of recombinant protein technology and is effective at generating mAbs against conformation-sensitive targets. Although significant progress in the use of DNA immunization to generate mAbs has been made over the last two decades, the literature does not contain an updated summary of this experience. The current review provides a comprehensive analysis of the literature, including our own work, describing the use of DNA immunization to produce highly functional mAbs, in particular, those against emerging infectious diseases. Critical factors such as immunogen design, delivery approach, immunization schedule, use of immune modulators and the role of final boost immunization are discussed in detail.

Output: DNA immunization as a technology platform for monoclonal antibody induction


Input: Consider Input: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious 24 disease localized to China, Japan, and Korea that is characterized by severe hemorrhage and a 25 high fatality rate. Currently, no specific vaccine or treatment has been approved for this 26 disease. To develop a therapeutic agent for SFTS, we isolated antibodies from a phage-27 displayed antibody library that was constructed from a patient who recovered from SFTS 28 virus (SFTSV) infection. One antibody, designated as Ab10, was reactive to the Gn envelope 29 glycoprotein of SFTSV and protected host cells and A129 mice from infection in both in 30 vitro and in vivo experiments. Notably, Ab10 protected 80% of mice, even when injected 5 31 days after inoculation with a lethal dose of SFTSV. Using cross-linker assisted mass 32 spectrometry and alanine scanning, we located the non-linear epitope of Ab10 on the Gn 33 glycoprotein domain II and an unstructured stem region, suggesting that Ab10 may inhibit a 34 conformational alteration that is critical for cell membrane fusion between the virus and host 35 cell. Ab10 reacted to recombinant Gn glycoprotein in Gangwon/Korea/2012, HB28, and SD4 36 strains. Additionally, based on its epitope, we predict that Ab10 binds the Gn glycoprotein in 37 247 of 272 reported SFTSV isolates previously reported. Together, these data suggest that 38 Ab10 has potential to be developed into a therapeutic agent that could protect against more 39 than 90% of reported SFTSV isolates. 40 41

Output: An anti-Gn glycoprotein antibody from a convalescent patient potently 2 inhibits the infection of severe fever with thrombocytopenia syndrome virus


Input: Consider Input: Background: GenBank contains over 3 million viral sequences. The National Center for Biotechnology Information (NCBI) previously made available a tool for validating and annotating influenza virus sequences that is used to check submissions to GenBank. Before this project, there was no analogous tool in use for non-influenza viral sequence submissions. Results: We developed a system called VADR (Viral Annotation DefineR) that validates and annotates viral sequences in GenBank submissions. The annotation system is based on the analysis of the input nucleotide sequence using models built from curated RefSeqs. Hidden Markov models are used to classify sequences by determining the RefSeq they are most similar to, and feature annotation from the RefSeq is mapped based on a nucleotide alignment of the full sequence to a covariance model. Predicted proteins encoded by the sequence are validated with nucleotide-to-protein alignments using BLAST. The system identifies 43 types of "alerts" that (unlike the previous BLAST-based system) provide deterministic and rigorous feedback to researchers who submit sequences with unexpected characteristics


Output: VADR: validation and annotation of virus sequence submissions to GenBank