In this task, you're given a paragraph and title from the research paper. Your task is to classify whether the given title is suitable or not for the research paper based on the given paragraph. Return "True" if title is proper according to paragraph else "False".

Paragraph: The coronavirus disease 2019 pandemic poses an enormous challenge to healthcare systems in affected communities. Older patients and those with pre-existing medical conditions have been identified as populations at risk of a severe disease course. It remains unclear at this point to what extent chronic liver diseases should be considered as risk factors, due to a shortage of appropriate studies. However, patients with advanced liver disease and those after liver transplantation represent vulnerable patient cohorts with an increased risk of infection and/or a severe course of COVID-19. In addition, the current pandemic requires unusual allocation of healthcare resources which may negatively impact the care of patients with chronic liver disease that continue to require medical attention. Thus, the challenge hepatologists are facing is to promote telemedicine in the outpatient setting, prioritise outpatient contacts, avoid nosocomial dissemination of the virus to patients and healthcare providers, and at the same time maintain standard care for patients who require immediate medical attention.
Title: Care of patients with liver disease during the COVID-19 pandemic: EASL-ESCMID position paper
False

Paragraph: The binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities. We developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to outperform several prominent methods in identifying strongly binding peptides. As a method with demonstrated performance in the quantitative modeling of MHCpeptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.
Title: Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models
False

Paragraph: While the ferret is a valuable animal model for a number of human viral infections, such as influenza, Hendra and Nipah, evaluating the cellular immune response following infection has been hampered by the lack of a number of species-specific immunological reagents. Interleukin 2 (IL-2) is one such key cytokine. Ferret recombinant IL-2 incorporating a C-terminal histidine tag was expressed and purified and the three-dimensional structure solved and refined at 1.89 A by X-ray crystallography, which represents the highest resolution and first non-human IL-2 structure. While ferret IL-2 displays the classic cytokine fold of the four-helix bundle structure, conformational flexibility was observed at the second helix and its neighbouring region in the bundle, which may result in the disruption of the spatial arrangement of residues involved in receptor binding interactions, implicating subtle differences between ferret and human IL-2 when initiating biological functions. Ferret recombinant IL-2 stimulated the proliferation of ferret lymph node cells and induced the expression of mRNA for IFN-g and Granzyme A. Crown
Title: Structural and functional characterisation of ferret interleukin-2
True