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: Introduction-HIV-1 uses a programmed -1 ribosomal frameshift to generate Gag-Pol, the precursor of its enzymes, when its full-length mRNA is translated by the ribosomes of the infected cells. This change in the reading frame occurs at a so-called slippery sequence that is followed by a specific secondary structure, the frameshift stimulatory signal. This signal controls the frameshift efficiency. The synthesis of HIV-1 enzymes is critical for the virus replication and therefore, the -1 ribosomal frameshift could be the target of novel antiviral drugs. Areas covered-This review examines various approaches used to select drugs interfering with the -1 frameshift of HIV-1. These include the selection and modification of chemical compounds that specifically bind to the frameshift stimulatory signal, the use of antisense oligonucleotides targeting this signal and the selection of compounds that modulate HIV-1 frameshift, by using bicistronic reporters where the expression of the second cistron depends upon HIV-1 frameshift. Expert opinion-The most promising approach is the selection and modification of compounds specifically targeting HIV-1 frameshift stimulatory signal. The use of antisense oligonucleotides binding to the frameshift stimulatory signal is still questionable. The use of bicistronic reporters preferentially selects compounds that modulate the frameshift by targeting the ribosomes, which is less promising. 
 Title: Targeting frameshifting in the human immunodeficiency virus
True

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: Coronavirus disease 2019 (COVID-19) is a newly emerged infection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and has been pandemic all over the world. This study described acute kidney injury (AKI) at early stage of COVID-19 and its clinical significance. Three-hundred and fifty-five COVID-19 patients with were recruited and clinical data were collected from electronic medical records. Patient's prognosis was tracked and risk factors of AKI was analyzed. Of 355 COVID-19 patients, common, severe and critical ill cases accounted for 63.1%, 16.9% and 20.0%, respectively. On admission, 56 (15.8%) patients were with AKI. Although AKI was more common in critical ill patients with COVID-19, there was no significant association between oxygenation index and renal functional indices among COVID-19 patients with AKI. By multivariate logistic regression, male, older age and comorbidity with diabetes were three important independent risk factors predicting AKI among COVID-19 patients. Among 56 COVID-19 patients with AKI, 33.9% were died on mean 10.9 day after hospitalization. Fatality rate was obviously higher among COVID-+19 patients with AKI than those without AKI (RR=7.08, P<0.001). In conclusion, male elderly COVID-19 patients with diabetes are more susceptible to AKI. AKI at early stage may be a negative prognostic indicator for COVID-19. 
 Title: Acute kidney injury at early stage as a negative prognostic indicator of patients with COVID-19: a hospital-based retrospective analysis

False