You will be given a definition of a task first, then an example. Follow the example to solve a new instance of the task.
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 severe acute respiratory syndrome (SARS) epidemic originating from China in 2002 was caused by a previously uncharacterized coronavirus that could be identified by specific RT-PCR amplification. Efforts to control future SARS outbreaks depend on the accurate and early identification of SARS-CoV infected patients. A real-time fluorogenic RT-PCR assay based on the 3 -noncoding region (3 -NCR) of SARS-CoV genome was developed as a quantitative SARS diagnostic tool. The ideal amplification efficiency of a sensitive SARS-CoV RT-PCR assay should yield an E value (PCR product concentration increase per amplification cycle) equal to 2.0. It was demonstrated that the 3 -NCR SARS-CoV based RT-PCR reactions could be formulated to reach excellent E values of 1.81, or 91% amplification efficacy. The SARS-CoV cDNA preparations derived from viral RNA extract and the cloned recombinant plasmid both exhibit the identical amplification characteristics, i.e. amplification efficacy using the same PCR formulation developed in this study. The viral genomic copy (or genomic equivalences, GE) per infectious unit (GE/pfu) of SARS-CoV used in this study was also established to be approximate 1200-1600:1. The assay's detection sensitivity could reach 0.005 pfu or 6-8 GE per assay. It was preliminarily demonstrated that the assay could efficiently detect SARS-CoV from clinical specimens of SARS probable and suspected patients identified in Taiwan. The 3 -NCR based SARS-CoV assay demonstrated 100% diagnostic specificity testing samples of patients with acute respiratory disease from a non-SARS epidemic region.
Title: Development and evaluation of an efficient 3 -noncoding region-based SARS coronavirus (SARS-CoV) RT-PCR assay for detection of SARS-CoV
Solution: True
Why? The answer is true because the title of the paragraph is correct where it described the development of 3 -noncoding region of SARS-CoV genome and assay efficiently detecting SARS-CoV from clinical specimens.

New input: Paragraph: Purpose: To examine the feasibility of using a computer tool for stratifying the severity of Coronavirus Disease 2019 (COVID-19) based on computed tomography (CT) images. Materials and methods: We retrospectively examined 44 confirmed COVID-19 cases. All cases were evaluated separately by radiologists (visually) and through an in-house computer software. The degree of lesions was visually scored by the radiologist, as follows, for each of the 5 lung lobes: 0, no lesion present; 1, <1/3 involvement; 2, >1/3 and < 2/3 involvement; and 3, >2/3 involvement. Lesion density was assessed based on the proportion of ground-glass opacity (GGO), consolidation and fibrosis of the lesions. The parameters obtained using the computer tool included lung volume (mL), lesion volume (mL), lesion percentage (%), and mean lesion density (HU) of the whole lung, right lung, left lung, and each lobe. The scores obtained by the radiologists and quantitative results generated by the computer software were tested for correlation. A Chi-square test was used to test the consistency of radiologist-and computerderived lesion percentage in the right/left lung, upper/lower lobe, and each of the 5 lobes. The results showed a strong to moderate correlation between lesion percentage scores obtained by radiologists and the computer software (r ranged from 0.7679 to 0.8373, P < 0.05), and a moderate correlation between the proportion of GGO and mean lesion density (r A0.5894, P < 0.05), and proportion of consolidation and mean lesion density (r 0.6282, P < 0.05). Computer-aided quantification showed a statistical significant higher lesion percentage for lower lobes than that assessed by the radiologists (c 2 8.160, P 0.004). Conclusions: Our experiments demonstrated that the computer tool could reliably and accurately assess the severity and distribution of pneumonia on CT scans.
Title: Quantitative computed tomography analysis for stratifying the severity of Coronavirus Disease 2019-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Solution:
False