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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.

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.
Solution: Development and evaluation of an efficient 3 -noncoding region-based SARS coronavirus (SARS-CoV) RT-PCR assay for detection of SARS-CoV
Why? It's a correct title because the paragraph described the development of 3 -noncoding region of SARS-CoV genome and assay efficiently detecting SARS-CoV from clinical specimens.

New input: Background: To control the spread of Corona Virus Disease , 30 screening large numbers of suspected cases for appropriate quarantine and 31 treatment is a priority. Pathogenic laboratory testing is the diagnostic gold standard 32 but it is time consuming with significant false negative results. Fast and accurate 33 diagnostic methods are urgently needed to combat the disease. Based on 34 COVID-19 radiographical changes in CT images, we aimed to develop a deep 35 learning method that could extract COVID-19's graphical features in order to provide 36 a clinical diagnosis ahead of the pathogenic test, thus saving critical time for 37 disease control. 38 Methods and Findings: We collected 1,065CT images of pathogen-confirmed 39 COVID-19 cases (325 images) along with those previously diagnosed with typical 40 viral pneumonia (740 images). We modified the Inception transfer-learning model 41 to establish the algorithm, followed by internal and external validation. The internal 42 validation achieved a total accuracy of 89.5% with specificity of 0.88 and sensitivity 43 of 0.87. The external testing dataset showed a total accuracy of 79.3% with 44 specificity of 0.83 and sensitivity of 0.67. In addition, in 54 COVID-19 images that 45 first two nucleic acid test results were negative, 46 were predicted as COVID-19 46 positive by the algorithm, with the accuracy of 85.2%. 47 Conclusion: These results demonstrate the proof-of-principle for using 48 artificial intelligence to extract radiological features for timely and accurate 49 COVID-19 diagnosis. 50
Solution:
Breast Cancer Prevention and Therapy, Ministry of Education