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: Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect virulencerelated genes in novel agents, as we show here for the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. 
 Title: Interpretable detection of novel human viruses from genome sequencing data
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Paragraph: medRxiv preprint [Abstract]Objective Coronavirus disease 2019 (COVID-19) has become pandemic in the world. The need for IgG-IgM combined antibody test is booming, but data on diagnostic indexes evaluation was inadequate. The aim of this study was to evaluate diagnostic indexes of a rapid IgG-IgM combined antibody test for SARS-CoV-2. Methods A total of 179 patients were enrolled. Serum were collected for IgG-IgM combined antibody test and corresponding nasal and pharyngeal swab specimens were collected for SARS-CoV-2 RT-PCR. According to SARS-CoV-2 RT-PCR results, patients under study were categorized as PCR positive group in 90 patients and PCR negative group in 89 patients. Results 1. Of the 90 PCR positive samples, 77 were tested positive by SARS-CoV-2 IgG-IgM test kit, yielding a sensitivity of 85.6%. Meanwhile, of the 89 PCR negative sample, 8 samples were detected positive, resulting in a specificity of 91%. Positive predictive value, negative predictive value and accuracy of this test kit was 95.1%, 82.7%, and 88.3%, respectively. Kappa efficiency between IgG/IgM test kit and RT-PCR were 0.75. 2. Accuracy in mild/common and severe/critical subgroup were 73.9% and 97.7%, respectively. Accuracy in clinical confirmed, suspected cases and other disease subgroups were 70%, 60%, and 100%, respectively. 3. Patients were further divided into '0 -7', '8 -15' and '>= 16' groups according to the time from illness onset to sample collection. Sensitivity, specificity and accuracy in these three groups were 18.8%, 77.8% and 40%; 100%, 50% and 87.5%; 100%, 64.3%, and 93.9, respectively. Conclusion The sensitivity and specificity of this ease-of-use IgG/IgM combined test kit were adequate, plus short turnaround time, no specific requirements for additional equipment or skilled technicians, all of these collectively contributed to its competence for mass testing. At the current stage, it cannot take the place of SARA-CoV-2 nucleic acid RT-PCR, but can be served as a complementary option for RT-PCR. The combination of RT-PCR and IgG-IgM combined test kit could provide further insight into SARS-CoV-2 infection diagnosis. 
 Title: Diagnostic Indexes of a Rapid IgG/IgM Combined Antibody Test for SARS-CoV-2
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Paragraph: Background: Middle East Respiratory Syndrome (MERS) is a respiratory disease caused by a novel coronavirus that was identified in 2012 in Saudi Arabia. It is associated with significant mortality and morbidity. We identified factors associated with the Middle East Respiratory Syndrome-Coronavirus (MERS-CoV) infection among suspected cases presented with sign and symptoms of upper respiratory infection or exposure to the virus. We also looked at the impact of medication history on virus transmission. Method: We included subjects with suspected MERS-CoV infection and confirmed cases of MERS infection. Subjects were excluded if there were any missing data that prevent the final analysis. Descriptive statistics were used to report demographic data. Percentages and frequencies were used to summarize the categorical variables, while means and standard deviations were calculated for continuous variables. Logistic regression was used to assess the risk of MERS-CoV infection among the suspected cases. A value of p < 0.05 was considered statistically significant. Results: A total of 16,189 suspected cases were identified, complete data were analyzed for 3154 to assess factors that are independently associated with MERS-CoV infection. MERS-CoV infection was associated with age (adjusted odds ratio [AOR] = 1.06; 95% CI [1.02-1.098], P-value = 0.004), male gender (AOR = 1.617; 95% CI [1.365-1.77], P-value < 0.001) and diabetes (AOR = 1.68; 95% CI [1.346-1.848], P-value = 0.002. There was no significant association with the other comorbidities. Medication history was not associated with an increase or decrease the likelihood of the infection. Conclusions: MERS-Cov infection is more common in male, advanced age and diabetes. No medications were associated with an increase or decrease the likelihood of the infection. This is important to focus on screening and detection to this patient population. 
 Title: Identifying factors and target preventive therapies for Middle East Respiratory Syndrome sucsibtable patients

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