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".

Q: Paragraph: The new era in systems pharmacology has revolutionized the human biology. Its applicability, precise treatment, adequate response and safety measures fit into all the paradigm of medical/clinical practice. The importance of mathematical models in understanding the disease pathology and epideomology is now being realized. The advent of high-throughput technologies and the emergence of systems biology have resulted in the creation of systems pharmacogenomics and the focus is now on personalized medicine. However, there are some regulatory issues that need to be addresssed; are we ready for this universal adoption? This article details some of the infectious disease pharmacogenomics to the developments in this area.
Title: System pharmacogenomics application in infectious diseases

A: True
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Q: Paragraph: Objective: The aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia. Methods: A total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission. Results: 76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR = 5.20, 95% CI 1.41-12.52, p = 0.010; 5 points), lymphocyte 0.8 * 10 9 /L (OR = 4.53, 95% CI 2.55-8.05, p < 0.001; 4 points), bacterial coinfection (OR = 3.71, 95% CI 2.11-6.51, p < 0.001; 4 points), acute-smoker (OR = 3.19, 95% CI 1.34-6.26, p = 0.001; 3 points), quit-smoker (OR = 2.18, 95% CI 0.99-4.82, p = 0.054; 2 points), hypertension (OR = 2.39, 95% CI 1.55-4.26, p = 0.003; 2 points) and age 60 years (OR = 2.14, 95% CI 1.04-4.39, p = 0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs. 0.717, p < 0.001). Conclusion: Here, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.
Title: Citation: Clinical Features Predicting Mortality Risk in Patients With Viral Pneumonia: The MuLBSTA Score

A: False
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Q: Paragraph: , is an infectious disease found worldwide. Children infected with MTB are more likely to progress to active TB (ATB); however, the molecular mechanism behind this process has long been a mystery. We employed the label-free quantitative proteomic technology to identify and characterize differences in plasma proteins between ATB and latent TB infection (LTBI) in children. To detect differences that are indicative of MTB infection, we first selected proteins whose expressions were markedly different between the ATB and LTBI groups and the control groups (inflammatory disease control (IDC) and healthy control (HC) groups). A total of 521 proteins differed (> 1.5-fold or < 0.6-fold) in the LTBI group, and 318 proteins in the ATB group when compared with the control groups. Of these, 49 overlapping proteins were differentially expressed between LTBI and ATB. Gene Ontology (GO) analysis revealed most proteins had a cellular and organelle distribution. The MTB infection status was mainly related to differences in binding, cellular and metabolic processes. XRCC4, PCF11, SEMA4A and ATP11A were selected and further verified by qPCR and western blot. At the mRNA level, the expression of XRCC4, PCF11and SEMA4A presented an increased trend in ATB group compare with LTBI. At the protein level, the expression of all these proteins by western blot in ATB/LTBI was consistent with the trends from proteomic detection. Our results provide important data for future mechanism studies and biomarker selection for MTB infection in children.
Title: Characterization of plasma proteins in children of different Mycobacterium tuberculosis infection status using label-free quantitative proteomics

A:
False
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