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 recent Ebola virus disease outbreak emphasized the potential misuse of personal protective equipment (PPE) by health care workers (HCWs) during such an event. We aimed to compare selfperceived proficiency of PPE use and objective performance, and identify predictors of low compliance and PPE misuse. Methods: An observational study combined with subjective questionnaires were carried out during a bioterror simulation drill. Forty-two observers evaluated performance under PPE. Mistakes were recorded and graded using a structured observational format and were correlated with the subjective questionnaires and with demographic parameters. Results: One hundred seventy-eight HCWs from community clinics and hospitals were included. The mean self-perceived proficiency was high (6.1 out of 7), mean level of comfort was moderate (4.0 out of 7), and mean objective performance was intermediate (9.5 out of 13). There was no correlation between comfort and objective performance scores. Self-perceived proficiency was in correlation with donning and continuous performance with PPE but not with doffing. Clinic personnel performed better than personnel in hospitals (40.3% vs 67.8% with 3 or more mistakes, respectively; P = .001). Demographic characteristics had no correlation with objective or self-perceived performance. Conclusions: Self-perceived proficiency is a poor predictor of appropriate PPE use. The results suggest poor awareness of the possibility of PPE misuse.
Title: The association between self-perceived proficiency of personal protective equipment and objective performance: An observational study during a bioterrorism simulation drill
False

Paragraph: The NIAID (National Institute for Allergy and Infectious Diseases) Biodefense Proteomics program aims to identify targets for potential vaccines, therapeutics, and diagnostics for agents of concern in bioterrorism, including bacterial, parasitic, and viral pathogens. The program includes seven Proteomics Research Centers, generating diverse types of pathogen-host data, including mass spectrometry, microarray transcriptional profiles, protein interactions, protein structures and biological reagents. The Biodefense Resource Center (www.proteomicsresource.org) has developed a bioinformatics framework, employing a protein-centric approach to integrate and support mining and analysis of the large and heterogeneous data. Underlying this approach is a data warehouse with comprehensive protein + gene identifier and name mappings and annotations extracted from over 100 molecular databases. Value-added annotations are provided for key proteins from experimental findings using controlled vocabulary. The availability of pathogen and host omics data in an integrated framework allows global analysis of the data and comparisons across different experiments and organisms, as illustrated in several case studies presented here. (1) The identification of a hypothetical protein with differential gene and protein expressions in two host systems (mouse macrophage and human HeLa cells) infected by different bacterial (Bacillus anthracis and Salmonella typhimurium) and viral (orthopox) pathogens suggesting that this protein can be prioritized for additional analysis and functional characterization. (2) The analysis of a vaccinia-human protein interaction network supplemented with protein accumulation levels led to the identification of human Keratin, type II cytoskeletal 4 protein as a potential therapeutic target. (3) Comparison of complete genomes from pathogenic variants coupled with experimental information on complete proteomes allowed the identification and prioritization of ten potential diagnostic targets from Bacillus anthracis. The integrative analysis across data sets from multiple centers can reveal potential functional significance and hidden relationships between pathogen and host proteins, thereby providing a systems approach to basic understanding of pathogenicity and target identification.
Title: Systems Integration of Biodefense Omics Data for Analysis of Pathogen-Host Interactions and Identification of Potential Targets
False

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/)
False