TASK DEFINITION: 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".
PROBLEM: Paragraph: Pulmonary gene delivery is thought to play an important role in treating genetically related diseases and may induce immunity towards pathogens entering the body via the airways. In this study we prepared poly (D,L-lactide-co-glycolide) (PLGA) nanoparticles bearing polyethyleneimine (PEI) on their surface and characterized them for their potential in serving as non-viral gene carriers to the pulmonary epithelium. Particles that were synthesized at different PLGA-PEI ratios and loaded with DNA in several PEI -DNA ratios, exhibited narrow size distribution in all formulations, with mean particle sizes ranging between 207 and 231 nm. Zeta potential was strongly positive (above 30 mV) for all the PEI -DNA ratios examined and the loading efficiency exceeded 99% for all formulations. Internalization of the DNA-loaded PLGA -PEI nanoparticles was studied in the human airway submucosal epithelial cell line, Calu-3, and DNA was detected in the endo-lysosomal compartment 6 h after particles were applied. Cytotoxicity of these nanoparticles was dependent on the PEI -DNA ratio and best cell viability was achieved by PEI -DNA ratios 1:1 and 0.5:1. These findings demonstrate that PLGA-PEI nanoparticles are a potential new delivery system to carry genes to the lung epithelium. q 
 Title: PLGA -PEI nanoparticles for gene delivery to pulmonary epithelium

SOLUTION: False

PROBLEM: Paragraph: Infectious diseases are a leading cause of death globally. Decisions surrounding how to control an infectious disease outbreak currently rely on a subjective process involving surveillance and expert opinion. However, there are many situations where neither may be available. Modeling can fill gaps in the decision making process by using available data to provide quantitative estimates of outbreak trajectories. Effective reduction of the spread of infectious diseases can be achieved through collaboration between the modeling community and public health policy community. However, such collaboration is rare, resulting in a lack of models that meet the needs of the public health community. Here we show a Susceptible-Infectious-Recovered (SIR) model modified to include control measures that allows parameter ranges, rather than parameter point estimates, and includes a web user interface for broad adoption. We apply the model to three diseases, measles, norovirus and influenza, to show the feasibility of its use and describe a research agenda to further promote interactions between decision makers and the modeling community. Despite substantial public health improvements in the last century, infectious diseases remain one of the leading causes of both morbidity and mortality 1,2 . When confronted with an infectious disease outbreak, public health officials typically work to control the outbreak by performing assessments, analyzing surveillance data, identifying resources and interacting with subject mater experts 2-4 . Control measures are then implemented based on the cumulative information collected. These approaches rely heavily on good surveillance systems, access to experts, and good intuition about which control measures to use. As such, they are largely subjective, time consuming, and the infrastructure required is often not present in high disease burden areas. Modeling is an attractive supplemental method because of the ability to estimate an outbreak's trajectory and the effects of possible control measures in a timely manner. Compartmental models are historically common; they divide individuals into categories based on their disease status. The most common variant is the SIR model, named after the categories used-"susceptible", "infectious" and "recovered". Models of this nature have small computational requirements, and are thus commonly used as first pass attempts to characterize outbreaks or infections quickly 5 . For example, after the sudden emergence of Severe Acute Respiratory Syndrome (SARS) in the early 2000s, researchers used modeling to characterize the virus' epidemiology. Several used compartmental models with control measures like quarantine or isolation in various settings (e.g., hospitals or cities) to describe effects of possible interventions 6-9 . Similar work exists for essentially all well known infectious diseases. For example, Mandal et al. provide a review of models used for malaria 10 and Bauch et al. explore model use with respect to SARS and other emerging infectious diseases 6 . Methods among these groups are often similar, but tend to focus on specific diseases and locations of interest. In contrast, agent-based models use a bottom-up approach where the agents (these are often people) interact with particular rules to simulate outbreaks 11 . This allows simulations at high resolutions, but requires large amounts of data to parameterize the models, as well as substantial computational power. It is thought that they may reflect real world scenarios more accurately, but the lack of available epidemiological data necessitates assumptions that are difficult or impossible to test 11 . These models further require computational resources inaccessible to an average health department. Both agent-based and compartmental models exhibit additional features that are problematic to wide-spread model adoption. The first is the focus on particular disease-location pairs. This emphasis precludes application to 
 Title: An approach to and web-based tool for infectious disease outbreak intervention analysis OPEN

SOLUTION: False

PROBLEM: Paragraph: classified as a member of the genus coronavirus in the family coronaviridae. The enveloped virus has a positive-sense, single-stranded RNA genome of approximately 28 kilo-bases, which has a 5 cap structure and 3 polyadenylation tract. The complete genome sequence of infectious bronchitis virus (IBV), Beijing isolate, was determined by cloning sequencing and primer walking. The whole genome is 27733 nucleotides in length, has ten open reading frames: 5 -orf1a-orf1ab-s-3a-3b-e-m-6a-6b-n-3 . Alignments of the genome sequence of IBV Beijing isolate with those of two AIBV strains and one SARS coronavirus were performed respectively. The genome sequence of IBV Beijing isolate compared with that of the IBV strain LX4 (uncompleted, 19440 bp in size) was 91.2% similarity. However, the full-length genome sequence of IBV Beijing isolate was 85.2% identity to that of IBV Strain Beaudette, and was only 50.8% homology to that of SARS coronavirus. The results showed that the genome of IBV has remarkable variation. And IBV Beijing isolate is not closely related to SARS coronavirus. Phylogenetic analyses based on the whole genome sequence, S protein, M protein and N protein, also showed that AIBV Beijing isolate is lone virus in group and is distant from SARS coronavirus. In conclusion, this study will contribute to the studies of diagnosis and diseases control on IBV in China. Keywords: avian infectious bronchitis virus (AIBV), severe acute respiratory syndrome coronavirus (SARS-CoV), primer walking, sequence analysis. 
 Title: Genome sequencing and characterization analysis of a Beijing isolate of chicken coronavirus infectious bronchitis virus

SOLUTION:
False