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".

Input: Consider Input: Paragraph: The present study focuses on the neuroprotective effect of glycyrrhizic acid (GA, a major compound separated from Glycyrrhiza Radix, which is a crude Chinese traditional drug) against glutamate-induced cytotoxicity in differentiated PC12 (DPC12) cells. The results showed that GA treatment improved cell viability and ameliorated abnormal glutamate-induced alterations in mitochondria in DPC12 cells. GA reversed glutamate-suppressed B-cell lymphoma 2 levels, inhibited glutamateenhanced expressions of Bax and cleaved caspase 3, and reduced cytochrome C (Cyto C) release. Exposure to glutamate strongly inhibited phosphorylation of AKT (protein kinase B) and extracellular signal-regulated kinases (ERKs); however, GA pretreatment enhanced activation of ERKs but not AKT. The presence of PD98059 (a mitogen-activated protein/extracellular signal-regulated kinase kinase [MEK] inhibitor) but not LY294002 (a phosphoinositide 3-kinase [PI3K] inhibitor) diminished the potency of GA for improving viability of glutamate-exposed DPC12 cells. These results indicated that ERKs and mitochondriarelated pathways are essential for the neuroprotective effect of GA against glutamate-induced toxicity in DPC12 cells. The present study provides experimental evidence supporting GA as a potential therapeutic agent for use in the treatment of neurodegenerative diseases. 
 Title: ERKs and mitochondria-related pathways are essential for glycyrrhizic acid-mediated neuroprotection against glutamate-induced toxicity in differentiated PC12 cells

Output: True


Input: Consider Input: 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

Output: False


Input: Consider Input: Paragraph: As the human lung is exposed to a variety of microbial pathogens in the environment, a first line of defense is built up by pulmonary cells like bronchial/ alveolar epithelial cells and alveolar macrophages. These cells express several pattern recognition receptors (PRRs) recognizing highly conserved microbial motifs and initiating the production of chemokines and pro-and anti-inflammatory cytokines acting as transmembrane or intracellular receptors. This might not only lead to acute but also to chronic inflammation which is discussed as an underlying mechanism in the pathogenesis of different lung diseases. 
 Title: Innate immunity in the human lung: pathogen recognition and lung disease


Output: False