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".
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Question: Paragraph: Long non-coding RNAs (lncRNAs) are found not only in mammals but also in other organisms, including viruses. Recent findings suggest that lncRNAs play various regulatory roles in multiple major biological and pathological processes. During viral life cycles, lncRNAs are involved in a series of steps, including enhancing viral gene expression, promoting viral replication and genome packaging, boosting virion release, maintaining viral latency and assisting viral transformation; additionally, lncRNAs antagonize host antiviral innate immune responses. In contrast to proteins that function in viral infection, lncRNAs are expected to be novel targets for the modulation of all types of biochemical processes due to their broad characteristics and profound influence. This review highlights our current understanding of the regulatory roles of lncRNAs during viral infection processes with an emphasis on the potential usefulness of lncRNAs as a target for viral intervention strategies, which could have therapeutic implications for the application of a clinical approach for the treatment of viral diseases. 
 Title: Viral lncRNA: A regulatory molecule for controlling virus life cycle

Answer: False


Question: 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

Answer: False


Question: Paragraph: Nafamostat mesylate (NM), a synthetic serine protease inhibitor first placed on the market by Japan Tobacco in 1986, has been approved to treat inflammatory-related diseases, such as pancreatitis. Recently, an increasing number of studies have highlighted the promising effects of NM in inhibiting cancer progression. Alone or in combination treatments, studies have shown that NM attenuates various malignant tumors, including pancreatic, colorectal, gastric, gallbladder, and hepatocellular cancers. In this review, based on several activating pathways, including the canonical Nuclear factor-κB (NF-κB) signaling pathway, tumor necrosis factor receptor-1 (TNFR1) signaling pathway, and tumorigenesis-related tryptase secreted by mast cells, we summarize the anticancer properties of NM in existing studies both in vitro and in vivo. In addition, the efficacy and side effects of NM in cancer patients are summarized in detail. To further clarify NM's antitumor activities, clinical trials devoted to validating the clinical applications and underlying mechanisms are needed in the future. 
 Title: The Molecular Aspect of Antitumor Effects of Protease Inhibitor Nafamostat Mesylate and Its Role in Potential Clinical Applications

Answer:
True