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: Objective: Tumor-treating fields are currently used to successfully treat various cancers; however, the specific pathways associated with its efficacy remain unknown in the immune responses. Here, we evaluated tumor-treating fields-mediated initiation of the macrophage-specific immune response. Materials and Methods: We subjected RAW 264.7 mouse macrophages to clinically relevant levels of tumor-treating fields (0.9 V/cm, 150 kHz) and evaluated alterations in cytokine expression and release, as well as cell viability. Additionally, we investigated the status of immunomodulatory pathways to determine their roles in tumor-treating fields-mediated immune activation. Results and Discussion: Our results indicated that tumor-treating fields treatment at 0.9 V/cm decreased cell viability and increased cytokine messenger RNA/protein levels, as well as levels of nitric oxide and reactive oxygen species, relative to controls. The levels of tumor necrosis factor a, interleukin 1b, and interleukin 6 were markedly increased in tumor-treating fields-treated RAW 264.7 cells cocultured with 4T1 murine mammary carcinoma cells compared with those in 4T1 or RAW 264.7 cells with or without tumor-treating fields treatment. Moreover, the viability of 4T1 cells treated with the conditioned medium of tumor-treating fields-stimulated RAW 264.7 cells decreased, indicating that macrophage activation by tumor-treating fields effectively killed the tumor cells. Moreover, tumor-treating fields treatment activated the nuclear factor kB and mitogen-activated protein kinase pathways involved in immunomodulatory signaling. Conclusion: These results provide critical insights into the mechanisms through which tumor-treating fields affect macrophagespecific immune responses and the efficacy of this method for cancer treatment. 
 Title: Tumor-Treating Fields Induce RAW264.7 Macrophage Activation Via NK-kB/MAPK Signaling Pathways

A: True
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Q: 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

A: False
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Q: Paragraph: A R T I C L E I N F O Geo-social interaction patterns Geo-social scale Location-based social network Agent-based epidemic models Social network analysis Infectious disease transmission and control High performance computing A B S T R A C T Cities play an important role in fostering and amplifying the transmission of airborne diseases (e.g., influenza) because of dense human contacts. Before an outbreak of airborne diseases within a city, how to determine an appropriate containment area for effective vaccination strategies is unknown. This research treats airborne disease spreads as geo-social interaction patterns, because viruses transmit among different groups of people over geographical locations through human interactions and population movement. Previous research argued that an appropriate scale identified through human geo-social interaction patterns can provide great potential for effective vaccination. However, little work has been done to examine the effectiveness of such vaccination at large scales (e.g., city) that are characterized by spatially heterogeneous population distribution and movement. This article therefore aims to understand the impact of geo-social interaction patterns on effective vaccination in the urbanized area of Portland, Oregon. To achieve this goal, we simulate influenza transmission on a large-scale location-based social network to 1) identify human geo-social interaction patterns for designing effective vaccination strategies, and 2) and evaluate the efficacy of different vaccination strategies according to the identified geo-social patterns. The simulation results illustrate the effectiveness of vaccination strategies based on geosocial interaction patterns in containing the epidemic outbreak at the source. This research can provide evidence to inform public health approaches to determine effective scales in the design of disease control strategies. 
 Title: A large-scale location-based social network to understanding the impact of human geo-social interaction patterns on vaccination strategies in an urbanized area

A:
False
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