In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

Q: Background. One health is a concept that was officially adopted by international organizations and scholarly bodies in 1984. It is the notion of combining human, animal, and environmental components to address global health challenges that have an ecological interconnectedness. Methods. A cross-sectional study of the available literature cited was conducted from January 1984 when the one health concept was adopted till December 2012 to examine the role of the one health approach towards zoonoses. Inclusion criteria included publications, professional presentations, funding allocations, official documentation books, and book chapters, and exclusion criteria included those citations written outside the period of review. Results. A total of 737 resources met the inclusion criteria and were considered in this review. Resources showed a continuous upward trend for the years from 2006 to 2012. The predominant resources were journal publications with environmental health as the significant scope focus for one health. There was also an emphasis on the distribution of the work from developed countries. All categories of years, resources, scopes, and country locale differed from the means ( = 0.000). Year of initiative, scope, and country locale showed a dependent relationship ( = 0.022, = 0.003, and = 0.021, resp.). Conclusion. Our findings demonstrate the rapid growth in embracing the concept of one health, particularly in developed countries over the past six years. The advantages and benefits of this approach in tackling zoonoses are manifold, yet they are still not seemingly being embraced in developing countries where zoonoses have the greatest impact.

A: Zoonoses and One Health: A Review of the Literature
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Q: A series of 4-aminoquinoline derivatives were synthesized by the reaction of 4-chloro-7-substituted-quinolines with the corresponding mono/ dialkyl amines. The structures of the synthesized compounds were confirmed by NMR and FAB-MS spectral and elemental analyses. Subsequently, the compounds were examined for their cytotoxic effects on two different human breast tumor cell lines: MCF7 and MDA-MB468. Although all compounds examined were quite effective on both cell lines, the compound N 0 -(7-chloro-quinolin-4-yl)-N,N-dimethyl-ethane-1,2-diamine emerged as the most active compound of the series. It was particularly potent against MDA-MB 468 cells when compared to chloroquine and amodiaquine. The compound butyl-(7-fluoro-quinolin-4-yl)-amine showed more potent effects on MCF-7 cells when compared to chloroquine. Therefore, 4-aminoquinoline can serve as the prototype molecule for further development of a new class of anticancer agents.

A: Synthesis and in vitro cytotoxicity evaluation of 4-aminoquinoline derivatives
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Q: Background: Recently, the outbreak of MERS-CoV infections caused worldwide attention to Saudi Arabia. The novel virus belongs to the coronaviruses family, which is responsible for causing mild to moderate colds. The control and command center of Saudi Ministry of Health issues a daily report on MERS-CoV infection cases. The infection with MERS-CoV can lead to fatal complications, however little information is known about this novel virus. In this paper, we apply two data mining techniques in order to better understand the stability and the possibility of recovery from MERS-CoV infections. Method: The Naive Bayes classifier and J48 decision tree algorithm were used to build our models. The dataset used consists of 1082 records of cases reported between 2013 and 2015. In order to build our prediction models, we split the dataset into two groups. The first group combined recovery and death records. A new attribute was created to indicate the record type, such that the dataset can be used to predict the recovery from MERS-CoV. The second group contained the new case records to be used to predict the stability of the infection based on the current status attribute. Results: The resulting recovery models indicate that healthcare workers are more likely to survive. This could be due to the vaccinations that healthcare workers are required to get on regular basis. As for the stability models using J48, two attributes were found to be important for predicting stability: symptomatic and age. Old patients are at high risk of developing MERS-CoV complications. Finally, the performance of all the models was evaluated using three measures: accuracy, precision, and recall. In general, the accuracy of the models is between 53.6% and 71.58%. We believe that the performance of the prediction models can be enhanced with the use of more patient data. As future work, we plan to directly contact hospitals in Riyadh in order to collect more information related to patients with MERS-CoV infections.

A:
Building predictive models for MERS-CoV infections using data mining techniques Building predictive models for MERS-CoV infections using data mining techniques 745
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