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]: Containment of the SARS coronavirus (SCV) outbreak was accompanied by the rapid characterization of this new pathogen's genome sequence in 2003, encouraging the development of anti-SCV therapeutics using short interfering RNA (siRNA) inhibitors. A pair of siRNA duplexes identified as potent SCV inhibitors in vitro was evaluated for in vivo efficacy and safety in a rhesus macaque SARS model using intranasal administration with clinical viable delivery carrier in three dosing regimens. Observations of SCV-induced SARS-like symptoms, measurements of SCV RNA presence in the respiratory tract, microscopic inspections of lung histopathology, and immunohistochemistry sections from 21 tested macaques consistently demonstrated siRNA-mediated anti-SCV activity. The prophylactic and therapeutic efficacies resulted in relief of animals from SCV infectioninduced fever, diminished SCV in upper airway and lung alveoli, and milder acute diffuse alveoli damage (DAD). The dosages of siRNA used, 10 to 40 mg/kg, did not show any sign of siRNA-induced toxicity. These results support that a clinical investigation of this anti-SARS siRNA therapeutic agent is warranted. The study also illustrates the capability of siRNA to enable a massive reduction in development time for novel targeted therapeutic agents. We detail a representative example of large-mammal siRNA use.
[A]: Application of siRNA Against SARS in the Rhesus Macaque Model

[Q]: Background: Disaster is a serious public health issue. Health professionals and community residents are main players in disaster responses but their knowledge levels of disaster medicine are not readily available. This study aimed to evaluate knowledge levels and training needs of disaster medicine among potential disaster responders and presented a necessity to popularize disaster medicine education. Methods: A self-reporting questionnaire survey on knowledge level and training needs of disaster medicine was conducted in Shanghai, China, in 2012. A total of randomly selected 547 health professionals, 456 medical students, and 1,526 local residents provided intact information. The total response rate was 93.7%. Results: Overall, 1.3% of these participants have received systematic disaster medicine training. News media (87.1%) was the most common channel to acquire disaster medicine knowledge. Although health professionals were more knowledgeable than community residents, their knowledge structure of disaster medicine was not intact. Medical teachers were more knowledgeable than medical practitioners and health administrators (p = 0.002). Clinicians performed better than public health physicians (p,0.001), whereas public health students performed better than clinical medical students (p,0.001). In community residents, education background significantly affected the knowledge level on disaster medicine (p,0.001). Training needs of disaster medicine were generally high among the surveyed. 'Lecture' and 'practical training' were preferred teaching methods. The selected key and interested contents on disaster medicine training were similar between health professionals and medical students, while the priorities chosen by local residents were quite different from health professionals and medical students (p,0.001). Conclusions: Traditional clinical-oriented medical education might lead to a huge gap between the knowledge level on disaster medicine and the current needs of disaster preparedness. Continuing medical education and public education plans on disaster medicine via media should be practice-oriented, and selectively applied to different populations and take the knowledge levels and training needs into consideration.
[A]: Knowledge Levels and Training Needs of Disaster Medicine among Health Professionals, Medical Students, and Local Residents in Shanghai, China

[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