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.

Example Input: Despite flaviviruses remaining the leading cause of systemic human infections worldwide, ocular manifestations of these mosquito-transmitted viruses are considered relatively uncommon in part due to under-reporting. However, recent outbreaks of Zika virus (ZIKV) implicated in causing multiple ocular abnormalities, such as conjunctivitis, retinal hemorrhages, chorioretinal atrophy, posterior uveitis, optic neuritis, and maculopathies, has rejuvenated a significant interest in understanding the pathogenesis of flaviviruses, including ZIKV, in the eye. In this review, first, we summarize the current knowledge of the major flaviviruses (Dengue, West Nile, Yellow Fever, and Japanese Encephalitis) reported to cause ocular manifestations in humans with emphasis on recent ZIKV outbreaks. Second, being an immune privilege organ, the eye is protected from systemic infections by the presence of blood-retinal barriers (BRB). Hence, we discuss how flaviviruses modulate retinal innate response and breach the protective BRB to cause ocular or retinal pathology. Finally, we describe recently identified infection signatures of ZIKV and discuss whether these system biology-predicted genes or signaling pathways (e.g., cellular metabolism) could contribute to the pathogenesis of ocular manifestations and assist in the development of ocular antiviral therapies against ZIKV and other flaviviruses.
Example Output: Ocular Manifestations of Emerging Flaviviruses and the Blood-Retinal Barrier

Example Input: The severe acute respiratory syndrome (SARS) crisis led to the construction of a negative pressure operating theatre at a hospital in Hong Kong. It is currently used for treatment of suspected or confirmed airborne infection cases, and was built in anticipation of a return of SARS, an outbreak of avian influenza or other respiratory epidemics. This article describes the physical conversion of a standard positive pressure operating theatre into a negative pressure environment, problems encountered, airflow design, and evaluation of performance. Since entering regular service, routine measurements and observations have indicated that the airflow performance has been satisfactory. This has also been confirmed by regular air sampling checks. Computational fluid dynamics, a computer modelling technique, was used to compare the distribution of room air before and after the design changes from positive to negative pressure. The simulation results show that the physical environment and the dispersion pattern of bacteria in the negative pressure theatre were as good as, if not better than, those in the original positive pressure design.
Example Output: Conversion of operating theatre from positive to negative pressure environment

Example Input: Differentiating dengue patients from other acute febrile illness patients is a great challenge among physicians. Several dengue diagnosis methods are recommended by WHO. The application of specific laboratory tests is still limited due to high cost, lack of equipment, and uncertain validity. Therefore, clinical diagnosis remains a common practice especially in resource limited settings. Bayesian networks have been shown to be a useful tool for diagnostic decision support. This study aimed to construct Bayesian network models using basic demographic, clinical, and laboratory profiles of acute febrile illness patients to diagnose dengue. Data of 397 acute undifferentiated febrile illness patients who visited the fever clinic of the Bangkok Hospital for Tropical Diseases, Thailand, were used for model construction and validation. The two best final models were selected: one with and one without NS1 rapid test result. The diagnostic accuracy of the models was compared with that of physicians on the same set of patients. The Bayesian network models provided good diagnostic accuracy of dengue infection, with ROC AUC of 0.80 and 0.75 for models with and without NS1 rapid test result, respectively. The models had approximately 80% specificity and 70% sensitivity, similar to the diagnostic accuracy of the hospital's fellows in infectious disease. Including information on NS1 rapid test improved the specificity, but reduced the sensitivity, both in model and physician diagnoses. The Bayesian network model developed in this study could be useful to assist physicians in diagnosing dengue, particularly in regions where experienced physicians and laboratory confirmation tests are limited.
Example Output:
Accuracy of dengue clinical diagnosis with and without NS1 antigen rapid test: Comparison between human and Bayesian network model decision