TASK DEFINITION: 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.
PROBLEM: To examine the effect of provision of information about the infection control in the specific infection disease treatment unit in a city hospital on the outpatient's intention of outpatient service use, respondents who underwent outpatient medical care at the hospital (N = 821) were asked whether or not they intended to continue the outpatient visit at the hospital if a severe acute respiratory syndrome (SARS) patient was admitted to the unit. Although 56% of respondents replied that they could continue to visit the department if a SARS patient was admitted to the unit in the hospital before they read the information, the proportion of those who intended to continue outpatient care significantly increased by 15% after they read it. The logistic regression analyses revealed that respondents who had frequently visited the outpatient department (P < 0.001), those who felt relieved by reading the information about the unit (P < 0.001), and those who did not worry about nosocomial SARS infection inside the hospital (P < 0.001) were significantly more likely to reply that they would continue outpatient visits. We estimated that admission of a SARS patient to the unit would result in a 20% decrease in the cumulative total number of outpatients in the hospital during a 180-day interval after admission of a SARS patient to the unit, and the cumulative total number of outpatients increased by 7% after they read the information. This study suggests that providing outpatients with appropriate information about SARS infection control in the hospital had a statistically significant and substantial impact on the outpatients' intention to continue outpatient visits at the hospital.

SOLUTION: Estimation of the impact of providing outpatients with information about SARS infection control on their intention of outpatient visit

PROBLEM: Protein chips have emerged as a promising approach for a wide variety of applications including the identification of protein-protein interactions, protein-phospholipid interactions, small molecule targets, and substrates of proteins kinases. They can also be used for clinical diagnostics and monitoring disease states. This article reviews current methods in the generation and applications of protein microarrays. Understanding complex cellular systems will require the identification and analysis of each of its components and determining how they function together and are regulated. A critical step in this process is to determine the biochemical activities of the proteins and how these activities themselves are controlled and modified by other proteins. Traditionally, the biochemical activities of proteins have been elucidated by studying single molecules, one experiment at a time. This process is not optimal, as it is slow and labor intensive. In contrast to this traditional approach, high-throughput scientific methods have been developed in the last decade to optimize the study of large numbers of molecules, including DNA, proteins and metabolites. DNA microarrays in particular have proved valuable in genomic research (Schena et al., 1995) . They have been used to study gene expression patterns, to locate transcription factor binding sites, and to detect sequence mutations and deletions on a grand scale. However, DNA microarrays tell us only about the genes themselves and provide little information regarding the functions of the proteins they encode. More recently high throughput approaches have been developed for the study of proteins, including profiling of proteins using mass spectrometry (Gavin et al.,

SOLUTION: Protein Microarray Technology Types of protein microarrays

PROBLEM: Knowing which populations are most at risk for severe outcomes from an emerging infectious disease is crucial in deciding the optimal allocation of resources during an outbreak response. The case fatality ratio (CFR) is the fraction of cases that die after contracting a disease. The relative CFR is the factor by which the case fatality in one group is greater or less than that in a second group. Incomplete reporting of the number of infected individuals, both recovered and dead, can lead to biased estimates of the CFR. We define conditions under which the CFR and the relative CFR are identifiable. Furthermore, we propose an estimator for the relative CFR that controls for time-varying reporting rates. We generalize our methods to account for elapsed time between infection and death. To demonstrate the new methodology, we use data from the 1918 influenza pandemic to estimate relative CFRs between counties in Maryland. A simulation study evaluates the performance of the methods in outbreak scenarios. An R software package makes the methods and data presented here freely available. Our work highlights the limitations and challenges associated with estimating absolute and relative CFRs in practice. However, in certain situations, the methods presented here can help identify vulnerable subpopulations early in an outbreak of an emerging pathogen such as pandemic influenza.

SOLUTION:
Estimating Absolute and Relative Case Fatality Ratios from Infectious Disease Surveillance Data HHS Public Access