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African Swine Fever (ASF), endemic in many African countries, is now spreading to other continents. Though ASF is capable of incurring serious economic losses in affected countries, no vaccine exists to provide immunity to animals. Disease control relies largely on rapid diagnosis and the implementation of movement restrictions and strict

African Swine Fever (ASF), endemic in many African countries, is now spreading to other continents. Though ASF is capable of incurring serious economic losses in affected countries, no vaccine exists to provide immunity to animals. Disease control relies largely on rapid diagnosis and the implementation of movement restrictions and strict eradication programs. Developing a scalable, accurate and low cost diagnostic for ASF will be of great help for the current situation. CIM's 10K random peptide microarray is a new high-throughput platform that allows systematic investigations of immune responses associated with disease and shows promise as a diagnostic tool. In this study, this new technology was applied to characterize the immune responses of ASF virus (ASFV) infections and immunizations. Six sets of sera from ASFV antigen immunized pigs, 6 sera from infected pigs and 20 sera samples from unexposed pigs were tested and analyzed statistically. Results show that both ASFV antigen immunized pigs and ASFV viral infected pigs can be distinguished from unexposed pigs. Since it appears that immune responses to other viral infections are also distinguishable on this platform, it holds the potential of being useful in developing a new ASF diagnostic. The ability of this platform to identify specific ASFV antibody epitopes was also explored. A subtle motif was found to be shared among a set of peptides displaying the highest reactivity for an antigen specific antibody. However, this motif does not seem to match with any antibody epitopes predicted by a linear antibody epitope prediction.
ContributorsXiao, Liang (Author) / Sykes, Kathryn (Thesis advisor) / Zhao, Zhan-Gong (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
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Background: Vaccine-preventable diseases significantly influence the health and academic success of college students. Despite the known negative impact of these diseases, vaccination rates routinely fall short of national goals and recommendations. Although vaccination decisions are complex, a recommendation from a health care provider is one of the key motivators for

Background: Vaccine-preventable diseases significantly influence the health and academic success of college students. Despite the known negative impact of these diseases, vaccination rates routinely fall short of national goals and recommendations. Although vaccination decisions are complex, a recommendation from a health care provider is one of the key motivators for individuals receiving a vaccine. Motivational interviewing (MI), a counseling approach primarily used to address substance abuse, can be applied to other health-related behaviors.

Local Problem: Despite previous quality improvement efforts aimed at increasing vaccine rates for influenza, human papillomavirus (HPV), and meningitis B (MenB), vaccinations at large university health centers have been well below benchmarks set by Healthy People 2020.

Methods: This study was guided by the Theory of Planned Behavior and included MI training and regular reinforcement for health care providers to address vaccine hesitancy with college students.

Results: Influenza vaccination rates improved, but HPV vaccine rates remained stable and MenB vaccine rates decreased compared with the previous year. Clinicians demonstrated a significant increase in knowledge of MI techniques after a targeted educational intervention. Repeat measures indicate the potential for sustained improvement when ongoing reinforcement is provided.

Conclusion: MI can be an effective part of a strategy to increase vaccination rates.

ContributorsWermers, Rita (Author) / Ostroski, Tammy (Author, Thesis advisor) / Hagler, Debra (Author, Thesis advisor)
Created2019-04-26