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Full-Text Articles in Analytical, Diagnostic and Therapeutic Techniques and Equipment
Serological Proteomic Screening And Evaluation Of A Recombinant Egg Antigen For The Diagnosis Of Low-Intensity Schistosoma Mansoni Infections In Endemic Area In Brazil, Vanessa Silva-Moraes, Lisa Marie Shollenberger, William Castro-Borges, Ana Lucia Teles Rabello, Donald A. Harn, Lia Carolina Soares Medeiros, Wander De Jesus Jeremias, Liliane Maria Vidal Siqueira, Caroline Stephane Salviano Pereira, Maria Luysa Camargos Pedrosa, Nathalie Bonatti Franco Almeida, Aureo Almeida, Jose Roberto Lambertucci, Nidia Francisca De Figueiredo Carneiro, Paulo Marcos Zech Coelho, Refaella Fortini Queiroz Grenfell
Serological Proteomic Screening And Evaluation Of A Recombinant Egg Antigen For The Diagnosis Of Low-Intensity Schistosoma Mansoni Infections In Endemic Area In Brazil, Vanessa Silva-Moraes, Lisa Marie Shollenberger, William Castro-Borges, Ana Lucia Teles Rabello, Donald A. Harn, Lia Carolina Soares Medeiros, Wander De Jesus Jeremias, Liliane Maria Vidal Siqueira, Caroline Stephane Salviano Pereira, Maria Luysa Camargos Pedrosa, Nathalie Bonatti Franco Almeida, Aureo Almeida, Jose Roberto Lambertucci, Nidia Francisca De Figueiredo Carneiro, Paulo Marcos Zech Coelho, Refaella Fortini Queiroz Grenfell
Biological Sciences Faculty Publications
Background
Despite decades of use of control programs, schistosomiasis remains a global public health problem. To further reduce prevalence and intensity of infection, or to achieve the goal of elimination in low-endemic areas, there needs to be better diagnostic tools to detect low-intensity infections in low-endemic areas in Brazil. The rationale for development of new diagnostic tools is that the current standard test Kato-Katz (KK) is not sensitive enough to detect low-intensity infections in low-endemic areas. In order to develop new diagnostic tools, we employed a proteomics approach to identify biomarkers associated with schistosome-specific immune responses in hopes of developing …
Using Quality Improvement Methods To Increase Use Of Pain Prevention Strategies For Childhood Vaccination., Jennifer Verrill Schurman, Amanda D. Deacy, Rebecca J. Johnson, Jolynn Parker, Kristi Williams, Dustin Wallace, Mark Connelly, Lynn Anson, Kevin Mroczka
Using Quality Improvement Methods To Increase Use Of Pain Prevention Strategies For Childhood Vaccination., Jennifer Verrill Schurman, Amanda D. Deacy, Rebecca J. Johnson, Jolynn Parker, Kristi Williams, Dustin Wallace, Mark Connelly, Lynn Anson, Kevin Mroczka
Manuscripts, Articles, Book Chapters and Other Papers
AIM: To increase evidence-based pain prevention strategy use during routine vaccinations in a pediatric primary care clinic using quality improvement methodology.
METHODS: Specific intervention strategies (i.e., comfort positioning, nonnutritive sucking and sucrose analgesia, distraction) were identified, selected and introduced in three waves, using a Plan-Do-Study-Act framework. System-wide change was measured from baseline to post-intervention by: (1) percent of vaccination visits during which an evidence-based pain prevention strategy was reported as being used; and (2) caregiver satisfaction ratings following the visit. Additionally, self-reported staff and caregiver attitudes and beliefs about pain prevention were measured at baseline and 1-year post-intervention …
Developing Novel Machine Learning Algorithms To Improve Sedentary Assessment For Youth Health Enhancement., Gowtham Kumar Golla, Jordan A. Carlson, Jun Huan, Jacqueline Kerr, Tarrah Mitchell, Kelsey Borner
Developing Novel Machine Learning Algorithms To Improve Sedentary Assessment For Youth Health Enhancement., Gowtham Kumar Golla, Jordan A. Carlson, Jun Huan, Jacqueline Kerr, Tarrah Mitchell, Kelsey Borner
Manuscripts, Articles, Book Chapters and Other Papers
Sedentary behavior of youth is an important determinant of health. However, better measures are needed to improve understanding of this relationship and the mechanisms at play, as well as to evaluate health promotion interventions. Wearable accelerometers are considered as the standard for assessing physical activity in research, but do not perform well for assessing posture (i.e., sitting vs. standing), a critical component of sedentary behavior. The machine learning algorithms that we propose for assessing sedentary behavior will allow us to re-examine existing accelerometer data to better understand the association between sedentary time and health in various populations. We collected two …