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Objective: To identify unique immunogenic epitopes of Zika virus non-structural 1 (NS1) antigen and produce immunoglobulin Y (IgY) for potential use in he diagnosis of of Zika virus infection. Methods: Immunogenic epitopes were identified using in silico B-cell epitope prediction. A synthetic peptide analog of the predicted epitope was used to induce antipeptide IgY production in hens which was purified using affinity chromatography. Presence of purified IgY and its binding specificity were performed by gel electrophoresis and ELISA, respectively. Results: Out of the nine continuous epitopes identified, the sequence at position 193-208 (LKVREDYSLECDPAVI) was selected and used to produce anti-peptide IgY. The produced IgY was found to bind to the synthetic analog of the Zika virus NS1 immunogenic epitope but not to other flaviviruses and random peptides from other pathogens. Conclusions: In this study, we identified an immunogenic epitope unique to Zika virus that can be used to develop a serodiagnostic tool that specifically detect Zika virus infection.
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Objective: To identify unique immunogenic epitopes of Zika virus non-structural 1 (NS1) antigen and produce immunoglobulin Y (IgY) for potential use in he diagnosis of of Zika virus infection. Methods: Immunogenic epitopes were identified using in silico B-cell epitope prediction. A synthetic peptide analog of the predicted epitope was used to induce antipeptide IgY production in hens which was purified using affinity chromatography. Presence of purified IgY and its binding specificity were performed by gel electrophoresis and ELISA, respectively. Results: Out of the nine continuous epitopes identified, the sequence at position 193-208 (LKVREDYSLECDPAVI) was selected and used to produce anti-peptide IgY. The produced IgY was found to bind to the synthetic analog of the Zika virus NS1 immunogenic epitope but not to other flaviviruses and random peptides from other pathogens. Conclusions: In this study, we identified an immunogenic epitope unique to Zika virus that can be used to develop a serodiagnostic tool that specifically detect Zika virus infection.
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Background@#Among the different criteria, the General Weighted Average Grade (PMGWAG) holds the biggest bearing on admission for the UP College of Medicine. However, GWAs are not comparable across different courses, different batches, different UP units and different schools. An Adjustment Factor is necessary to make PMGWAGs comparable and to level the playing field. @*Objectives@#This study covering a 24-year period aimed to compare various proposed Admission Adjusted Factors of %PMGWAG (Pre-Med GWAG) in terms of Pearson's Correlation, Linear Regression Models and Mean Differences with %MGWAG (Medical GWAG), Class Rank and Board Rating as Outcome variables. @*Methodology@#Various proposed Adjustment Factors were applied to %PMGWAG of medical students from Class 1990 to Class 2014 and Pearson's Correlation, Linear Regression Models and Mean Differences with %MGWAG, Class Rank and Board Rating were derived and analyzed. @*Results@#Adjustment Factor A3 as applied to %PMGWAG correlates best with Board Rating and Class Rank while Adjustment Factor A6 with %MGWAG. On Linear Regression, A3 likewise bested other Adjustment Factors in predicting %MGWAG and %Board Rating while A6 on predicting Class Ranking. Among the various adjustments, A3 exerted the most impact on the outcome variables, based on mean differences. @*Conclusion@#The A3 Adjustment Factor is the preferred and most ideal among the various proposed adjustment factors. Its application on %PMGWAG, correlated best with, most predictive of and most influential to %MGWAG, Board Rating and Class Rating.