Matching Items (2)
Description
Aberrant glycosylation has been shown to be linked to specific cancers, and using this idea, it was proposed that the levels of glycans in the blood could predict stage I adenocarcinoma. To track this glycosylation, glycan were broken down into glycan nodes via methylation analysis. This analysis utilized information from N-, O-, and lipid linked glycans detected from gas chromatography-mass spectrometry. The resulting glycan node-ratios represent the initial quantitative data that were used in this experiment.
For this experiment, two Sets of 50 µl blood plasma samples were provided by NYU Medical School. These samples were then analyzed by Dr. Borges’s lab so that they contained normalized biomarker levels from patients with stage 1 adenocarcinoma and control patients with matched age, smoking status, and gender were examined. An ROC curve was constructed under individual and paired conditions and AUC calculated in Wolfram Mathematica 10.2. Methods such as increasing size of training set, using hard vs. soft margins, and processing biomarkers together and individually were used in order to increase the AUC. Using a soft margin for this particular data set was proved to be most useful compared to the initial set hard margin, raising the AUC from 0.6013 to 0.6585. In regards to which biomarkers yielded the better value, 6-Glc/6-Man and 3,6-Gal glycan node ratios had the best with 0.7687 AUC and a sensitivity of .7684 and specificity of .6051. While this is not enough accuracy to become a primary diagnostic tool for diagnosing stage I adenocarcinoma, the methods examined in the paper should be evaluated further. . By comparison, the current clinical standard blood test for prostate cancer that has an AUC of only 0.67.
For this experiment, two Sets of 50 µl blood plasma samples were provided by NYU Medical School. These samples were then analyzed by Dr. Borges’s lab so that they contained normalized biomarker levels from patients with stage 1 adenocarcinoma and control patients with matched age, smoking status, and gender were examined. An ROC curve was constructed under individual and paired conditions and AUC calculated in Wolfram Mathematica 10.2. Methods such as increasing size of training set, using hard vs. soft margins, and processing biomarkers together and individually were used in order to increase the AUC. Using a soft margin for this particular data set was proved to be most useful compared to the initial set hard margin, raising the AUC from 0.6013 to 0.6585. In regards to which biomarkers yielded the better value, 6-Glc/6-Man and 3,6-Gal glycan node ratios had the best with 0.7687 AUC and a sensitivity of .7684 and specificity of .6051. While this is not enough accuracy to become a primary diagnostic tool for diagnosing stage I adenocarcinoma, the methods examined in the paper should be evaluated further. . By comparison, the current clinical standard blood test for prostate cancer that has an AUC of only 0.67.
ContributorsDe Jesus, Celine Spicer (Author) / Taylor, Thomas (Thesis director) / Borges, Chad (Committee member) / School of Mathematical and Statistical Sciences (Contributor) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) General Abilities Index (GAI) and Cognitive Proficiency Index (CPI) have been advanced as possible diagnostic markers of Attention-Deficit Hyperactivity Disorder (ADHD). Diagnostic utility statistics were used to test the ability of GAI-CPI difference scores to identify children with ADHD. Participants included an ADHD sample (n = 78), a referred but non-diagnosed hospital sample (n = 66), and a simulated sample with virtually identical psychometric characteristics as the WISC-IV 2,200 child standardization sample. Receiver Operating Characteristic (ROC) analyses were computed to determine the utility of GAI-CPI difference scores to identify children with ADHD. The GAI-CPI discrepancy method had an AUC of .64, 95% CI [0.58, 0.71] for the ADHD sample compared to the simulated normative sample and an AUC of .46, 95% CI [0.37, 0.56] for the ADHD sample compared to the referred but non-diagnosed hospital sample. These AUC scores indicate that the GAI-CPI discrepancy method has low accuracy.
ContributorsDevena, Sarah (Author) / Watkins, Marley W. (Thesis advisor) / Wodrich, David L (Committee member) / Sullivan, Amanda (Committee member) / Arizona State University (Publisher)
Created2010