BME100 s2015:Group15 12pmL6

BME 100 Spring 2015	Home People Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3 Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6 Course Logistics For Instructors Photos Wiki Editing Help
OUR COMPANY Name: Tyler McCluskey: Team Leader Name: Kelsie Hermanson: Driver Name: student Name: student Name: student LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System BME100 tested patients for the disease-associated SNP by utilizing PCR reactions and ImageJ to measure the phosphorescence of the samples. By measuring the phosphorescence of samples, it could be determined through utilization of positive and negative controls whether a patient was at an increased risk for the disease. Through division of labor, 68 patients were tested for the disease, but 20 patient's tests were inconclusive or were rejected for lack of data. Within each team, 2 sub-groups were created, those who captured data and those who examined and interpreted the data. Error in data was prevented by the creation of positive and negative controls for every group so that each data set could be compared against it. The separate controls effectively created individual ranges for which the data could be compared to. The number of replicates ensured that the average of the data would heed a value close to expected. The ImageJ calibration tools and color splitting tools allowed for the stripping of colors other than green that may have added to the amount of phosphorescence. By stripping all photos of blue and red pigment, the green is better measured. In addition, the calibration creates unity among all groups in measurement technique. While twenty of the patients' tests must be discarded, the patients whose data was not significantly skewed showed a high reliability as the Bayes values were nearly 1.00. What Bayes Statistics Imply about This Diagnostic Approach The results of calculations 1 and 2 imply that the reliability of the individual PCR replicates for concluding that a person has the disease or not is very credible as the Bayes values are very close to 1.00. Possible sources of error, be both human or machine, include but are not limited to: human error in data input, mis-measurement of data, rounding error, and machine malfunction. The data that was discarded led to a smaller pool of available data but positively-impacted the overall reliability of results. Computer-Aided Design TinkerCAD Our Design Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter