BME100 f2014:Group10 L6

BME 100 Fall 2014	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: student Name: student Name: student Name: student Name: student Name: student LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System The BME class tested 68 patients for a disease-associated SNP. 34 teams of 6 students each worked to diagnose the patients. Each team was assigned two patients to test for the designated SNP. 3 replicates were tested per patient to decrease error from contamination of samples and natural sample variation. Each sample was mixed with green fluorescent protein, which marked the concentration of SNP positive DNA within the sample. The patient samples were each compared to a positive and negative control, which was tested using the same method. The samples were mixed with the GFP mixture. 80 μL drops of the mixture were then dropped onto slides. Images were then captured of the drops while they were in complete darkness so the fluorescence could be captured. These images were then analyzed with ImageJ software to determine the amount of green in the image. Solutions of a known DNA concentration were run through the ImageJ software to determine a calibration curve, which was then used to determine the DNA concentration of the patient samples. 3 drop images were used for the ImageJ calculations per PCR sample to reduce error. These concentration values allowed the researchers to determine which patients tested positive for the disease. These values are representative of the final values found by the BME100 class. What Bayes Statistics Imply about This Diagnostic Approach The Bayesian statistics values for calculations 1 and 2 imply that the PCR replicates are reliable for concluding whether or not a patient has the disease SNP. The Bayes values were both moderately close to 1.00 (100%). The Bayes values could have been negatively effected by various factors. The PCR tubes could have been switched during testing through poor labeling practices or communication. The Bayes values could also have been effected by the deterioration of GFP solution when exposed to light. Other error could have been caused because of the differences between cameras used, which would express the fluorescence differently. Calculations 3 and 4 imply that The PCR reliability for prediction of the disease is moderate to low, because the Bayes values are close to 50%. Computer-Aided Design TinkerCAD Our Design Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter