BME100 f2014:Group9 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 Kevin Koza Evan Targioni Logan Murphy Phil Liles Cameron Hiller Tiffany Gong LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System In the BME100 lab, multiple teams of students diagnosed 68 total patients to analyze their DNA to determine if they had a specific disease. Each group of students received all equipment necessary to perform PCR on the two patients, such as the samples themselves, the PCR mix, machine, and pipettor. The procedure for PCR was to combine a specific amount of the PCR mix with the target DNA by using the micropipettor, and place the samples in the OpenPCR machine. Multiple samples for each patient were run in the machine to prevent error. Two groups shared one OpenPCR machine, for a total analysis of 4 samples. Overall, the labor was divided among 34 groups for the analysis of all samples. Once the PCR was completed, the samples were added to a slide with SYBR Green I dye, and placed on a fluorimeter. This allowed the groups to analyze the amount of green fluorescence produced through the use of smartphone cameras and ImageJ software. Other samples with a positive and negative control were also analyzed, so that the patient samples could be compared to the ImageJ results of the controls. Three photos of each sample were analyzed, and the data from ImageJ allowed us to determine if the patient tested positive for the SNP. The results for positive and negative results, along with the determination of the presence of the SNP can be seen in the following: What Bayes Statistics Imply about This Diagnostic Approach Calculations 1 and 2 discuss the sensitivity and specificity of the system regarding the ability to detect the disease. Calculation 1 shows the probability that the patient will conclude positive, given a positive PCR test. This result was approaching 100%, showing that it is likely that the patient has the disease if they tested positive. Calculation 2 shows the probability that the patient will conclude negative, given a negative PCR test. The value for this was also near 100%. Error for this analysis could be involved in the ImageJ calibration or sample analysis, along with PCR contamination, or failure to properly determine correlation between the samples and the controls from ImageJ data. Calculations 3 and 4 discuss the sensitivity and specificity of the system regarding the ability to predict the disease. Calculation 3 shows the probability that the patient will develop the disease, given a positive test. The Bayes value for this was low. Calculation 4 shows the probability that the patient will not develop the disease, given a negative test. The value for this was approximately 50%, meaning that it is inconclusive. Once again, error for these results... Computer-Aided Design TinkerCAD Our Design Our design changed the OpenPCR machine to enable PCR on 32 samples rather than 16. This allows for more tests to be run quickly, maximizing efficiency of PCR. Also, since more samples can be run, one machine can serve the purpose of many, so the costs can be cut down. Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter The improved PCR machine will be able to run up to 32 samples at once. This allows for testing to be run quickly and more efficiently than before. Also, the improved fluorimeter system will have a cradle and camera in a set position from the sample slide. This will eliminate or marginalize variance in the photographs of the samples, thus improving accuracy of the testing. Also, ImageJ will be easier to run on these photographs because the samples should be in the same position every time.