BME100 f2014:Group31 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: Jimmy Xu Name: Andrew Liu Name: Andy Chang Name: Charles Bolton Name: Afshin Isadvesta Name: Michael Chatarachanwong LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System From the DNA test of a disease-associated SNP, a Bayesian calculations were done in order to determine probability of an accurate diagnosis. The division of labor was 34 teams averaging around 6 students each that diagnosed 68 total patients. Each patient was tested for a positive or negative reading, which was then compared to a positive or negative control. In this way, accuracy was tested. In order to further prevent errors, 3 trials per patient were run, to ensure proper reading. Two PCR controls, one positive with DNA in it and one negative without any DNA, were utilized to provide a benchmark for ImageJ pixel comparisons. Within ImageJ itself, the pixels were calibrated to match the drop size used in the measurement. Finally, three pictures of each trial were taken in order to give a more accurate reading of the drop. The following Bayesian calculations were done using the results shown below. The equation used to calculate probability of reading accuracy was: P(A|B)=(P(B|A)*P(A))/P(B) P(A|B) is the Probability of A given B P(B|A) is the Probability of B given A Probability of positive final test conclusion given a positive PCR reaction A=.48 B=.49 P(B|A)=.89 P(A|B)=.87 Probability of negative final test conclusion given a negative PCR reaction A=.39 B=.39 P(B|A)=.77 P(A|B)=.77 Probability patient will develop disease w/ positive final test conclusion A=.34 B=.48 P(B|A)=.43 P(A|B)=.30 Probability that a patient will not develop disease w/ negative final test conclusion A=.66 B=.39 P(B|A)=.27 P(A|B)=.46 What Bayes Statistics Imply about This Diagnostic Approach The results for calculations 1 and 2, which compared the diagnosis from the device and the reading of the PCR reaction as positive and negative, described the accuracy with which PCR readings are done. Essentially, the sensitivity and the specificity of detecting the actual disease SNP within the DNA. Both measurements were close to 1.00 or 100%, meaning the detection of the SNP within the DNA was relatively accurate. Although the measurements produced Bayes measurements close to 1, there were potential sources of error that detracted from their accurate measurement. One potential error may have arisen because of the placement of the camera. The slide may not have been entirely level with the camera, producing an image with a skewed view of the drop. This may have caused the fluorescence to be under or overestimated to the point that the SNP is not detected. Another source of error may have come from the actual structure of the box. Had there been crevices within the construction, light may have been increased, causing the fluorescence to be inaccurately measured. This would have possibly created a positive diagnosis for the SNP when there was no SNP sequence. Finally, the image quality itself is a potential source of error. With differing camera phones used by each group, the image quality may have varied. In addition, even though each camera was placed a set distance of about 4 cm. away from the slide, uncertainty may have been caused from differing levels of zoom, which would increase the image of the drop but alter the fluorescence measurement. All these errors may have resulted in the faulty detection of the SNP within the DNA. The calculations for 3 and 4 are considered the "diagnosis." These Bayesian calculations directly find the probability and accuracy of the device in predicting contraction of the disease. These are the sensitivity and specificity in predicting the disease. Both these values were both beneath .50 and thus, considered very small. This indicates a lower degree of accuracy in predicting the disease, especially when compared with detection of the SNP in calculations 1 and 2. These small values that result from the calculations are indicative of the necessity for redesign should a device be aimed towards predicting the disease. Computer-Aided Design TinkerCAD The TinkerCAD tool was very easy to use during our Computer-Aided Design lab and allowed us to redesign a part of the PCR machine with ease. The tool is user friendly and shapes can be quickly resized and duplicated, which were two extremely useful features since our group decided to modify the sample block of the PCR machine. The block was modified by rearranging the vial holes to fit one additional set of them. Other TinkerCAD features we made use of were the grouping feature and the color modification feature. Overall, TinkerCAD is recommended to use to quickly make mock-up designs through the convenience of a web browser. Our Design Works Cited: This image was directly copied from KickStarter.com and can open the link below! https://www.kickstarter.com/projects/930368578/openpcr-open-source-biotech-on-your-desktop Using TinkerCAD, the sample block of the PCR machine was redesigned to hold one more set of PCR samples than the original. Since it takes quite a bit of time for the PCR machine to process the DNA, adding one additional set increases the efficiency of the lab and gets results back to the patient faster. This redesign was chosen due to the fact that no major modification would have to be made to the heating lid. The costs to manufacture the machine would also remain the same. This minor modification improves the PCR machine without sacrificing any other aspect of the design or adding to the cost to manufacture it. Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter