BME100 f2015:Group5 1030amL6

BME 100 Fall 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: Doug Brown Role(s) Name: Taylor Barda Role(s) Name: Mary Cauley Role(s) Name: Steven Stamm Role(s) Name: Andrew Soich Role(s) Name: Alexandra Davis Role(s) LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System BME100 successfully tested and diagnosed 34 patients for the disease-associated SNP. Teams of 6 individuals were responsible for 2 patients each – a combined effort of 17 teams. This allowed each team to wholly focus on only 2 patients rather than sacrifice the quality of the diagnoses in order to test more patients. Several methods were exercised to prevent error; these methods included the number of replicates per patient, the replication and testing of pure PCR positive and negative controls, and various other methods. During testing, micropipette tips were changed after every usage, glass slides were changed between each sample of PCR product and SYBR Green I, and thorough labelling of test tubes and materials were done to eliminate the risk of cross-contamination between samples. The Java program used, ImageJ, had specific calibration controls of its own that minimized error. Aside from taking three images of each PCR product and SYBR Green I solution, the RAWINTDEN of both the drop and the background were measured and subtracted – eliminating any discrepancies in light intensity within the box where the fluorimeter was located. The distance between the phone camera and the PCR product - SYBR Green I solution was initially measured and kept the same throughout the lab as another calibration control. In total, there were 30 successful tests, 2 inconclusive tests, and 2 blank tests. Of the 30 successful tests, 13 tested positive for the disease-associated SNP and 17 tested negative for the disease-associated SNP. During the lab, one of the main challenges came with successfully using the fluorimeter. The smartphone stand could easily be bumped – either messing up the distance accuracy between the smartphone and the fluorimeter or knocking over/jostling the smartphone itself. In addition, the absence of 2 results and the 2 inconclusive results could skew the frequency calculations. What Bayes Statistics Imply about This Diagnostic Approach Calculation 1: W​hat is the probability that a patient will get a positive final test conclusion, given a positive PCR reaction? Calculation 2: W​hat is the probability that a patient will get a negative final test conclusion, given a negative diagnostic signal? Calculation 3: W​hat is the probability that a patient will develop the disease, given a positive final test conclusion? Calculation 4: W​hat is the probability that a patient will not develop the disease, given a negative final test conclusion? Intro to Computer-Aided Design TinkerCAD Our Design Feature 1: Consumables This consumables kit includes only the consumables necessary for basic PCR, not any processes occurring post-PCR on the PCR products. Feature 2: Hardware - PCR Machine & Fluorimeter