BME100 f2014:Group3 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: Marissa Seelhammer Name: Brianna Denuit Name: Farhad Eghbalian Name: Shane Mitchell Name: Catherine Piatak Name: Nivenka Mahesh LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System What Bayes Statistics Imply about This Diagnostic Approach Calculation 1: This shows how accurate the test is to detect a positive result for SNP (sensitivity). The result for this test showed that the probability of a patent getting a positive result for having SNP from the PCR machine given that they have the disease is 89%. Calculation 2: This shows how accurate the test is to detect a negative result for SNP (specificity). The result for this test showed that the probability of a patent getting a negative result for having SNP from the PCR machine given that they do not have the disease is 77%. In other words, both of these show accuracy but also have room for improvement. This room for improvement comes from the error that occurred during the lab. These were a result from both human error and device error. Human errors include error in the preparation for the test. Specific tests that were shown to be positive should have displayed a negative result. This could be as a result from mixing up the samples. In the case for our group, the negative control (in other words, the sample that we knew should have displayed a negative result for the disease) displayed a positive result for SNP. The mix up in the samples probably occurred before we actually deceived the samples. This error could have also been a result of improper disposal of pipet tips before and after each use during preparation, thus causing a mix of samples. Device error includes not running the same amount of cycle in the open PCR machine. Each group's machine ran a different amount of cycles and some machines simply did not work at all. Receiving a positive result for our patient sample when it should have been negative left error in the Bayes values. Calculation 3: This shows how accurate the PCR machine is to predict the disease in a patient that would develop the disease. The test was not accurate given that it had a 43% efficiency rate. Calculation 4:This shows how accurate the PCR machine is to predict that a patient would not develop the disease. The test was even mess reliable than calculation 3 because it only showed a 27% efficiency rate. Computer-Aided Design TinkerCAD The TinkerCAD tool is used just like the software SolidWorks, it is there to create 2-dimnsional or 3-dimensional figures online. TinkerCAD was used to make our 3-dimensional PCR. First we made all of the different pieces of the PCR separately and it was 2-dimensional. Then after all the separate pieces were made, it was put all together to make the final, 3-dimensional, product. Our Design Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter