BME100 f2014:Group21 L6: Difference between revisions
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<!-- Instructions: Write a medium-length summary (~10 - 20 sentences) of how BME100 tested patients for the disease-associated SNP. Describe (A) the division of labor (e.g., 34 teams of 6 students each diagnosed 68 patients total...), (B) things that were done to prevent error, such as the number of replicates per patient, PCR controls, ImageJ calibration controls, and the number of drop images that were used for the ImageJ calculations (per unique PCR sample), and (C) the class's final data from the BME100_fa2014_PCRResults spreadsheet (successful conclusions, inconclusive results, blank data). --> | <!-- Instructions: Write a medium-length summary (~10 - 20 sentences) of how BME100 tested patients for the disease-associated SNP. Describe (A) the division of labor (e.g., 34 teams of 6 students each diagnosed 68 patients total...), (B) things that were done to prevent error, such as the number of replicates per patient, PCR controls, ImageJ calibration controls, and the number of drop images that were used for the ImageJ calculations (per unique PCR sample), and (C) the class's final data from the BME100_fa2014_PCRResults spreadsheet (successful conclusions, inconclusive results, blank data). --> | ||
The PCR reactions done in the BME100 lab were designed to mitigate error. Each group tested 2 patients, meaning a total of 68 tests were done (there were 34 groups). Since each patient had their DNA ran three times there was little chance for error (a total of 208 PCRs were run!) Even after this was done there were still things put in place to reduce the risk of error. When it came time to do the image processing there were three ImageJ tests done on each of the DNA samples (3 samples per patient would mean a total of 9 tests were run for one patient) to ensure accuracy in the diagnosis. Not only where the quantity of tests a way to prevent error, but the quality of the ImageJ calibrations served as a check on error. These photos were also taken multiple times to ensure that the images were good enough to use. Since it was so closely calibrated it would be hard to misread the image and give the wrong diagnosis. Since there was so much data in the final spread sheet the Bayesian statistics could be used effectively. A large sample size made determining the actual likelihood of a false positive or negative much easier. | |||
'''What Bayes Statistics Imply about This Diagnostic Approach''' | '''What Bayes Statistics Imply about This Diagnostic Approach''' | ||
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'''Our Design'''<br> | '''Our Design'''<br> | ||
[[Image:PCR_edit.JPG|100px|PCR machine made with TinkerCAD]] | |||
<!-- Instructions: Show an image of your TinkerCAD design here --> | <!-- Instructions: Show an image of your TinkerCAD design here --> |
Revision as of 19:54, 25 November 2014
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
LAB 6 WRITE-UPBayesian StatisticsOverview of the Original Diagnosis System The PCR reactions done in the BME100 lab were designed to mitigate error. Each group tested 2 patients, meaning a total of 68 tests were done (there were 34 groups). Since each patient had their DNA ran three times there was little chance for error (a total of 208 PCRs were run!) Even after this was done there were still things put in place to reduce the risk of error. When it came time to do the image processing there were three ImageJ tests done on each of the DNA samples (3 samples per patient would mean a total of 9 tests were run for one patient) to ensure accuracy in the diagnosis. Not only where the quantity of tests a way to prevent error, but the quality of the ImageJ calibrations served as a check on error. These photos were also taken multiple times to ensure that the images were good enough to use. Since it was so closely calibrated it would be hard to misread the image and give the wrong diagnosis. Since there was so much data in the final spread sheet the Bayesian statistics could be used effectively. A large sample size made determining the actual likelihood of a false positive or negative much easier. What Bayes Statistics Imply about This Diagnostic Approach
Computer-Aided DesignTinkerCAD Using the TinkerCAD ruler tool helped us see how precise designing in a virtual setting can be done efficiently and quickly. The shape tools made it easier to combine small details into one overall product. The shapes also made it easier to put on the computer what you were originally visioning in your mind. The boat that was made was a simple design, but it incorporated many of the features of TinkerCAD. Making the boat in TinkerCAD helped us better design a better version of the PCR machine. Our Design
Feature 1: Consumables KitFeature 2: Hardware - PCR Machine & Fluorimeter |