BME100 f2015:Group1 1030amL6
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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 | ||||||
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OUR COMPANY - "Nappy Happy"
LAB 6 WRITE-UPBayesian StatisticsOverview of the Original Diagnosis System Seventeen teams of six students each diagnosed 34 patients total. Each team diagnosed two patients. There were multiple aspects to the lab that eliminated the error. The first thing was there was three replicates for each patient instead of just one. Then we also created a positive and negative control for the PCR, which we used water and the positive PCR, and water with negative PCR. The imageJ calibration controls were created by making six different concentrations of Calf Thymus DNA and relating those to the average amount of pixels of the drops. We took a total of 18 pictures, three pictures per concentration, for the calibrations. Next we took 24 pictures, three pictures of each the positive and negative controls.Then three pictures of each replicate of the patients. Our class data resulted with 34 total final results, with 2 No Tests, 2 Inconclusive tests,13 positive tests and 19 negative tests. Our group had two conclusive patients, one being positive and one being negative. We encountered a couple of problems throughout this process. When we were making the mixtures of PCR we were lacking some reagents, which made it difficult to replicate data for accuracy. We also encountered a problem figuring out how to do the final calculations, once we figured out what number to use we achieved our final data. What Bayes Statistics Imply about This Diagnostic Approach The results for calculation 1 is about 75% reliable that an individual receives a positive test result given a positive PCR reaction. The result for calculation 2 is close to 100% reliable that an individual receives a negative test result given a negative PCR reaction. The result for calculation 3 has a very low reliability that an individual will develop the disease when they get a positive test result. The result for calculation 4 has close to 100% reliability that an individual will not develop the disease when they get a negative test result. Three possible sources of error: 1) Lack of settings on the error so the image wasn't as clear as it could've possibly been 2) The distance of the camera to the drop could have varied throughout each trial 3) Lack of reagents given to us to create the drops Intro to Computer-Aided DesignTinkerCAD Our Design
Feature 1: ConsumablesConsumables: Glass slides that are 3/4 inch by 3 inches PCR mix Primer solution SYBR Green Solution Buffer solution Standard plastic tubes
Feature 2: Hardware - PCR Machine & FluorimeterOur design utilizes the current PCR machine and a redesigned fluorimeter. A major faw with the current fluorimeter is having to use an external camera and the errors associated with handling and re-positioning the camera between trials. In order to reduce the chance of error from the position of the camera, we added a built-in camera to the existing fluorimeter. As shown in the TinkerCAD drawing above the slide holder slot and light remain unchanged, while an arm is added with a built-in camera positioned at the height of a drop of DNA on a slide and exactly 5.8 cm away from the drop. When the box lid is closed, a signal is sent to the camera and after a short delay 3 pictures are taken. These pictures are immediately uploaded via bluetooth or cable to the computer for image processing. This also eliminates the trouble we had keeping pictures in order and uploading them to process with ImageJ software.
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