BME100 s2018:Group7 W0800 L6

BME 100 Spring 2018	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: Hannah Bunda Name: Angelica Injejikian Name: Kendall Saville Our Brand Name LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System BME 100 tested patients for the disease-associated SNP by first dividing app all the work. The class was split up into 11 teams of 6. Since there was 22 patients, each team received the DNA of two patients. Each team would individually conduct the PCR and thus have their own positive and negative controls. This helped prevent error because each of the controls was individualized to how they conducted the PCR. Depending on what phone a person has and how the experiment was set up, their photos could be slightly different. Therefore, because each individual uses the ImageJ calibration controls to calibrate everything, the differences between lab groups don't matter. Furthermore there were 3 replicates per patient and three photos were taken for each which further prevented error. By having a lot of data, in this case photos, the data will be more accurate. After everyone conducted the PCR and analyzed all their images, everyone's data was collected on an excel sheet. There was no data for Group 9, most likely because they didn't submit their data. There were a total of 5 positive conclusions and out of those, only 3 were correct which means they were successful. There was a total of 14 negative conclusions and 12 of them were correct. Additionally, one of the tests from Group 7 was found to be inconclusive. What Bayes Statistics Imply about This Diagnostic Approach The results for calculation 1 and 2 imply that the individual PCR replicates for concluding whether a person has the disease SNP or not are reliable. Both the chances that a positive PCR reaction means the patient is positive and the chances that a negative PCR reaction means the patient is negative are about the same. They are both about 90% accurate. The results for calculations 2 and 4 imply that the PCR for predicting the development of the disease are semi-reliable. The chances that a negative test result will mean that the patient will not develop the disease is more reliable than the chances that a positive test results will mean that the patient will develop the disease. The chances that a negative test accurately predicts the development of the disease is about 80% while for a positive test it is about 70%. There are various sources of error that could have occurred. One of them could have taken place when conducting the PCR. Even though extra care was taken to not contaminate the pipette tip, it is still possible that a small amount of contamination could have occurred. Someone could have easily accidentally touched the tip to something before using it without realizing. Another possible source of error could have been when taking the photo. It was difficult to adjust the phone so that it was exactly where it needed to be. As a result, the some of the photos taken wouldn't have been the best and could be blurry and slightly inaccurate. Finally, another source of error could have been when analyzing the photos on Image J. There were so many different photos and even though they were labeled, because there were so many, whoever was analyzed them could've accidentally used the wrong one at times. All of these could have affected the Bayes values in a negative way because the data collected wouldn't have been as accurate. Intro to Computer-Aided Design 3D Modeling Our team used Solidworks when creating our new device design. A member of our group has used Solidworks multiple times before, so the construction of our device wasn't as challenging as expected. First, we designed the new holder that the phone will sit in. Then, we attached an actual phone to the stand so that the purpose of the device would be easier to see. The clamp was made, which holds the phone to the stand in an upright position, and then it was attached to the stand and phone in Solidworks. The adjustment portion of the new design was made in the application along with a simple design of the actual fluorimeter machine so that the user could see how the stand would interact with the actual device. Finally, the controller that will let the user take photos without opening the box was created in the application. Overall, our new design is an improvement over the previous one because it addresses the major problems involved with the use of the fluorimeter: the stability of the phone for accuracy of pictures and the actual picture-taking being so time consuming. Our Design In our design, there is an adjustable stand which holds the phone and allows you to move it accordingly to make it stable and perfectly inline with the slide. It also includes a bluetooth compatible button that will take pictures from outside of the box. We chose to design the fluorimeter this way because it addresses the major problems of the original design. It removes the problem of having to raise the stand with random objects to get it close to the right height to take photos. There is no longer a need to measure the distance of the phone from the fluorimeter because the stand has a ruler on the side and is stable so it won't move during the procedure. The bluetooth button is more efficient that the original design because it allows the user to take multiple pictures at once without having to go inside of the box and risk moving a portion of the apparatus, which would disrupt the accuracy of the data. Overall, our design is much more user-friendly and reliable than the original design because it removes the major weaknesses that it had. Feature 1: Consumables In our package, consumables that will be included are: PCR mix, primer solution, SYBR Green solution, buffer, and glass slides. The user will have to provide their own micropipette, pipette tips, and plastic tubes (which will most likely be available to them already if they are conducting this experiment in a lab or academic setting). Feature 2: Hardware - PCR Machine & Fluorimeter In our design, the PCR machine will be excluded, assuming that the user has already performed the experiment and just needs the fluorimeter to see if their experiment was successful and to get data from it. In other words, our packaging will include the consumables listed above, and the fluorimeter machine with the stand attached and box to cover the system when taking pictures. One of the major weaknesses of the fluorimeter was that the stand for the phone was very hard to use, it had to be raised with random objects underneath and was difficult to get the phone stable for the pictures to be taken at the same angle each time. Another major weakness in the design was having to use a timer to take the photos and open the box between each picture when going to take a new one (very time consuming and could effect the position of the phone and the data retrieved). In our design, we have attached the phone stand to the fluorimeter to make it more sturdy. The phone stand will be adjustable forward and backwards (towards the fluorimeter) and also up and down to be compatible with any size phone (this will allow the phone camera to be lined up perfectly with the droplet on the slide). The new design component involved with the picture-taking portion of the problem assessment will connect your phone to a hand-held button outside of the box via bluetooth, this will remove the problem of having to use a timer to take photos and risking moving the angle of the phone when tapping the picture button. This will allow the user to take all three photos within seconds and ensure the phone angle was the same each time, making the overall data more reliable in the end. Overall, our design will make the use of the fluorimeter much easier and more accurate, which will positively effect the reliability of the data.