BME100 f2016:Group13 W8AM L6

BME 100 Fall 2016	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: Christian Sulit Name: Scott Colebeck Name: Valerie Cortez Name: Samson Nguyen Name: Mingdi Lu Arrow Dynamics---Where Accuracy is Everything LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System (A) In order to test patients for the SNP associated with disease, 17 teams of 6 BME 100 students each diagnosed 2 individual patients for a total of 34 patients. (B) Multiple precautions were taken to minimize error. Every student was provided with lab coats and gloves in order to maintain the sterility of the lab. Students were also given appropriate training and online resources for handling lab equipment, and were also instructed to clearly label each of their test tubes in order to avoid mistakes, such as cross-contamination, and maximize the accuracy of their results. During the PCR replication process, students performed three replications on each patient to increase sample size and reduce error. The positive and negative control samples acted as points of comparison which would later help in interpreting the final results of the class. When measuring the fluorescence of each of the DNA samples, three pictures were taken of each sample, which were then analyzed using ImageJ. The mean and standard deviation of each sample’s images were determined in order to increase accuracy. Additionally, students used a series of ascending concentrations of calf thymus DNA samples to create a comparative graph which could be compared with patient DNA measurements. (C) After all calculations were completed, the entire class’s results were compiled into a spreadsheet and analyzed. Two groups’ results were deemed inconclusive and omitted from the class results because they did not submit their final results. From our data, it was concluded that the first patient (11385) tested negative for SNP while the second patient (54376) tested positive for SNP. One of the challenges we encountered during the lab was using ImageJ properly. Because we were relatively unfamiliar with the software and analytical techniques at the time, it was difficult to determine whether the results we were obtaining were accurate or not. Some of our numbers seemed much larger than they should have been, which made it necessary for us to compare results with other teams to make sure our numbers landed within a reasonable range. What Bayes Statistics Imply about This Diagnostic Approach Calculations 1 and 2 serve to determine the reliability of the PCR and fluorimetry method in diagnosing for an SNP gene. Bayesian statistical analysis, in these two scenarios, is used to determine the probability that a patient will receive a positive conclusion, given that his or her DNA test for an SNP is positive, and the probability that a patient will receive a negative conclusion, given that his or her DNA test for an SNP is negative. The probabilities determined by both of these calculations was very close to 1.00 (100%), which implies that using PCR and fluorescence measurements is very reliable in determining whether a patient will receive a positive test conclusion given a positive diagnostic signal, and vice versa. Calculations 3 and 4 serve to determine the reliability of using PCR results to actually detect the development of an SNP-related disease. In these calculations, Bayesian analysis is used to determine the probability that a patient will develop the disease if he or she has a positive PCR conclusion, and the probability that a patient will not develop the disease if he or she has a negative PCR conclusion. The probabilities determined by these calculations were relatively low when compared with calculations 1 and 2, rounding up to about 0.7 (70%) for both calculations. This implies that the use of PCR and fluorimetry is only semi-reliable when tasked with determining whether a patient with a positive test conclusion will actually develop the disease, and vice versa. Sources of Error One possible human error that could greatly distort the Bayes values of the experiment would be the use of contaminated pipette tips to transfer samples from one microtube to another. This mistake would result in the mixing of each of the samples and would most likely skew the results of a group’s PCR reactions. A mechanical error that could negatively affect Bayes values is the stand used to orient the phone for photo-capturing. Our group found that it was difficult to properly keep the phone upright and stable at an angle suited for taking pictures of the DNA samples. If the pictures are analyzed in ImageJ but were taken from an awkward angle, the results of one’s measurements may end up skewed. A final mechanical error is the limitation of phone camera’s focal length when taking pictures of the DNA samples. Our group found it difficult to manually focus the camera on the drop we were placing on the slide, which is likely because of the short distance between the phone and the fluorimeter. Although we were able to obtain clear images from our experiment, blurry images caused by this mechanical error could affect a group’s ImageJ calculations and therefore negatively affect the Bayes values. Intro to Computer-Aided Design 3D Modeling As a team we used Tinkercad to design our OpenPCR machine. Overall our experience was very positive in that the program itself was very intuitive and easy to use. At any point that we may have had a question on something there were tutorials along the way to help. The prefabricated designs provided by the professor also allowed for the whole design process to flow very smooth. One of the most important things that we as a team came to understand was simply how much goes into designing a machine such as this. Overall it was a very positive experience for us as a team to see and understand the work and thought that is needed to render our designs. Our Design Arrow Dynamics OpenPCR Machine The design that we chose is ultimately the same structure as the original OpenPCR, however it will be smaller and incorporate pre-programmed settings to ensure accuracy of the PCR reaction. Feature 1: Consumables While analyzing the consumables that we used for the lab during our OpenPCR experiment we concluded that while the products were easy to use and effective in conducting our experiment, we felt that the greatest weaknesses were the amount of waste produced and the possible room for error. During the original experiment, we had to pipette the buffer and SYBR Green solution into the individual OpenPCR test tubes. This process required repeated disposal of pipet tubes and the buffer and SYBR Green were both taken from larger tubes. This repeated in and out extraction left the experiment open to the possibility of cross-contamination. As a team, we felt that above all it is important to offer a patient with the most accurate results possible. Our solution to eliminate waste and proved the most accurate results possible our new consumable packages will include tubes already filled with the appropriate solutions. During this experiment there many instances that same the solution in the same volume is used so it would be helpful to have this prepackaged and to already have the test tubes marked for the experiment. A cut down on waste will save money making it cheaper for the test to be performed offering help to a wider group of people. Above all else this will help to eliminate the possibility that either the wrong solution or the wrong amount of the solution will be used. Feature 2: Hardware - PCR Machine & Fluorimeter PCR Machine While originally conducting the experiment ourselves we felt that the OpenPCR machine itself was easy to use, however, the programming for the device also allowed a certain room for error if it was not programmed correctly. In order to Our new OpenPCR design will be smaller and a new programming system will be applied to it. The smaller design will help the design and process be easier and quicker. The size will also make the product be more affordable to produce and for the consumer to purchase. The new programming system that will be applied to the new design will be able to eliminate any error in processing. This will ensure that the results are as accurate as possible. Fluorimeter While we were analyzing the OpenPCR machine and the fluorimeter setup we came to the conclusion that it was the fluorimeter setup the posed the biggest issue in the experiment. We chose this design because it provides a stable platform to be able to collect the most accurate data from the experiment. Currently, the fluorimeter and phone setup are incredibly unstable. In order to get the fluorimeter to the appropriate height, you need to stack random object to be level with the camera from your phone. This new design will provide a cheap and easy fix by allowing the user to simply place their phone into the attached phone slot and adjust the fluorimeter platform to be level with the camera. Front; The phone holder tucks into the bottom of the fluoriemter and deploys upwards using a spring mechanism Back; Crank in back is used to raise and lower the platform that holds the fluorimeter