BME100 f2017:Group12 W0800 L6

BME 100 Fall 2017	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: Amy Nystrom Name: Priscilla Han Name: Vishnu Karthigeyan Name: Kira Styrker Name: Jason Zhang Company Name: Whole in the Box LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System This semester our BME 100 lab tested patients for a disease-associated SNP. We had a total of 15 lab groups that tested 2 patients each for a total of 30 patients. The experiment has multiple facets but our group divided the labor into two parts; half of our group obtained images of our fluorimeter results and the other half did ImageJ analysis of aforementioned images. The experiment revolved around using ImageJ to quantify the amount of fluorescence from each sample’s fluorimeter results. The amount of fluorescence would guide our decision on whether the sample had the disease-associated SNP. The design of the experiment in addition to the our division of labor both contributed to our error prevention. We had multiple trained members conduct each part of the experiment to create a system of checks. There were three samples tested of each patient’s DNA, which resolved contradictions and struck a balance between reliability and speed. In addition, there were both a negative and positive control for comparison. Finally, there was 3 pictures taken for analysis of each fluorimeter result. Having multiple trials and comparisons at each step decreased the chance of a confounding variable skewing results and increased our accuracy. A practical benefit of our experimental design came through ease in conducting the experiment. We had multiple ears verifying instruction from TA, professors, and the workbook. In addition, we had additional resources for checking parts of the experiment we thought were suspicious or coming to a decision on unexpected occurrences that we didn’t have to divert from the individual conducting the experiment, cutting down on distraction and possible error. The high standard was reflected in the experimental data. The class came to a conclusion on 26 of the 30 patient results. Of the 4 results that were inconclusive, half were as a result of lack of submission of data rather than issues with the experiment itself. Overall, the original system had specificities above 80%. CALCULATION 1: Probability Patient gets Positive Final Test Conclusion given a Positive PCR Reaction VARIABLE DESCRIPTION NUMERICAL VALUE A Positive PCR Reaction 0.273 B Positive Final Test Conclusion 0.25 P(B/A) Total Positive Final Test Conclusion that show up with Positive PCR 0.79 P(A/B) Total Positive PCR that shows up with Positive Final Test Conclusion 0.869 CALCULATION 2: Probability Patient gets Negative Final Test Conclusion given a Negative PCR Reaction VARIABLE DESCRIPTION NUMERICAL VALUE A Negative PCR Reactions 0.631 B Negative Final Test conclusion 0.678 P(B/A) Total Negative Final Test Conclusion that shows up with Negative PCR reaction 0.973 P(A/B) Total Negative PCR Reaction that show up with Negative Final Test Conclusion 0.906 CALCULATION 3: Probability Patient will Develop Disease Given a Positive Final Test Conclusion VARIABLE DESCRIPTION NUMERICAL VALUE A Positive Final Test Conclusion 0.25 B Positive Diagnostic 0.25 P(B/A) Total Yes Diagnosis that Shows Up with Positive Final Test Conclusion 0.571 P(A/B) Total Positive Final Test Conclusion that Shows Up with Yes Diagnosis 0.571 CALCULATION 4: Probability Patient will not Develop Disease given a Negative Final Test Conclusion VARIABLE DESCRIPTION NUMERICAL VALUE A Negative Final Test Conclusion 0.678 B Negative Diagnostic 0.75 P(B/A) Total No Diagnosis that Shows Up with Negative Final Test Conclusion 0.931 P(A/B) Total Negative Final Test Conclusion with No Diagnosis 0.842 What Bayes Statistics Imply about This Diagnostic Approach The results for calculations 1 and 2 suggest that using this diagnostic approach is reliable in reaching a conclusion on the presence of the disease SNP. These two calculations reflect the system’s reliability in detecting the disease SNP. Since calculation 1 is for positive conclusions, this calculation reflects detection rate of the disease SNP if it's there and calculation 2 is for negative conclusions and reflects the accurately not detecting the disease SNP if it's not there. Both calculations are around 90% leading us to conclude this diagnostic approach is reliable and our system is slightly better at detecting the presence of disease SNPs rather than the lack thereof. The results for calculations 3 and 4 suggest that using this diagnostic approach is fairly reliable in predicting disease outbreak based on the test’s conclusion. These two calculations reflect the system’s reliability in predicting the disease. Since calculation 3 is for positive conclusions, this calculation reflects disease rate if the disease SNP is present and calculation 2 is for negative conclusions and reflects health rate if the disease SNP is not present. Calculation 3 is slightly above 50%, while calculation 4 is around 90% leading us to conclude this diagnostic approach is fairly reliable and our system gives more false negatives than false positives. Some possible errors that could have affected our Bayes values negatively include imprecise equipment, changing environmental conditions, and nonstandardized analysis. The class used the cameras available to their group, so phone cameras were used with a wide range of models and sensitivities. A standardized camera with a high resolution would do much for increasing the precision of our results and possibly the accuracy. Each group learned how to do ImageJ analysis from the workbook. However, there may have been a difference from group to group in physical judgment since observation with the naked eye is subjective and prone to human error. Lastly, we all conducted our experiments in different parts of the room that was only semi-evenly lighted with different standards for camera and fluorimeter placement, so that could have skewed results as well. Of course, these three errors all compounded each other which exacerbated the problem. Intro to Computer-Aided Design 3D Modeling Our team used SolidWorks to model our improved fluorimeter in three dimensions. SolidWorks is a product prototyping and modeling CAD software (Computer-Aided Design), created by Dassault Systems and available to ASU students online through ASU's applications permits. The program is used mainly for engineering purposes, and in this lab it was used by our group to model our improved fluorimeter in 3D, and for us to contemplate what improvements would be feasible for the product by seeing them on the model. The operation of the program was relatively easy when the person using the program was familiar with it, so our process of adding a hinge, a hole for a cell phone camera, and an adjustable shelf for the cell phone was simple when preformed by an experienced group member. In the end, our team's experience with the SolidWorks computer-aided design program was simple and easy to execute once we had decided on a mutual vision for the improvements we intended to add to our fluorimeter. As seen in the completed image below, we were able to model all components of the improved fluorimeter that we hoped to add to the design. Our Design For our improved design, we decided to focus on the fluorimeter stage of the PCR analysis process. In order to improve the current fluorimeter used in the lab, we added the following components- A hinged front panel for easy access to the inside of the fluorimeter. An opening in the front panel through which a cell phone camera can capture the fluorimeter photo. An adjustable shelf to set the cell phone on, to allow for the different types of cell phones that could be used. This design improves upon the current one by limiting light within the fluorimeter (by having the fully close-able front flap with the access opening for photos), and by making the cell phone photo process easier by providing a shelf for stability of the camera. This adjustable shelf (for use with different sized phones) also improves fluorimeter quality by making repeated photos in the same place possible. We chose these design improvements because these problems were the ones mainly encountered by our team when going through the OpenPCR and fluorimeter process. Feature 1: Consumables Our PCR kit would contain the typically consumables required for conducting a PCR. This includes micropipette and micropipette tips, colored PCR tubes, glass slides, a buffer solution, SYBR Green solution, and gloves. Micropipette and Micropipette tips A micropipette is the instrument used to measure and administer compact incremental samples in a controlled manner. The micropipette tip is the disposable part of a micropipette that contains the sample; each tip is designed for only one sample use to avoid contamination of the samples. There are different sized tips for different desired measurements. The device holds the sample while it is being transported to its desired destination. The sample can then be injected into or onto its desired location. PCR tubes PCR tubes are comparable to small test tubes. They also have a lid so the test tube can be sealed. PCR tubes can hold small samples and are opening of the tubes are wide enough to allow micropipette tips to inject samples into the tubes. Our kit would provide a selection of colored PCR tubes; in this way, the consumer will be able to use color-coding to identify their samples. Microscope slides Glass sides were inserted into the fluorometer and a light was shined though the sample. The sample reflected green to show the levels of deoxyribonucleic acid. Buffer A buffer is a liquid substance. The purpose of the buffer used in this experiment is to create the preferred conditions for TAQ DNA polymerase. SYBR Green Solution SYBR Green solution is used as a marker to identify the target genetic material. Under fluorescent light, the SYBR Green solution causes positive results to appear green. Our kit will provide SYBR Green solution in a vial that is impenetrable to light. Gloves The purpose of gloves is to protect hands from the samples and to protect the samples from the bacteria that is on hands. Gloves are designed to fit on a hand and come in different sizes the gloves were provided in latex and nonlatex gloves. Feature 2: Hardware - PCR Machine & Fluorimeter The PCR machine provided by ASU was used in lab to amplify the DNA samples that we had. PCR tubes containing our samples were placed in the machine and the top lid containing a hot plate was closed. The machine was then programmed so that the temperatures changed at the correct times and the cycle repeated 25 times in total. This process allowed the simulation of the environment required for DNA replication. At the end, our PCR tubes contained a large concentration of DNA and we had to dilute it for the next part. We then made use of the Fluorimeter system provided by ASU to analyze our samples along with a positive and negative control. The Fluorimeter system was simply comprised of a black box with a front flap opening and box to put our glass slides on. We systematically added a droplet of a sample and a droplet of dye such that they would converge to form one bubble on the glass slide. The glass slide box was then placed inside the black box so that the excess lighting was removed. We used a smartphone to take a photo of the combined droplet while it was inside the black box and then transported that photo to a computer. We then used ImageJ to analyze the droplet and graph our results. Lastly, we drew conclusions from the data using Bayesian statistics. The strengths of the PCR machine include that it is relatively fast and it accurately produced our samples. We did not redesign any features in the PCR machine. However, the fluorimeter system had some design flaws. We observed that it was tedious to constantly adjust the position of the phone to take photos and there was also some light that still reached the slides. To fix these issues we designed a black box that had a premade hole where the phone would fit and adjustable positions for smaller or bigger phones. This made it so that the phone did not have to be moved around constantly and less light would reach the slide since the opening in the box is smaller. Thus we made use of the same overall process as before to amplify and analyze our samples but we used a modified version of the black box to make the process more accurate, efficient, and accessible.