BME100 f2017:Group5 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: Anna Role(s): Team member Name: Anu Pal Role(s): Team member Name: Joey Gurule Role(s): Team member Name: Savina Plougmann Role(s): Team member Lytebox LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System As a class, we divided the patients between 15 groups, 30 patients total, 2 patients each. We then performed a 3 identical PCRs per patient, 6 per group, by adding the sample DNA with 50μL PCR reaction mix, containing reagents and DNA polymerase, to a total volume of 100μL and running it in a thermocycler. Once we ran the samples through the thermocycler, we added 50μL of buffer to dilute the DNA for each sample, then placed 80μL of the diluted DNA on the middle row of the slide, and right behind it, 80μL of SYBR green 1 solution to merge the drops. We then turned on a blue light, took a picture of the drop in the darkness and repeated these steps for each of the diluted samples of DNA. We had a positive control that contained a template DNA strand with the known SNP, and a negative control that did not contain the known SNP, so the primers would not properly bind. We also had positive and negative controls for our fluorimeter, and used bovine serum albumin concentrations to construct our calibration curve. We took three pictures of each droplet in order to get more reliable results, from averaging. We used ImageJ to analyze our PCR results, which we found by combining a droplet of PCR product with a droplet of Syber green on a fluorimeter, and taking a picture with our phones. Our data analysis went awry, and we had a negatively-sloped calibration curve, so we determined our positive and negative results from the apparent green color observable with the naked eye. When the whole class pooled their data, one group did not submit final conclusions, so we omitted their data when calculating statistics. Each group submitted their positive and negative conclusions for each trial, and their final positive and negative determinations. What Bayes Statistics Imply about This Diagnostic Approach The probability that a patient will get a final positive test conclusion given a positive PCR result is about 80%, so relatively high, but not definitive. The probability that a patient will get a final negative test conclusion given a negative PCR result is very high--about 97%, so we are fairly confident in this assumption. The probability that a patient will develop the disease given a final positive test result is about 65%, so the sensitivity of this test is very low. The probability that a patient will not develop the disease given a final negative test result is about 90%, so our specificity is much higher than our sensitivity, but still not conclusive. One possibility is a failure in primer design, different thermocycler parameters then what normal are, or nonspecific binding to other template sequences. A possibility is that the pictures we took of the reaction could have been blurry, or out of focus in which case the measurements that were given to us from imageJ would be skewed. The micropipetting method could have been incorrect allowing for excess liquid or not enough liquid to fill the micropipette, which in turn would skew the data by filling the test tube with an incorrect amount of substance. Steps that could have affected the Bayes Values in a negative way: Bayes Values can be affected in a negative way by if our test was not sensitive enough and we could not see the green that would indicate if our sample was positive or negative. For example, our first patient we diagnosed as positive, but the doctors diagnosed it as negative. Intro to Computer-Aided Design 3D Modeling We used SolidWorks to design our Lytebox which is our twist on a current Fluorimeter. Joey designed the Sample Illuminator. This will hold the slide and have many lights on the side to illuminate the droplets for the pictures. Anna designed the box with cameras. This box will house the slide and the Sample Illuminator, along with taking pictures of the droplets and sending it to your email. Anu designed the slide for the droplets to be pipetted on. The team’s experience with SolidWorks was marvelous, everything went smoothly and nobody had a problem working on SolidWorks. Our Design Underside of the box with cameras: Side view of box with cameras: Light stand with slide assembly: Our design is 2 inches tall, by 4 inches long, by 12 inches wide. The slide itself is 2 inches wide and 10 inches long. The Sample Illuminator is 3 inches wide, by 10.5 inches long, by 2 inches tall. We chose this design because it we wanted to keep it similar to the original Fluorimeter to so it is familiar to the user; however, we wanted to improve upon some of the weaknesses we addressed. It is different from the original design because it is longer than the original Fluorimeter and instead of having a flap on the box, ours is a cover that you put over eliminating the chance for light to penetrate and cause an error in the picture. Feature 1: Consumables The only consumable in our product is the slide. After one use, the slide must be cleaned our replaced with a new one. Feature 2: Hardware - PCR Machine & Fluorimeter Our design will use fluorimeter technology, not PCR technology because our product is only designed to improve fluorimetry. We kept the technology in fluorimetry the same, only utilizing a more powerful camera and a box less prone to light contamination. The light attached with the camera will move between loaded sample drops, so the user will not have to load the box for each sample, but will rather load multiple sample and receive the pictures for all loaded samples. All samples will still be loaded the same way, with a drop mixture of SYBR green and DNA loaded into the wells of the glass slide.