The division of labor consisted of 34 teams of 5 or 6 students who diagnosed a total of 68 patients. Each group ran a PCR test on two patients, and prepared three samples of replicated DNA of each patient along with a primer mix to ensure accuracy. Each of the solutions were compared to both positive and negative controls. The positive control would fluoresce if it contained DNA, while the negative control would do nothing if it did not contain any DNA. If the sample did have DNA, it was compared to the positive control. If the sample did not contain DNA, it was compared to the H20 curve. To avoid error, a practice trial for using a micropipette was conducted so that it could be used with ease when testing with actual samples. After each experiment, photos of the samples were taken and analyzed through the use of ImageJ. ImageJ separated the photo into three different color channels (red, blue, and green), although only the green channel was used. The wavelengths of that green channel were compared to the light of the droplet in the test tube. That drop was analyzed to determine the density and area. There were three images per unique PCR sample, however, only the best picture of each sample was used for the ImageJ calculations. Every group’s final data was then uploaded onto a document to compare the overall results and come to a conclusion. Overall, the class had a successful conclusion. On the other hand, this group’s conclusions were not successful as the data was inconsistent with the results. From the overall results, it can be deduced that the probability of having positive PCR results directly relates to having Coronary Heart Disease.
What Bayes Statistics Imply about This Diagnostic Approach
The results for calculations 1 where variable A meant there was a positive test conclusion while variable B showed the positive PCR reaction. Both individually showed the number of patients diagnosed and tested for both produced values that were less then .500 being relatively close to each other, indicating that Variable A and B correlates through the experiments. Now taking the Bayes values from both variable A for positive test conclusion and variable B for positive PCR reaction where the probability of variable B given A shows that the Bayes value is close to 1.00 or 90%, which means that majority of the patients that did test positive to the PCR reactions did in fact also test positive for cancer. Switching the variables where the probability of Variable A given B where the patients that concluded positive for coronary heart disease given having positive PCR reactions equaled 94%, a higher frequency then the probability of B given A. This concludes that the number of patients that tested positive for either PCR reaction or Positive Coronary heart disease has a high probability of testing positive for the other.
Computer-Aided Design
TinkerCAD
The tinkerCAD tool is an online interface to create 3D objects and devices. This interface can be used to create simple objects or to create intricate designs. During this lab, TinkerCAD was used to create part of the open PCR device container(front-back-bottom and one side). The rest of the PCR machine would be assembled in a likewise fashion; however, the lab TA told the group to only assemble 3 sides for this portion. The TinkerCAD for the basics of the PCR Machine is as follows:
Our Design
The new design was chosen to address the problem regarding amount of samples which can be tested. The original openPCR device only could fit 16 micro-tubes, which only allowed for 16 tests, and in the application of this course, only allowed for two teams per box. This new design will expand the amount of tests per box, allowing for more tests, the new openPCR design has double the area for microtubes, allowing for double the amount of tubes. The new design will accommodate up to 32 micro-tubes.
Feature 1: Consumables Kit
The consumables that will be packaged in the kit will be:
pipetting tips
PCR reagent mixers
test tubes to hold samples
A major problem with the consumables in our kit used in this experiment were the amount of plastic disposed. As engineers, sustainability should always be a top priority when conducting an experiment. As a solution to this problem, the plastic tubes used for micro-pipetting should be produced using more durable material and eco-friendly materials. This material will have to be one where it will not absorb the sample or liquid that it is picking up, so as to not contaminate later samples. With more durable and eco-friendly materials, these plastic tubes can then be sanitized and reused multiple times before disposal, thus lessening the amounts of plastic waste. Also, with eco-friendly material, the decomposition of these tubes will not contribute to rapid increase of plastic pollution in the environment.
BME 100 Spring 2015
