BME100 s2015:Group2 12pmL6
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LAB 6 WRITE-UP
Overview of the Original Diagnosis System
The division of labor consisted of 34 teams of 5 or 6 students who diagnosed a total of 68 patients. This allowed for the class to test a massive amount of patients in an efficient amount of time. 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. Each individual group ran its own positive and negative control when completing their individual PCR tests. The positive control would fluoresce if it contained DNA, while the negative control would do nothing because of its lack of 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. This ensured the students accurately were able to pipette the correct amounts of mixes when using PCR so that the open PCR would work properly. Error prevention was also found in the fact that each group analyzed three samples from the same patient. This means there were 6 PCR tubes for only two patients (8 total when the positive and negative controls were included). This allowed for multiple trials to ensure that if one went awry, it wouldn't throw off all the data.
After the PCR was complete, the fluorimeter was used in conjunction with SYBR Green I dye to test each sample of the PCR. In the fluorimetry, photos of the samples were taken and then 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. For ImageJ, an error prevention was put on in the fact that the Image J had to be calibrated based upon the positive and negative controls for each group. This means that each group is using its own standard which was created in their PCR machine. This ensures that the results will be accurate. 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.
The results for calculations 1 where variable A meant there was a positive test conclusion while variable B showed the positive PCR reaction are as follows. Both individually showed the number of patients diagnosed and tested for both produced values that indicated that Variable A and B correlate through the experiments. Now taking the Bayes values from both variables, 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, 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 resulted in a higher frequency than the probability of B given A (value even closer to one). 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.
The results for calculation 2 where Variable A meant there was a negative test conclusion while variable B showed the negative PCR reaction are as follows. Both variable A and B showed the number of patients diagnosed and tested negative produced Bayes values that indicated both negative test results correlated with each other through the experiment. Taking the frequency from both variables, where the probability of having a negative PCR reaction given negative test conclusions shows that both variables correlate with each other having a probability very close to one. Inversely, the probability of a negative PCR result given the conclusion being negative gives a probability that is close to one but yet not as high as the first. In conclusion, a negative Final Test Conclusion and Negative PCR reaction do, in fact, correlate with one another.
The results for calculation 3 where Variable A meant the total patient that will develop disease while Variable B shows the positive test conclusion are as follows. Now the probability of variable B given A, Frequency of positive test conclusion given patient that will develop disease, gives a value close to one meaning that the number of patients that do test positive will have a high probability of developing the disease. Now, the probability of variable A given B, total patients that will develop the disease given the probability of positive final test conclusions, gave a value that is close to one yet was farther away than the rest of the values which were close to one meaning that the patient that will develop the disease later on will test positive during the final test but not necessarily have completely conclusive results. Since the value is not as close to 1.00 as previous values, there were likely some errors in the value or in the actual experiment.
The results for calculation 4 where Variable A meant the total patients that will not develop the disease while variable B shows the total negative test conclusions are as follows. Taking the frequency for both values where the probability of variable B given A, negative Test Conclusion given Total Patient that will not develop disease, gives a Bayes value close to 1.00 which shows that yes, patients that test negative will be likely to not develop the disease. Now the probability of variable A given B, total patients that will not develop the disease given the probability of the negative final test conclusion, gives a frequency that is close to one. Meaning that the majority of the total patients that do not develop the disease will be more likely to have a negative final test conclusion though not completely conclusive due to the fact that the value had some slight deviation from being exactly at one.
Due to the fact that the Bayesian statistics were based entirely off of data collected from the experiments of many different groups, there is some likelihood of error. Because each group likely had some slight variation in the way the experiment was carried out, the test conclusions and PCR results may have some discrepancies from being entirely accurate. There was a likelihood that some groups experienced light pollution when taking the picture for the ImageJ analysis of the PCR results. This could have caused PCR results to have skewed results due to the tampering of the ImageJ analysis. Another factor is that some groups data was recorded as void because they had some unknown error which rendered their PCR results non-useful to the class (Dr. Haynes identified these groups and instructed the class not to record these results in the Bayesian analysis). This means that not everyone who participated was able to contribute results which threw off the experiment as whole. Lastly, a very likely cause for error was due to some PCR reactions not being carried out properly. If some group did not place the right mixture of contents into the PCR tubes, the reaction could not occur and groups would experience odd results. This was seen by the fact that some groups had PCR results where one of three samples yielded a different result from the other two and left the diagnosis somewhat ambiguous. These factors could throw off the Bayesian statistics. These factors are the errors which will effect the overall statistics. Other errors which could arise which would influence data include outside "noise" and human error during fluorometer analysis. The outside noise is outside data including-dirt, virus' or any other influence in the DNA which could effect the data and make it difficult to analyze the DNA. During the flurometer process, a picture was taken inside of a black box to keep out light, however this had to be done quickly, the button pressed and the door immediately shut. During this time small amounts of light could still go into the box and effect how the droplet looks during analysis. The analysis itself was done by hand using imageJ, and if the part of the photo selected to be analyzed was not completely on the right spot, or included the 'noise' discussed above, it would influence how the data was interpreted and throw off overall results.
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. In order to do this, the group assembled the outside of the Open PCR machine and increased the length and width so that the dimension created a doubling of the inside area. This doubling in area means that double the amount of test tubes (and double the amount of samples) will be able to be tested.
Feature 1: Consumables Kit
The consumables that will be packaged in the kit will be:
A major problem with the consumables typically used used in a PCR experiment is that there is a large amount of plastic which is disposed off (as seen in the pipette tips and micro-tubes). 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 be one which will not absorb the sample or liquid that is being picked up. This will result in later samples being uncontaminated when the materials are cleaned using soap and water. With more durable and eco-friendly materials, these plastic tubes and pipette tips 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.
Feature 2: Hardware - PCR Machine & Fluorimeter
The PCR machine is used to replicate the DNA multiple times, the machine in this system can be redesigned to address multiple problems. The PCR machine only allows for 16 micro-tubes to be replicated during each use, each use also takes 2 hours to process through and must be connected to a computer the entire time. These requirements waste time for the user and serves to be incredibly inconvenient. The PCR machine can be redesigned to better adapt to the users needs by allowing for more samples in each run, and by making each of those runs faster and not needing a computer connection. The wireless connection can also be setup in such a way that there could be an app designed so that the user would be able to receive real-time notifications regarding the status of their PCR. This would allow for updates instead of manual checking. Also, if the PCR machine was to be enlarged, it would allow for more samples to be included in the 2 hour run. This change will allow for the user to use less time to replicate a certain quantity of samples, and allow for less PCR machines to be needed for that quantity of samples. This would prove much more efficient for the user and would allow for less time wasted in having to run more than one PCR test due to a lack of sample size. Making the replication time faster could be done by increasing the components which cool and heat the samples, so it takes less time to cool or heat meaning the reaction can be done faster. Even if the PCR machine continues to take 2 hours to run, it would be more convenient to the user if it did not need to be hooked up to a computer. The PCR machine's requirement to be connected to a computer the entire 2 hours, means the user cannot use their computer during the run, which is inconvenient for them. It would be beneficial if the PCR machine had an internal storage device for all the information to be stored, and later transferred to a computer using a USB device. All of these changes would allow for a smoother run, which has the least negative impact on the user.
The new device designed looked at the PCR machine and addressed the issues of quantity of samples analyzed at once. The current design only accommodates 16 micro-tube samples at a single time. This means that the user had to either run the machine multiple times or buy multiple machines if they wanted to be able to analyze more than 16 samples. The new design is clean and cost effective, by simply enlarging the dimensions of the PCR machine, a larger area for samples is allowed. This means the PCR machine will be able to accommodate more samples, and allow for a faster rate of PCR replication based upon the fact that more samples doen in the same amount of time equals a faster sampling rate. The design the group created has dimensions which enlarge the base in such a manner as to double the internal area of the PCR machine. Being twice as big, it will allow for twice as many samples, meaning 32 samples will be able to be accommodated for replication with each run of the PCR machine. The significance of the new design is that it allows for the user to replicate more DNA in a single run, so that with one machine they, can produce what two machines (or two runs) used to. Now, the user will only have to do one run for 32 samples, and will have to do less total runs, or use less total machines. This is cost and time effective for the user.