BME100 f2018:Group12 T1030 L6

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OUR COMPANY

Kami Leka
Role(s)
Omar Rivera
Role(s)
Michelle Sorsher
Role(s)
Cassidy Michaels
Role(s)
Ian Salgado
Role(s)

Our Brand Name

LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

Throughout the course of the PCR lab, there were 17 teams and each has approximately four to five students. There was a total of 34 patients and three tests were run for each. The results were analyzed and each group came up with either a positive or negative conclusion/diagnosis for each patient. To prevent error, three tests were run for each patient. Running multiple tests prevents errors because it provides data for comparison. It also helps get a more accurate result after averaging the three results. Also, if there is an error in one trial, then there are two other tests to base results off of. Another precaution that was taken to prevent error was there were positive and negative controls. The positive and negative controls give baseline data to compare the results with. This is useful in detecting any errors in the experiment. Overall, the results were mixed. There were a few inconclusive results. In addition, there were some incongruences where the conclusion would be positive, but the disease was not detected.

What Bayes Statistics Imply about This Diagnostic Approach

The result of calculation 1 being close to 1 mean that the individual PCR replicates are very reliable for concluding when a person has the disease SNP. The result of calculation 2 also bing very close to 1 means that the individual PCR replicates are very good at telling when the individual has the healthy SNP. The two calculations combined being both very close to 1 makes it very reasonable to conclude that the individual PCR replicates are very reliable at determining whether or not the individual has the disease SNP.



The result of calculation 3 was very small, about a third. However the result of calculation 4 was close to one. Calculation 3 ws measuring the probability of developing the disease given a positive final test, so having a small result means that a positive final test does not mean that developing the disease is a certainty. The fourth calculation is the probability of not getting the disease based on a negative final test. Because the result of the fourth calculation was very high it is likely that the test is very good at predicting when a patient will not develop the disease. Putting both calculation 3 and 4 together mean the test gives out very few false negatives, but it gives a higher percentage of false positives.



One possible source of both human and machine error that could have occured during the PCR steps could have occured during the micropipetting. It is possible that the whole 50mL did not make it to the test tubes as air could have been sucked up instead of liquid. Another possible source of error would be if we left the SYBR Green 1 out in the light for too long causing it to start being photobleached before we finished all our trials. This would decrease the fluorescence of the droplets. A third possible source of error could be that we didn’t line up the laser with the droplet completely correctly for some of the trials. This would cause the SYBR Green 1 to not be activated and the drops would not glow no matter what the SNP there was.

Intro to Computer-Aided Design

3D Modeling
Our team used TinkerCAD to render a model of our new fluorimeter stand design. We had a positive experience with this software especially compared to how intensive and stressful Solidworks can be. The feature allowing us to look up and incorporate past 3D designs made building our model much easier as we simply had to edit pre-existing designs rather than starting from scratch. In addition, the software made it very simple and easy to export our model as a .stl file for potential 3D printing. The ease with which it could be exported for 3D printing could be very useful for in-person pitches where a solid model could be helpful for demonstrations. All in all, TinkerCAD proved simpler and faster for this creating this model than Solidworks would have been.


Our Design


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Our design changed some aspects of the fluorimeter to try to lessen the amount of error when using it. The new design includes a camera stand that is linked to the fluorimeter so that the distance between the two will remain consistent throughout experiments. The new design also makes the height of the fluorimeter adjustable so that random objects will not be needed to match the height if the camera. Finally the last change to the old design was to change the light switch into a button so that activating it will not make the fluorimeter shift and move.



Feature 1: Consumables

Feature 2: Hardware - PCR Machine & Fluorimeter