BME100 f2017:Group2 W1030 L6

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Kaitlyn Nielsen
Alexis Crego
Krysten Walker
Will Goodall
Ryan Shapiro

Fluoroblast

LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

In BME100 Laboratory, there were 16 teams of 5 students and we tested patients for the disease-associated SNP with a PCR reaction and a fluorimeter. Two members of the group conducted the actual experiment, which involved using a fluorimeter to capture images of drops of the PCR reaction mix combined with SYBR green. The images were then imported onto using Image J. By splitting the channels and analyzing the green channel the amount of fluorescence was detected and measured. From these measurements mean and standard deviation were calculated to draw conclusions about the patients to figure out whether or not they have the disease. Measures taken to prevent error included performing the experiment for three replicates per patient to ensure the image captured was accurate. The PCR controls remained constant because the same amount of liquid (160 micro liters) was in every drop and the group members performing the experiment were careful with the pipetting. There were 14 images used for the ImageJ calculations, so there was only one image per unique PCR sample which could have caused error. The ImageJ calibration controls used to prevent error included splitting the color channels of the images so there were then multiple images to work with (3 per unique PCR sample).


What Bayes Statistics Imply about This Diagnostic Approach


Calculation 1 showed about 79.7% sensitivity in the test. The result for calculation 2 showed 92.3% specificity.


Calc I. Prob of Pos. Conclusion given Pos. PCR

A: 0.531 Pos. conclusion freq.

B: 0.583 Pos. PCR freq.

P(B|A): 0.875 Freq. Pos. PCR given Pos. conclusion

P(A|B): 0.797 Freq. of Pos. conclusion given Pos.PCR


Calc II. Prob Neg. conclusion given Neg. PCR

A: 0.406 Neg. conclusion freq.

B:0.406 Neg PCR freq.

P(B|A): 0.923 Freq. Neg. PCR given Neg. conclusion

P(A|B):0.923 Freq. Neg. conclusion given Neg.PCR


Calculation 3 showed at 57.1% in sensitivity of disease development after positive conclusion. Calculation 4 this number is close to 1 showing high specificity.


Calc III. Probability of developed disease given Pos. Conclusion

A: 0.469 Pos. diagnostic freq.

B: 0.531 Pos. test conclusion freq.

P(B|A): 0.647 Freq. Pos conclusion given Pos. diagnostic

P(A|B): 0.571 Freq Pos. diagnostic given Pos. conclusion


Calc IV. Probability of not developed disease given a Neg. Conclusion

A:0.531 Pos. diagnostic freq.

B: 0.406 Pos. test conclusion freq.

P(B|A): 0.769 Freq. Neg. conclusion given Neg. diagnostic

P(A|B): 1 Freq. Neg. diagnostic given Neg. conclusion


One major error that could have resulted from this experiment is the quality of the pictures that were captured by the smartphone. Some older smartphones don't have extremely high-tech or high pixel cameras so they don't produce clear images all the time. A human error that occured was using the incorrect side of the glass slides in the fluorimeter machine which caused the reagents to not form a drop but to spill on the slide. An error that might have resulted from the PCR machine would be that they did not reach the exact temperatures required for the reaction so the results may have been slightly off. The machines are inexpensive ($50-$100) so they are not the highest quality of PCR machines.

Intro to Computer-Aided Design

3D Modeling

Our team decided to use TinkerCad to create our design. The software is actually quite simple to use and has a feature that lets the user create an account to save their work. Our team members did not have any experience with using this software, but we had troubles using SolidWorks a while back so we decided to try something new. The TinkerCad software is a lot easier to use and a lot more straightforward than the other choice of software. After using it, our team feels quite confident designing with it and we think we will use it again to bring other designs to life.

Our Design

Our Design


Our design, although similar to the original, has a built-in camera to provide the user with accurate and high-quality photographs. This will allow the consumer to have the same angle and focus in every photo, which will make the overall lab more accurate. What also differentiates our PCR machine from the original is that our glass sides are going to have a small strip on the side that is supposed to be used. With the other slides, the only difference was the texture. This made it difficult to tell which side was the correct side to use, and our group made a few mistakes during the previous lab. Our PCR machine is going to be made out of titanium alloyed with aluminum. This makes it lightweight, durable, and more affordable than what some other PCR machines use. Other PCRs use silver, which is great for durability, but not for affordability.


Feature 1: Consumables

The glass slides will be manufactured by our company. The reason being is that we would like to improve the slides by making them easier to use and more durable.We plan on doing so by using a newly found and improved glass made primarily of silver and palladium. This metallic glass is highly durable and almost never cracks. The reagents such as the PCR mix, primer solution, and SYBR Green solution will remain the same. Our group believes that these reagents work well as is, and will be left alone. The heater will also remain the same.

Feature 2: Hardware - PCR Machine & Fluorimeter

In our design, the Open PCR machine and the fluorimeter are combined into one thing. We only made one addition to the original design and that was adding the camera. It will perform the duty that the smartphone performed with the fluorimeters that we used in lab. The addition of the camera allows for more accurate and consistent photographs This machine has the technology to run the PCR reaction as well as take the photographs that would've been taken separately in the fluorimeter. The preparation for the fluorimeter, such as setting up the slides and incorporating the SYBR green will have to be done manually before the machine begins to run the experiment. Our machine will utilize the new hardware by combining the two parts of the experiment making for more efficient results.