BME100 f2014:Group11 L6

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Owwnotebook icon.png BME 100 Fall 2014 Home
Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
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Name: Aliya Yano
Name: Breanna Corrigan
Name: Julian Lopez
Name: Carlos Cabanes
Name: Mohammed Almaimani


Bayesian Statistics



Calculation one describes the sensitivity to detect disease SNP.
Calculation two describes the specificity to detect the disease SNP.
Calculation three describes the sensitivity to predict the disease.
Calculation four describes the specificity to predict the disease.

Overview of the Original Diagnosis System
The BME 100 class tested patients for disease-associated SNP by assigning 34 different teams of 5-6 students 2 patient samples to test. That results in 68 total patients being tested. Within the groups, many things were done to prevent error. Each group was given three samples for each of their patients and had controls to compare the PCR results to. The use of ImageJ was first used on calibration samples in order to determine a baseline and get the best results. When actually testing the patient PCR samples three pictures of each individual PCR sample were supposed to be taken and analyzed in ImageJ. Out of the 68 patients sampled, 6 had no test results given, and 8 results were inconclusive. Out of those remaining, 30 tested positive and 24 negative.

What Bayes Statistics Imply about This Diagnostic Approach

Calculations 1 and 2 help describe the sensitivity and specificity of the test to be able to detect the disease. The first calculation shows whether the patient will be diagnosed as positive if the PCR test was positive. The result was relatively close to 100% which suggest that if the patient tested positive, they have the disease. In comparison, calculation 2 determines if the patient will be diagnosed as negative if the PCR test was negative. This too was near 100% and shows that if the test results negative, the patient likely does not have the disease.
Calculations 3 and 4 deal with the sensitivity and specificity of the test to predict the disease. Calculation 3 gives the probability that a patient will have a positive diagnosis for the disease if given a positive conclusion. The result was far from 100%. Calculation 4 gives the probability that the patient will not get the disease if given a negative result. This value was also far from 100%

Computer-Aided Design

TinkerCAD is a system very similar to Solidworks, which we used in BME 182. Using a template provided we simply used this program to create the PCR machine pieces put them together. The following are picture of the PCR machine we made from the template. calibration
Our Design


Our design change was simple: we increased the area that could hold samples to be tested. This would allow more samples to be tested at once and reduce the number of machines needed in the lab.

Feature 1: Consumables Kit

The consumables were relatively well packaged and therefor will be packaged similarly in our kit. However, there was one weakness we would address: all the samples could be contaminated easily as they were open to the air and had no protection from random particles that could float down into them. To remedy this,the sample containers could be made with a sort of plastic covering on the top of the tube. It would not be a cap, which they already have, but more like the thin plastic squeezable ketchup bottles have. This could keep contaminates from floating down into the samples.

Feature 2: Hardware - PCR Machine & Fluorimeter

The PCR machine and flourimeter worked well for the most part and would be used in basically the same fashion. They were both small, portable and inexpensive which are bonuses. But we would change a couple of things. First we would increase the amount of samples the PCR machine could test at once by increasing the sample space area. This would reduce the amount of PCR machines needed and hopefully make the testing go faster. And secondly, we would make the flourimeter out of more sturdy material as it was rather flimsy.