BME100 f2018:Group10 T1030 L6

From OpenWetWare
Owwnotebook icon.png BME 100 Fall 2018 Home
People
Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
Course Logistics For Instructors
Photos
Wiki Editing Help
BME494 Asu logo.png

OUR COMPANY

Name:Talia Hertzberg
Name:Samuel Whitworth
Name:Lauren Everett
Name:Maritza Trejo
Name:Angel Cardenas

Our Brand Name

LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

        In order for the BME100 class to test patients for the disease-associated SNP that results in Parkinson's, the class divided into 17 groups with about 5 to 6 people per group. Each group diagnosed 2 patients to diagnose a total of 34 patients. In order to prevent error, each patient had three replicates that were tested. We also utilized controls to make sure our results were in range. We did this through the use of a positive and negative controls to compare our final results against. For imagej, we used calibration controls of calf thymus DNA concentrations (5, 2, 1, 0.5, 0.25, 0) so that the quantitative numbers we were finding would be validated. When capturing images, for each calibration as well as each patient replicate, each group took three picture. The results from these three pictures were then averaged and used for our final calculations. In the end, all of the group's final calculations were compiled into a single excel sheet. The amount of data that resulted from the class went down to 32 patients as one group was not able to submit their calculations. In addition, there were 7 patient replicates that were found to be inconclusive and one PCR conclusion that was deemed to be inconclusive. Overall, the class found 14 of the final PCR results to be positive and 17 to be negative. 

What Bayes Statistics Imply about This Diagnostic Approach


       For Calculation 1,our results for the probability of getting a positive test conclusion was a little less than 50% and the probability of getting a positive PCR result was also a little less than 50%. When calculating the probability of a positive PCR given a positive conclusion it was close to 100%. Then, the probability of a positive conclusion given a positive PCR reaction was even closer to 100%. Overall, those who receive a positive PCR reaction result, 95% of them will actually have the SNP disease. Now about 5% will have a false positive. 
       For Calculation 2, our results for the probability of negative conclusion and the probability of a negative PCR reaction were both a little over 50%. Then, the probability of a negative PCR reaction given a negative conclusion was close to 100%. The final probability reading of a negative conclusion given a negative PCR reaction was really close to 100%. Overall, those who receive a negative PCR reaction result, 98% of them will not have the SNP disease. So about 2% have a false negative.


        For Calculation 3, our results for the probability of developing the disease is pretty below 50% and the probability of a positive test conclusion is a little less than 50%. Then, the probability of a positive test conclusion given that the patient has the disease is again less than 50%. The probability of developing the disease given the positive test conclusion is really less than 50%.
        For Calculation 4, the results for the probability of not developing the disease is over 50% and the probability of an negative test conclusion is a little over 50%. Then, the probability of a negative test conclusion given that the patient has developed the disease is over 50%. Finally, the probability of not developing the disease given a negative test conclusion is really close to 1.

Three possible sources of human or machine/device error that could have occurred are incorrect amounts of substance concentration, air bubbles getting into the micropipetter, and micropipette malfunction.

Intro to Computer-Aided Design

3D Modeling


Our Design





Feature 1: Consumables

Feature 2: Hardware - PCR Machine & Fluorimeter