BME100 s2016:Group4 W1030AM L6
BME 100 Spring 2016 | 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 | ||||||
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LAB 6 WRITE-UPBayesian StatisticsOverview of the Original Diagnosis System The BME 100 class used a disease SNP diagnostic system to diagnose patients. The division of labor was shared between seventeen groups with six students. Overall 34 patients were diagnosed. In the beginning of the lab, each group received three replicate DNA samples from two patients. The number of replicates per patient were done in order to prevent error, because three results are better than two. More results gives reliability to the accuracy of results. PCR controls were done by capturing the fluorescence of DNA with a camera phone. Three images were captured for each PCR reaction so the data could be more reliable. An iPhone was used because the optics was state of the art. SYBR green I was used to help capture the fluorescence when used in a fluorimeter. The ImageJ calibration controls helped determine the fluorescence of Calf Thymus by comparing the RAWINTDEN of the drop and the RAWINTDEN of the background. Statistics were used to find the mean and standard deviation of the RAWINTDEN of the drop minus RAWINTDEN of the background for three images. The data for the calf thymus helped generate a calibration curve. The calibration curve helped determine DNA concentration of each PCR reaction. The class's final data was shared on a spreadsheet. There were few inconclusive results which was expected. Each group had "yes" or "no" results. If a patient had three "no's" then the patient did not have the disease, and vice verse. Then Bayesian statistics was used to determine the reliability of the diagnostic tool. Such as what is the percentage that a patient receives a negative result if he or she really does have a disease. Any challenges were using new methods of analyzing data.
Bayesian statistics can be used to determine the reliability of the PCR reaction test to detect disease SNPs and the accuracy of the test to predict whether or not a patient will develop the disease. Our Bayesian statistics results indicated that the probability of getting a positive final test conclusion given a positive PCR reaction was close to 1. The probability of getting a negative final test conclusion given a negative PCR reaction was close to 1. These high probabilities mean that the PCR reaction test can reliably detect disease SNPs.
Intro to Computer-Aided DesignTinker CAD was a useful little tool. However, it does not compare to the mobility that SolidWorks allows. SolidWorks allows for more views when assembling a multiple part device. It was very simple to import parts for the PCR machine which was very useful. Tinker CAD also allowed us to change each parts color however it did not use different material types that SolidWorks allows. Our Design
Feature 1: Consumables
Feature 2: Hardware - PCR Machine & FluorimeterThe PCR reaction is a complex cycle of heating the samples at a specific temperature for a certain amount of time. The group can redesign the PCR machine that could time the reactions and set the desired temperatures. The redesigned PCR reaction can also store more reactions, and therefore give more results. The fluorimeter was difficult because the iPhone we wanted to use couldn't stay in one place. So the redesigned fluorimeter would have a built in camera or either a camera dock that could be adjustable. This necessary so a person could save time by not tinkering with the fluorimeter. Also the camera will be able to stay in one place so the distance from the camera to the drop is constant.
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