BME100 f2015:Group8 8amL6

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BME 100 Fall 2015 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: Riley Barnett
Name: Manuela Hiche Schwarzhaupt
Name: Nicholas Grant
Name: Chris A Pina 1
Name: Cody Wong
Name: Luis Montero


Bayesian Statistics

Overview of the Original Diagnosis System

The objective of our lab was to identify an unknown SNP which causes a disease. We had 34 patients and 17 groups. Each group consisted of 5-6 students and each group was assigned two patients at random. We were assigned to identify if the patients were positive or negative for the SNP using PCR and ImageJ to collect data and make a conclusion. We used three replicants per patient to increase the validity of our test results. We wore lab coats and gloves to prevent contaminating the PCR samples that contained our patients DNA, the buffer solution, and the SYBRGreen1. After our samples went through our PCR cycles we carefully placed droplets of the samples onto slides placed on a fluorimeter. The fluorimeter emitted a light which shined through our sample droplet. We then used a smart phone camera to take pictures of the droplets. In total we took 60 pictures of 18 replicants of our patients DNA and our positive and negative control. We analyzed theses pictures through ImageJ to find the area, integrated density, standard deviation, mean gray value and modal. These parameters were able to help us see how much SYBRGreen1 was present in the droplet sample which indicated to us whether the patient was positive or negative. High concentrations of SYBRGreen1 showed that the patient was positive for the disease SNP. Low concentrations indicated that the SNP was not present in the patient.

The class's final data from the BME100_Fa2015_PCRresults spreadsheet provided us the total positive and negative PCRs, total positive PCRs with positive test conclusions, and total negative PCRs with negative test conclusions. Furthermore it provided us their final test conclusion of whether or not the patient has the disease. 6 groups did not provide a test results leaving us with 28 total diagnoses. Only 8 groups diagnosed the patient with the disease.

What Bayes Statistics Imply about This Diagnostic Approach

Calculation 1 and 2

For calculation 1 the probability that a patient will get a positive final test conclusion, given a positive PCR reaction is 85%. Calculation 2 the probability that a patient will et a negative final test conclusion, given a negative diagnostic signal is 95%. Therefore we can can conclude that our PCR reactions were able to better identify negative reactions than positive reactions.

Calculation 3 and 4

The probability that a patient will develop the disease given a positive final test conclusion is 25%. The probability that a patient will not develop the disease given a negative final test conclusion is .69%. This indicates that both of our PCR reactions are not reliable at detecting whether or not a patient will develop the disease.

Sources of Human and Mechanical/Device Error

We were able to identify three possible sources of error are inconsistent distance from sample, camera angle, and quality of picture. Some groups may have failed to adjust the distance between the smart phone and fluorimeter to view the drop closely while keeping it in focus greater than 4cm away. In regards to the camera angle groups may have failed to adjust the height of the fluorimeter to get a camera view of the slide nearly edge-on. Our finally source of error was from poor quality photos. There is a possibility that the groups were not able to produce good quality photos droplets which could have prevent ImageJ from analyze them and creating inaccurate data. This would have most likely occurred it the groups smart phone did not have high exposure or saturation of light to fully capture the droplet on the slide.

Intro to Computer-Aided Design

Instead of using TinkerCAD we used Solid Works to create our device. We used Solid Works instead of TinkerCAD because we felt more comfortable with the program and had already had some experience. We found the program to make the design process easier and helped us create a new and improved fluorimeter with adjustable camera. We first made a base for fluorimeter and added two pillars and elongated them to make the light beam area that passes through the droplets. We attached an adjustable part to allow the camera to be moved back in forth from various position. The camera was added and is at the precise height for optimal quality of pictures. Our platform in-between the light poles can be adjusted to turn so that the glass slide can be moved off and replaced by another one.

Our Design

Our design is a camera that is attached to the fluorimeter and can be controlled from a computer. We choose to add a camera to the fluorimeter because during the PCR lab we had difficultly getting a consistent distance from the droplet. We also found it aggravating to stack multiple objects under the fluorimeter to reach the proper height for the smart phone camera to take a good picture. Our design eliminates both problems by maintaining a consistent distance from the droplet on the slide and height preventing the need to stack objects. Our camera can be attached to a computer via cable and directly be controlled from it. This also allows researchers to receive their pictures instantaneously from the camera without the added hassle of uploading the pictures form their smart phone. This also enables researchers to access the pictures and utilize them in ImageJ. Creating a better more efficient process for analyzing and gathering data from the PCR droplets. The original process required many tedious steps in order to analyze the droplets and collect the proper information. Our device will be able to help researchers to minimize the time taking pictures and make it easier to come up with accurate results.

Feature 1: Consumables

Smaller glass slides

Camera cord

By having smaller slides we see some minor weaknesses to the design of our device. Smaller glass slides decreases the amount of samples that we can put onto the slide. The normal slides will allot about 5 samples per slide. Ours would reduce this to three due the shortened length of our slides to accommodate for the reduction of our fluorimeter. This means that if researchers need to analyze many sample from a PCR they will have to replace the glass slide more often.

Feature 2: Hardware - PCR Machine & Fluorimeter

Our design incorporates a moving camera that can be locked into place. This will help insure that we have consistent distance and picture quality for each sample. This will help improve the data analyze and insure that we obtain accurate PCR information. Our fluorimeter can be attached to a computer so that we can take multiple pictures without the hassle of moving the box to manually take pictures. The draw backs to our design will be smaller glass slides to accommodate the redesign of our device. A camera cable compatible with the fluorimeter will also be needed so that pictures can be taken.