BME100 f2018:Group7 T1030 L6

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Name: Daniella McFeely
Role: Innovative Researcher
Name: Jennifer Smetanick
Role: Innovative Researcher
Name: Ethan Armendariz
Role: Innovative Researcher
Name: Sebastian Tellez
Role: Innovative Researcher



Bayesian Statistics

Overview of the Original Diagnosis System

There were 16 BME100 teams of about 4-5 student that diagnosed and submitted results for a total of 32 patients. Each group had two patients in which they diagnosed. To prevent error, there were three replicate PCR samples per sample. A positive and negative control was added to compare the values from the patients to the known control values. To prevent error in the ImageJ analysis, it was calibrated using calf thymus DNA to show a linear relationship between the concentration of DNA and mean RAWINTDEN drop - background. From the best fit line of the calibration, one can then calculate the initial PCR concentration and compare them with the controls--the very goal of the lab. Three images of each unique PCR sample was taken and measured with ImageJ to prevent error.

Of the 32 final test conclusions, 14 were positive, 17 were negative, and 1 was inconclusive. There was originally 17 groups, but one group was unable to submit their results in time for data analysis. In comparison with the physician's diagnosis, of the 32 diagnoses, 11 had the disease and 21 did not have the disease. Though there is error attributed with the PCR process, it does not count for all the differences. Individuals who have the SNP (in this case Parkinson's) may develop the disease later in life.

In regards to the error, most of the challenges was learning how to do the procedure for the first time. Since the lab is working with small scale values, any error will have a noticeable impact. For example, pipetting properly so all the solution is transferred to the proper test tube, placing the correct value of SYBR Green I on the fluorimeter and even measuring the drops using ImageJ all has error in it. Even with these issues, this lab group did well and correctly assessed both of the assigned patients (Patient 28063 = POS/yes and Patient 67655 = NEG/no).

What Bayes Statistics Imply about This Diagnostic Approach

Calculation 1 (sensitivity to detect the disease SNP) is very reliable in getting a positive final test conclusion given a positive PCR reaction. This is because the Bayes value is close to 1.00.

Calculation 2 (specificity to detect the disease SNP) is very reliable in getting a positive final test conclusion given a negative diagnostic signal. This is because the Bayes value is close to 1.00.

Calculation 3 (sensitivity to predict the development of the disease) is not very reliable in identifying if the patient will develop the disease given a positive final test conclusion. This is because the Bayes value is about 30%.

Calculation 4 (specificity to predict the development of the disease) is very reliable in identifying if the patient will not develop the disease given a negative final test conclusion. This is because the Bayes value is close to 1.00.

Three possible sources of human or machine/device error are:

1. (Machine) Taking pictures that have minimal light interference.

  • The light box set up left room for error for the images that needed to be analyzed.

2. (Human) Placing the sample drop correctly on the slides with the excitation/blue LED light directly through it.

  • ImageJ won't be able to analyze the picture the most accurate if the light isn't directly through the sample drop.

3. (Machine/Human) Keeping the fluorimeter and camera at a correct and constant height.

  • The fluorimeter had to be raised by imprecise means and the fluorimeter/camera set up would get slightly shifted when the light box was removed between new samples.

Intro to Computer-Aided Design

3D Modeling

Our team decided to use SolidWorks to 3D model the improvement we made for the lab set up. Because all team members had used SolidWorks in the past, it was an obvious choice for us to use this program. In comparison to TinkerCAD, SolidWorks is more difficult to use. However, our experience using SolidWorks in BME 182 made us confident to use this more professional software for our design. In the end, it was a good choice because we could better model exactly what we wanted to create. Before we started using SolidWorks, we brainstormed ideas as a group. Then we sketched the drawing on paper and labeled all the necessary dimensions. From there, we were ready to make the 3D model in SolidWorks.

Our Design

BME100 Group7 1030 Fluorimeter.PNG Fluorimeter slide.PNG

We decided to focus on the fluorimeter aspect of the lab and create fluorimeter with a built-in camera. The built-in camera is linked to a computer via a wireless signal or through USB so the imageJ analysis portion will be easier to utilize. When doing the lab ourselves, we noticed that it was difficult to have the phone and fluorimeter cradle consistent and steady between samples. The fluorimeter had to be raised by an imprecise amount, the distance between the phone and the fluorimeter slide was variable as they were not connected to each other and could minutely shift, and different phone models across all of the groups could very much have variance. Our product address these issues and still stays relatively cheap. We are also creating a new slide that will keep each sample separate and allow light to only penetrate the sample. This will avoid potential cross-contamination when changing out samples.

Feature 1: Consumables

The goal is to keep as many of the original consumable products as possible. By keeping the consumables the same, we are able to keep cost to a minimum. Our kit will contain the same liquid reagents: PCR mix, primer solution, SYBR Green solution, buffer. It will also contain the same plastic tubes. The glass slides for the fluorimeter; however, will be different. As we described above, the slides will be designed to separate each potential sample to avoid cross contamination.

Feature 2: Hardware - PCR Machine & Fluorimeter

We are not redesigning the PCR machine, and are solely focusing on improving the fluorimeter design. Because the orginal fluorimeter set up is very imprecise, the system could easily be moved or knocked over when the box was moved into place or closed. Our solution is to make the fluorimeter with a built-in camera. Users will not need to worry about adjusting the height of the fluroimeter and their phone because the camera will already be at optimal distance and height from the fluorimeter slide.