BME100 s2016:Group12 W1030AM L6

From OpenWetWare
Jump to navigationJump to search
BME 100 Spring 2016 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
Course Logistics For Instructors
Wiki Editing Help


Name: Liz Delgado
Name: Ryan Male
Name: Allan Santos
Name: Stephanie Vargas
Name: Antonio Lopez
Name: Aderonke Adewuyi


Bayesian Statistics

Overview of the Original Diagnosis System

Previously, the diagnosis system for SNP required the use of a fluorimeter and external camera as well as an image analysis program. The diagnoses of 32 patients was carried out by 16 different groups of 6 people each in order to reduce the error in the diagnoses. Half of each individual group used the fluorimeter and the external camera to take pictures of samples concentrated into dots after they added SYBR Green in order to make the samples glow independently based on how the substance interacted with the DNA in the sample. Because the pictures were meant to be analyzed for the amount of light the drops produced, the fluorimeter with drops with SYBR Green was put in an environment with less light in order to reduce the amount of error that would have resulted from there being additional light the pictures would have picked up. The other portion of the group then received the pictures from the camera and utilized the image analysis software, Image-J, to see how much green light was present in the glowing drop after the SYBR Green was added. In order to further reduce the error in the experiment, three different pictures of each drop sample were sent to this second group which analyzed each picture and took data from each, mitigating the amount of variance present in just one picture.This data was then used to create an Excel sheet organizing the Rawintden Drop values for each concentration of 2X Calf Thymus DNA solution. The means and standard deviation were then calculated using Excel's statistical capabilities. This allowed us to have a calibrate our data and compare our different DNA samples to determine if a patient tested, positive, negative, or if the test was inconclusive. From there, the data gathered was compiled with data from the other groups that also used the same diagnostic system, and the entire lab’s data concluded whether the patient’s diagnosis was positive, negative, or inconclusive. In the end, the lab’s data resulted in two inconclusive results, 22 correct results according to a doctor’s diagnosis, and 8 incorrect diagnoses. Some difficulties that our diagnosis system faced were the inability to have a completely light-free environment which would have been ideal due to the inability to set a timer on the external camera without it giving off light during the countdown. However, the amount of light was reduced as much as possible by closing as much of the surrounding structure for the fluorimeter as possible.

What Bayes Statistics Imply about This Diagnostic Approach

Calculations 1 and 2 Calculations 1 and 2 resulted in similar "given" results for both the positive and negative test conclusions. The probability that a patient will get a positive PCR reaction given a positive final PCR conclusion when considering the other individual PCR replicates was about the same for the same given concerning negative results: close to 1.0 or 100%. This percentage was also the case with the probability that a patient will get a positive/negative final test conclusion given a positive/negative PCR reaction. From this we can conclude individual PCR replicates are very reliable for concluding whether a person has the disease SNP or not.

Calculations 3 and 4 Calculations 3 and 4 actually display different results for their "given" probabilities of the negative and positive PCR conclusions. The probability that a patient will receive a positive PCR test given they develop the disease is .5 (50%), and the probability that a patient will develop the disease, given they get a positive test conclusion is only a little less than .5 (50%). This is a fairly low probability as it means the patient only has a 50:50 chance at a correct diagnosis if they have the disease, putting half those that go for a diagnosis in danger. On the other hand, the probability that a patient will get a negative test conclusion given they do not develop the disease is almost 1.0 (100%), and the probability that a patient will not develop the disease given a negative final test conclusion is actually over 1.0 (100%). These results are good as they mean that those that are free of the disease will not receive false positive results for the most part. This means they will not have to deal with treatments not intended for them that might actually endanger their health.

Possible Sources of Error Three possible errors that occurred during the diagnosis system that could have affected the Bayes values in a negative way were the use of an only light-reducing environment, the exposure of SYBR to light, and the variation in each different group's approach to Image-J software. As mentioned before, our group was unable to provide a light-free environment due to the external camera's inability to use a timer. This made extra light affect the results of the image analysis. The SYBR was also exposed to light at various times from putting it into the drops to just having it out throughout the experiment. Because of these two specific factors, the results were skewed as SYBR is light sensitive and was affected at all points it was exposed to light. Finally, the Image-J software required the user select the area to be analyzed with an oval tool which made some users define the area of the drop differently and, therefore, come up with different results from their image analysis.

Intro to Computer-Aided Design


Summary: TinkerCAD allowed for a relatively easy way to put together a model depicting the PCR machine that our group decided to compile for our lab. It consists of a solar panel on the external interface that allows for it to be more accessible for third world countries, and for those who do not have a readily apparent use of electrical interfaces by the more conventional means. There are other altercations to the design internally that will be further described in the following sections. In terms of the comparison to Solid Works, TinkerCAD allows for a much easily and more applicable interface to accurately and more efficiently describe exactly the changes that we have chosen to make. In the experiences that we have thus obtained from Solid Works, TinkerCAD is the preferred means of putting together a visually applicable model for what we have intended to design.

Our Design

Feature 1: Consumables

CONSUMABLES : PCR MIX, buffer, control DNA (positive/negative) SYBR Green Solution, Primer, glass slides, pre-labeled breakable plastic tubes, pipet tips.

Our consumables will consist of the items listed above. We have chosen to add the regents to the kit because they are crucial for experiments. The kit will be broken up into two sections the first section containing necessary regents such as the PCR mix, buffer, positive/negative control DNA, SYBR Green Solution, and Primer. We have included the pipet tips in the kit to minimize the chance of contamination. Lastly, and most importantly our group struggled with labeling and keeping tracks of the small tubes with the samples in it so with this being said, we have decided to include pre-labeled breakable tubes so they are easy to read and keep track of in order to come up with accurate and precise results.

Feature 2: Hardware - PCR Machine & Fluorimeter

Fluroimeter System: The fluorimeter device is a necessary component of the PCR machine designed to measure the intensity of light. In this experiment the fluorimeter device was setup to first obtain a calibration of the data and to obtain the intensity of the SYBR Green. However, in this experiment the fluorimeter was setup by using a smartphone and a stand to take pictures. Such setup made the analyzation of data become very inefficient. It was observed that after each picture the phone/camera would shift from position and it would be necessary to readjust the position. After obtaining the necessary pictures each individual picture had to be analyzed on ImageJ, this proved to be a very lengthy process.

To address such concern our PCR machine is designed with a built in camera, on board computer, touch screen, and connectivity. The built in camera provides our PCR machine the ability to obtain reliable results by ensuring the position of the camera stays the same. The on board computer is designed to be able to analyze the readings on the device itself in a fast and efficient manner. The touch screen allows the user to customize the settings and determine what color the onboard computer should be analyzing. This also allows the user to provide a calibration system and set the values as a standard to determine if DNA samples test positive for a disease. The connectivity feature allows the user to export the data to Excel and statistically study the data in more ways, if desired. Such features would enhance our branding and positioning. These features would make a connection with lab technicians and various clinics in the world that our Brand designs products that are very reliable, efficient, and effective. These features would also set our device apart from those in the market due to its revolutionizing new design which positions our PCR machine as superior than others.