BME100 s2015:Group6 12pmL6

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BME 100 Spring 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: Justin Blommer
Name: Ethan Mathew
Name: Spencer Cobb
Name: Sarah Jones
Name: Abigail Rene

Sharp Snap


Bayesian Statistics

Overview of the Original Diagnosis System

The BME 100 Class used a polymerase chain reaction to test patient samples for a disease-associated SNP. The class was split into 26 teams of approximately 5 students. A total of 52 patients were tested for the SNP, with each team testing 2 patients each.

Each patient had 3 replicates of their DNA sample. This was done so that the lab could use an average of the 3 results, in case there was an error or inaccuracy in one sample's testing. The DNA samples were added to a primer mix and run through a PCR thermal cycling machine. The maximum number of replications is usually achieved at ~32 cycles. To eliminate any possible errors, a total of 35 cycles were run to ensure the maximum number of replications had been reached. Once all cycles were completed, the samples underwent a final hold at 4°C. They were held at this temperature for a week in between lab sessions. This was to prevent any of the newly formed DNA strands from separating between the completion of PCR and the fluorometer analysis.

After PCR was completed, the DNA samples were analyzed using a fluorometer and the ImageJ software. The DNA samples were mixed with a SYBR Green I solution and placed in a fluorometer. Each of the patient's 3 replicates was analyzed, along with a positive and negative control. To analyze, a smartphone image was taken of each droplet in the fluorometer and uploaded to a computer. The ImageJ software was then used to analyze the images.

In Image J, the measurement settings were first changed to display: Area, Integrated Density, and Mean Grey Value. After the selected image was imported, it was split into its individual channels: red, blue and green. The only channel used for this analysis was the green channel image, because that is the color the SYBR Green solution fluoresced. When selecting the part of the sample droplet to be analyzed, it was important to not select the brightly colored edges of the droplet, as this is where the fluorometer light was entering and exiting the droplet. Including this in the measurement would cause greater inaccuracies in the data. Each unique sample drop had three separate images taken of it. The average value of these three images is what was used for the value for that replicate. This was to account for possible light contamination or poor image quality affecting the measurements. Once the values were calculated, these results were recorded on a spreadsheet.

Once completed, all 26 teams uploaded their results, which were added to one master spreadsheet. Each group's results were uploaded for each patient. Each replicate was determined to be either a "positive" or "negative" result, based on whether its values were more similar to those of the positive or negative control values. Ideally, all of a patient's replicates had the same conclusion for containing diseased or non-diseased DNA. This would be considered a successful conclusion. In some cases, the sample DNA values were in between the positive and negative values, deeming it inconclusive. Other data sets that displayed obvious errors were highlighted in the spreadsheet and disregarded from the Bayes statistics calculations.

What Bayes Statistics Imply about This Diagnostic Approach

Calculation one was used to determine the probability of a positive final test conclusion given positive PCR results. This meant that if a patient was given positive PCR results, did they also receive a positive final test conclusion? The probability of this was extremely close to 1 (100%), meaning that this was a very probable occurrence.

Calculation two was used to determine the probability of a negative final test conclusion given negative PCR results. What was being evaluated was if the patient received negative PCR results, did they also receive a negative final conclusion? While slightly smaller than calculation 1, the probability of this was also very close to 1 (100%), indicating a very probable occurrence.

There were multiple sources of human error that could have negatively impacted the calculated Bayes values. Most likely the greatest factor affecting the calculations, was incompetence in using the ImageJ program. Only 3 out of 26 teams correctly diagnosed both of their patients, with Group 6 being one of them. Many students were simply not using the correct method in analyzing images in the software. Another error was the quality of images being taken for use in image analysis. Even with proper settings, many smartphones were not able to capture high quality images of the DNA samples. A blurry, low quality image will not be accurately analyzed in the software. Additionally, some teams were not properly using a labeling system for their PCR tubes. This resulted in those teams' negative controls showing a higher RAWINTDEN value than the positive controls. This also, however, could have been attributed to incorrect analysis in ImageJ.

Calculation 3 determined the probability of the patient actually developing the disease, given a positive final test conclusion. This was essentially a cumulative assessment of the accuracy of the diagnoses. The probability of this was a little under 0.5 (50%). Therefore, while some of the diagnoses were accurate, less than 50% of the patients that were diagnosed as positive for the diseased DNA actually developed the disease.

Calculation 4 determined the probability of the patient not actually developing the disease, given a negative final test conclusion. The probability of this occurrence was slightly less than 1 (100%). Therefore, it can be determined that a correct negative final test conclusion was much more probable than a correct positive final test conclusion. Of the patients that were given a negative final test conclusion, roughly 90% of them did not actually develop the disease.

Computer-Aided Design


The TinkerCAD tool is an online 3-D design program that allows the user to create almost any design imaginable. The designs created in TinkerCAD can be sent to 3-D printers to be created. In the lab, the tutorial lessons were first completed in order for our group to obtain a basic understanding of the functionality of the program. Next, the already created pieces of the PCR machine were imported and assembled together to create the complete machine. Once that was complete, TinkerCAD was used to develop our group's design for our improved fluorometer. First, using an actual fluorometer as reference, the original fluorometer was designed in the program. Next, the improvements we made to it were incorporated into the design.

Our Design

Front View

Top View

Side View

Our design differs from the original fluorometer design in that it incorporates a self-contained camera. The camera mounts on to arms that mount on to either side of the slide platform. The arms are then able to be rotate between two different positions. The camera mount can be in the upward position so that it is out of the way for sample droplets to be placed onto the slide. After the slide is in position, it is rotated to the downward position where the camera is at an ideal angle for capturing images for analysis. The camera also houses a slot for an SD card. The images taken by the camera are automatically saved onto the SD card, which can then be easily removed and inserted into a computer for image analysis.

Feature 1: Consumables Kit

Consumables Kit Contents:

  • SYBR Green Dye (2 PCR tubes of 1,000μL each)
  • PCR Reaction Mix (8 PCR Tubes of >100μL each)
  • DNA Primer Mix (8 PCR tubes of >100μL each)
  • Positive/Negative Controls (2 tubes each, dependent on what customer is testing for)
  • Calf Thymus DNA Solution for Calibration (5 tubes of different concentrations (0.25, 0.5, 1, 2, 5)
  • Buffer (8 tubes of 500μL each)
  • Glass Slides for Fluorometer (package of 5)
  • Pipettor Tips- (rack of 96 tips)

All of the liquid reagents will come packaged in PCR tubes that will be mounted in a single rack. The lone rack will have different sized holes small and large enough to accommodate the different sized tubes. The PCR tubes used for class had some issues with the lids on staying secured tightly once opened. Therefore, our tubes will incorporate a snap lock design that will ensure that the lids will not come loose during shipping. The glass slides will come packaged in a hard case to ensure they do not break during delivery.

The SYBR GREEN 1 is light reactive. To prevent it from reacting, the packaging will not allow much light through and the SYBR GREEN 1 itself will be wrapped in aluminum foil to further prevent light from reaching it. All the PCR tubes will be put into a rack to hold them in place, and that rack will be placed in a box so that the tubes will not fall out even if the box turns upside down during shipping.

Feature 2: Hardware - PCR Machine, Fluorometer, and Micropipettor

The PCR machine had several shortcomings when it came to performing its primary task of thermal cycling. The machine required the use of a computer connection and its own software, called OpenPCR. For the purposes of this lab, it was not a major inconvenience because there were computers all around the lab already loaded with the software. However, this is not the case in all labs.

Our improved system would include a digital interface which allowed for programming the cycles (time, temperature, and number of cycles). This interface would also include convenience functions such as a a progress meter detailing what step the process was currently in as well as a countdown timer until the reaction is complete.

Another issue addressed with the PCR is the small capacity of the machine. Only holding 16 PCR tubes, each machine accommodated just two groups, meaning nine machines were required for the PM lab section. Ideally, just one single machine would have the capacity to evaluate all eighteen groups at once. This would also eliminate any unwanted variables created by samples being run in different machines.

The final issue noted in the operation of the PCR machine was the amount of time it took to complete all of the cycles. The most effective source of speeding up the process is using enzymes that are able to work faster. However, with current technologies, using improved enzymes with a faster process will cause decreased reliability of results. Disease detection in living patient's DNA is a process in which reliability is the top priority, therefore it will not be sacrificed in our improved device.

The greatest weakness addressed with the fluorometer technique used was the poor quality of images being captured with the smart phone cameras. Even with the proper calibration settings the images often came out blurry, causing inaccurate analysis. Also, having to manually click the take photo button on the screen and then quickly close the box over the set up was not ideal. Often times the camera or fluorometer was bumped, causing the image to be off. Also, the camera did not always adjust to the decreased lighting in time before the picture was taken.

To overcome this shortfall, our team developed a new fluorometer with a built in high definition camera. This camera would be pre-calibrated to capture images at close range in low lighting, the conditions present in fluorometer applications. Smartphones, even when calibrated correctly, often cannot capture ideal images in this environment. The camera will be wired to a small box mounted on the top of the fluorimeter which will include an SD card slot to which the captured images will automatically save to. This SD card can then be conveniently removed and inserted into a computer for fluorescence analysis.

Additionally, the micropipettors used for this lab were the traditional, analog style devices. These devices required repeated twists of the adjuster dial in order to switch back and forth between volumes to be transferred. In order to increase speed, as well as eliminate common operator errors, a digital micropipettor will be used in our improved system. These digital units only require the desired volume to be set, and then a single button is pushed indicating whether that volume is to be dispensed or took in.