BME100 f2018:Group2 T1030 L6

From OpenWetWare
Jump to navigationJump to search
Owwnotebook icon.png BME 100 Fall 2018 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
BME494 Asu logo.png

OUR COMPANY

Name: Daniella Pautz
Name: Amy Floyd
Name: Ally Spohn
Name: Jackson Gates
Name: Nick Hagan



Name:Company Logo

LAB 6 WRITE-UP

For the first part of the lab we calculated Bayesian Statistics. We found the probability that a patient gets a positive final conclusion given a positive PCR reading and a negative final conclusion given a negative PCR reading. Then we found the probability someone had Parkinson's given a positive or negative PCR result. We then related the sensitivities and specificities to reliability of the PCR tests. Based on these conclusions, we determined where errors were being made in the procedure, and brainstormed ways to improve our test. We designed a new fluorimeter machine with an improved method of placing the droplets and taking the pictures to give more accurate results.

Bayesian Statistics

Bayesian Statistics Bayesian statistics: is the theory in the statics based on probability and the degree of belief. It is used whenever prevalence, sensitivity, and false positive rates are provided. Sensitivity is the probability that a person with the disease will test positive for that disease, however, specificity is the probability that are person being tested for a disease, will test negative when they do not have the disease. The PPV (Positive Predictive Value) represents those who have the disease that are being tested and the probability that their results will be positive. The NPV (Negative Predictive Value) is the probability that a person who tests negative for a disease does not have the disease.

Overview of the Original Diagnosis System

Calculation 1 - The probability that a patient will get a positive final test conclusion, given a positive PCR reaction. The A value is the frequency of total POS conclusions which 0.44. The B value is the frequency of total pos PCRs which is 0.42. The probability of B given A is the frequency of total pos with POS and the value is 0.90. The calculation of the probability of A given B is (0.90 x 0.44)/(0.42) which is equal to 0.94 or 94%


Calculation 2 - The probability that a patient will get a negative final test conclusion, given a negative diagnostic signal. The A value is the frequency of total NEG conclusions which 0.53. The B value is the frequency of total neg PCRs which is 0.51. The probability of B given A is the frequency of total neg with NEG and the value is 0.94. The calculation of the probability of A given B is (0.94 x 0.53)/(0.51) which is equal to 0.98 or 98%.


Calculation 3 - The probability that a patient will develop the disease, given a positve final test conclusion. The A value is the frequency of total "yes" diagnoses which 0.34. The B value is the frequency of total POS conclusions which is 0.44. The probability of B given A is the frequency of total "yes" diagnoses with POS conclusions and the value is 0.43. The calculation of the probability of A given B is (0.43 x 0.34)/(0.44) which is equal to 0.33 or 33%.


Calculation 4 - The probability that a patient will not develop the disease, given a negative final test conclusion. The A value is the frequency of total "no" diagnoses which 0.66. The B value is the frequency of total NEG conclusions which is 0.53. The probability of B given A is the frequency of total "no" diagnoses with NEG conclusions and the value is 0.77. The calculation of the probability of A given B is (0.77 x 0.66)/(0.53) which is equal to 0.96 or 96%.


What Bayes Statistics Imply about This Diagnostic Approach

The calculations for a positive PCR with a positive conclusion and a negative PCR with a negative conclusion are both close to 100%, making them mostly reliable for detecting the PCR disease.

The calculation for a negative result without the disease is close to 100%, which means that it is very reliable in diagnosing someone without the disease as not having the disease. However, the calculation for someone having the disease receiving a positive diagnosis is below 50%, making it mostly unreliable for detecting the disease in someone who has it.


Questions
Which calculation describes the sensitivity of the system regarding the ability to detect disease SNP?

         Calculation 1 describes the sensitivity of the system regarding the ability to detect disease SNP.

Which calculation describes the sensitivity of the system regarding the ability to predict the disease?

         Calculation 3 describes the sensitivity of the system regarding the ability to predict the disease SNP.

Which calculation describes the specificity of the system regarding the ability to detect the disease SNP?

         Calculation 2 describes the specificity of the system regarding the ability to detect the disease SNP.

Which calculation describes the specificity of the system regarding the ability to predict the disease SNP?

         Calculation 4 describes the specificity of the system regarding the ability to predict the disease SNP.

Sources of Error

  1. Human error such that too much or too little of the SYBR Green was added into the calf thymus solution
  2. Errors with technology, such as the phone and image j
  3. Different light exposure times with the dye; there may have been too much contact with the light

Intro to Computer-Aided Design

3D Modeling

The group decided to use Solidworks in order to prototype our new fluorometer design. Most of the group had used this program before for other classes. Amy was selected to make the actual prototype. She had a relatively easy time building the various components; the most difficult of which were the hinges and the light 'on and off' switch. For her, the assembly of the separate components into the final design took the most time because she had only assembled using solidworks once before. The changing of the various materials' appearance was also time consuming.



Our Design

Name:Amberlight

Features
1) A blue LED light array enclosed within a 3 x 5 in box, all sides and the bottom of the box are hard plastic.
2) The top of the box has an inset of clear glass panel which will be backlit by a blue LED array when the switch is turned to the on position.
3) Included on the glass panel is a grid creating 180 - .25 in2 cubes.
4) Attached to the back of the box via two hinges is a see-through lid that is an amber orange color. When flipped down, it rests approximately .25 inches about the glass panel. This distance should leave enough room for the droplets of combined SYBR green and DNA concentrations to rest untouched on the gridded glass panel beneath.
5) The black out box was changed to include a single 1 x 1 hole in the center of the top panel. This is to be used by laying the smartphone down on top with the lens facing down into the box.




Feature 1: Consumables

Consumables Kit

  • 8 tubes of PCR reaction mix 50 μL each: mix includes Taq DNA polymerase, MgCl2, and dNTP’s
  • 8 tubes of DNA primer mix 50 μL each: Each mix contains a different template DNA but all tubes will have the same forward primer and reverse primer
  • A strip of 12 empty PCR tubes
  • 5 tubes of 1.5 mL each of calf thymus solution each containing different solutions: 5,2,1,0.5, and 0.25.
  • 1 1.5 mL tube of buffer
  • 1 1.5 mL black tube of SYBR green

We have decided to not include a micropipettor and corresponding disposable tips because it is assumed our target market of schools and labs will already have a supply of preferred micropipettors with the correct sized disposable tips for their needs. Our kit will also not include any glass slides because of the modifications we have made to the previous fluorimeter. The need for glass slides has been eliminated by the gridded glass panel attached to our filtered blue LED box, it will simply need to be cleaned with alcohol before each use. As we have not made any changes to the OpenPCR machine no additional or specialized consumables were added.

Feature 2: Hardware - PCR Machine & Fluorimeter

Fluoramber Hardware Kit

  • Fluorimeter system:
blue LED light box with amber filter
Black out box with 1 x 1 hole in top panel modification
  • OpenPCR Machine

Our new system will include both the OpenPCR machine and the fluorimeter. We decided not to make any modifications to the previous OpenPCR machine or software as we felt it was both well suited for a classroom and well priced. It also could serve as a good building project for the ambitious teacher or student. The fluorimeter system will no longer include the previous slide holder or LED light system, the phone holder was also removed, essentially replaced by our new product.

Instructions for use
All steps will remain the same as the previous lab system until the OpenPCR machine has finished its thermocycles. Once the samples have been removed from the OpenPCR machine and brought to the lab bench here are the list of steps to use the new fluoramber device:

  • Add 80 microliters of the calf thymus solution labeled 5 in the center of a .25 in2 square on the gridded glass panel (preferably starting at the left most square of the first or second row)
  • Add 80 microliters of the calf thymus solution labeled 2 in the center of a .25 in2 square on the same row as the 5 calf thymus solution but leaving an empty square in between
  • Repeat step 2 for the other calf thymus solutions 1, 0.5, and 0.25
  • Add all of each separate PCR reaction samples into the separate labelled buffer tubes. (the first PCR reaction, transferred to one buffer tube, close and relabel tube, then transfer second PCR reaction sample into the next buffer tube, relabel etc...)
  • Add 80 microliters of the first labelled PCR reaction sample with buffer onto the center of the leftmost square of a new row on the gridded glass panel.
  • Add 80 microliters of the second labelled PCR reaction sample with buffer onto the same row as the first PCR reaction sample preferably leaving a square between
  • Repeat step 2 for all other PCR reaction samples until all have been added to the glass panel
  • As quickly as possible add 80 microliters of SYBR green solution to every drop on the glass panel.
  • Flip down amber filter lid and slide box into black out box.
  • Switch on Blue LED light array
  • Close flap on the black out box
  • Place smartphone lens over 1x1 inch hole in top of black out box and focus camera
  • Take 3 clear images of the entire array of drops
  • Remove the blue LED light array Box
  • Remove the 160 microliter solution drop from the slide using the pipettor and dispose into the waste bucket provided
  • Proceed with pre-established imageJ processing steps but with only 3 pictures instead of 42.

Strengths of New Design
We chose to redesign this aspect of the fluorimeter and the black-out box to address two inter-related weaknesses of the previous system used in Lab 6. The first is the issue we had of maintaining the exact same distance of the smartphone camera. During the lab we kept bumping the camera stand, moving it in order to remove and apply new droplets, and generally forgot to measure the distance. By laying the smartphone on top of the black-out box and simply positioning it over a hole, two important things occur: 1) the distance is ensured to always be the same and 2) the group can focus and adjust the image on screen before taking any pictures. The second weakness is the amount of time and inconsistency involved in the processing all 30 images in imageJ. Nearly every picture taken had to have the measuring circle redrawn or repositioned for both the droplet and background. Our hope is that by simply putting all the droplets on the gridded glass panel in one go in combination with a set camera distance we can accomplish 2 goals: 1) reduce the number of pictures required for processing from 30 to just 3 images and 2) reduce the overall light exposure of the SYBR green. Lastly, since amber filters cancel out blue light while allowing the yellow of green light/fluorescence through, we added the amber filter lid was added in order improve the processing accuracy of our imageJ analysis by making the differences between a negative and positive more apparent.