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=OUR COMPANY=
=OUR COMPANY=
MedTech LLC


{| style="wikitable" width="700px"
{| style="wikitable" width="700px"
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==Bayesian Statistics==
==Bayesian Statistics==


Error prevention:


• Using the black box to cover the sample to protect the fluorescence from interacting with light available in the room
'''Overview of the Original Diagnosis System'''
<!-- Instructions: Write a medium-length summary (~10 - 20 sentences) of how BME100 tested patients for the disease-associated SNP. Describe (A) the division of labor (e.g., 34 teams of 6 students each diagnosed 68 patients total...), (B) things that were done to prevent error, such as the number of replicates per patient, PCR controls, ImageJ calibration controls, and the number of drop images that were used for the ImageJ calculations (per unique PCR sample), and (C) the class's final data from the BME100_fa2014_PCRResults spreadsheet (successful conclusions, inconclusive results, blank data). -->
 
We tested patients for the disease-associated DNA sequence or single nucleotide polymorphism (SNP) by first designing a SNP-specific primer pair which showed us how polymerase chain reaction (PCR) can be applied to detect diseased SNPs.  Each team was given three replicate DNA samples from two patients and PCR reactions were ran on these samples.  This was followed by determining if we could accurately measure different concentrations of DNA by first completing a calibration with our smart phone in order to calculate the amount of DNA.  Principles that we used here were optical caustics, fluorescence, surface activity, ImageJ software, Excel linear fit and statistics and DIY devices.  This was followed by using Bayesian statistics to determine the relibility of our attempt to detect disease SNPs and predict a patient's likelihood for developing the disease.
 
Division of Labor:
 
Our class had 26 teams of approximately five or six biomedical engineers in each team diagnosing and testing a total of 52 patients (2 per team) for a disease-associated DNA sequence or single nucleotide polymorphism (SNP). 
 
 
Error Prevention:
 
• Using the black box to cover the sample droplet in order to protect the SYBR Green fluorescence from interacting with light which would lessen its capability of giving a visual color signal when dsDNA is present.
 
• Placing the sample/SYBR Green drop in the middle of the glass slide so the drop is pinned like a ball.
 
•      Aligning the drop, by moving the slide, so that the blue LED light is focused by the drop to the middle of the black fiber optic fitting on the other side of the drop.
 
• Dispose the tip of the micropipettor each time to prevent the samples from contamination.
 
•      Use correct micropipetting technique in order to get accurate volumes and no contamination.
 
•      Using care in not moving the smartphone apparatus which would take it out of focus as well as change the distance from the phone to the drop.
 
•      Making sure that the drop is focused on the smartphone before each image is taken.
 
•      Each patient had three replicates of samples tested which would increase the reliability and accuracy of our data.
 
•      The ovals in the ImageJ software need to be done as accurate as possible for each replicate.
 
 
Class Results:
 
There was a total of 132 Polymerase chain reactions (PCRs) for the whole class.  These yielded 54 positive results for diseased single nucleotide polymorphisms (SNPs) and 67 negative results for this.  The number of positive results given a positive final conclusion was 44 and the number of negative results given a negative final conclusion for the disease was 52.  There was 40 total conclusive tests of which 20 were positive final diagnoses and the other half were 20 negative final diagnoses.  There was 4 inconclusive tests and 8 no tests.


• Placing the drop in the middle of the light in order to give the most accurate picture


• Dispose the tip of the Micropipette each time to prevent the samples from contamination
'''What Bayes Statistics Imply about This Diagnostic Approach'''




Calculation Discussion:
Calculation Discussion:
Using the data collected from the entire class, we used the following formula P(A|B) = ( P(B|A) * P(A) ) / ( P(B) ) based on the values given to us ( P(A),P(B) and P(B|A) ) for calculation 1 and followed the same procedure for calculations 2, 3 and 4.  
Using the data collected from the entire class, we used Bayes Theorem, P(A|B) = ( P(B|A) * P(A) ) / P(B) to determine our class's attempt to detect disease SNPs and predict a patient's likelihood for developing the disease.  P(A|B) is the probability of event A given that event B has occurred.  P(A) is the probability that event A occurred.  P(B) is the probability that event B occurred.  P(B|A) is the probability of event B given that event A has occurred.  




Line 46: Line 79:
• Rounding to the wrong decimal places or rounding off too soon in the calculations
• Rounding to the wrong decimal places or rounding off too soon in the calculations
• Mixing the results of A and B or negative and positive
• Mixing the results of A and B or negative and positive
• Miscounting the POS and negative values  
• Miscounting the positive and negative values  
some of data were inconclusive  
Some of data were inconclusive and/or no data which could cause errors in the rest of the data
error might have propagated from wrong data from the different groups
Error might have propagated from wrong data from the different groups
 
 
 
'''Overview of the Original Diagnosis System'''
<!-- Instructions: Write a medium-length summary (~10 - 20 sentences) of how BME100 tested patients for the disease-associated SNP. Describe (A) the division of labor (e.g., 34 teams of 6 students each diagnosed 68 patients total...), (B) things that were done to prevent error, such as the number of replicates per patient, PCR controls, ImageJ calibration controls, and the number of drop images that were used for the ImageJ calculations (per unique PCR sample), and (C) the class's final data from the BME100_fa2014_PCRResults spreadsheet (successful conclusions, inconclusive results, blank data). -->
 


'''What Bayes Statistics Imply about This Diagnostic Approach'''


<!-- Instruction 1: In your own words, discuss what the results for calculations 1 and 2 imply about the reliability of the individual PCR replicates for concluding that a person has the disease SNP or not. Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small." Discuss at least three possible sources of human or machine/device error that could have occurred during the PCR & detection steps that could have affected the Bayes values in a negative way. -->
<!-- Instruction 1: In your own words, discuss what the results for calculations 1 and 2 imply about the reliability of the individual PCR replicates for concluding that a person has the disease SNP or not. Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small." Discuss at least three possible sources of human or machine/device error that could have occurred during the PCR & detection steps that could have affected the Bayes values in a negative way. -->


CALCULATION 1:
Calculation 1:


{| {{table}}
Here we found the probability of a positive final test, given a positive PCR reaction. This result was extremely close to 1 or 100%.
|-
| '''Variable''' || '''Description''' || '''Numerical Value'''
|-
| A || Positive final test conclusion  || 0.4375
|-
| B || Positive PCR reaction || 0.4167
|-
| P(B/A) || Probability of B given A || 0.9
|-
| P(A/B) || Probability of A given B || 0.9449
|}




CALCULATION 2:
CALCULATION 2:


{| {{table}}
Here we found the probability of a negative final test conclusion, given a negative diagnostic signal. This result was close to 1.
|-
| '''Variable''' || '''Description''' || '''Numerical Value'''
|-
| A || Negative final test conclusion || 0.5625
|-
| B || Negative PCR reaction || 0.54167
|-
| P(B/A) || Probability of B given A || 0.923
|-
| P(A/B) || Probability of A given B || 0.958
|}
 


<!-- Instruction 1: In your own words, discuss what the results for calculations 3 and 4 imply about the reliability of PCR for *predicting the development disease* (referred to as "diagnosis"). Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small."  -->
<!-- Instruction 1: In your own words, discuss what the results for calculations 3 and 4 imply about the reliability of PCR for *predicting the development disease* (referred to as "diagnosis"). Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small."  -->
Line 96: Line 99:
CALCULATION 3:
CALCULATION 3:


{| {{table}}
Here we found the probability of a positive disease diagnosis, given a positive final test conclusion. This result was close to 1 as well.
|-
| '''Variable''' || '''Description''' || '''Numerical Value'''
|-
| A || Patient Develops Disease  || 0.3529
|-
| B || Positive Final Test || 0.4411
|-
| P(B/A) || Probability of B given A || 0.8
|-
| P(A/B) || Probability of A given B || 0.651
|}




CALCULATION 4:
CALCULATION 4:


{| {{table}}
Here we found the probability of not developing the disease diagnosis, given a negative final test conclusion. This result was close to 1 as well.
|-
 
| '''Variable''' || '''Description''' || '''Numerical Value'''
|-
| A || Patient NOT Develops Disease  || 0.647
|-
| B || Negative Final Test || 0.529
|-
| P(B/A) || Probability of B given A || 0.27
|-
| P(A/B) || Probability of A given B || 0.3302
|}




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{| {{table}}
{| {{table}}
|-
|-
| Which calculation describes the sensitivity of the system regarding the ability to detect the disease SNP? || Calculation 3  
| Which calculation describes the sensitivity of the system regarding the ability to detect the disease SNP? || Calculation 1  
|-
|-
| Which calculation describes the sensitivity of the system regarding the ability to predict the disease? || Calculation 1
| Which calculation describes the sensitivity of the system regarding the ability to predict the disease? || Calculation 3
|-
|-
| Which calculation describes the specificity of the system regarding the ability to detect the disease SNP? || Calculation 4
| Which calculation describes the specificity of the system regarding the ability to detect the disease SNP? || Calculation 2
|-
|-
| Which calculation describes the specificity of the system regarding the ability to predict the disease? || Calculation 2
| Which calculation describes the specificity of the system regarding the ability to predict the disease? || Calculation 4
|}
|}


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<!-- Instructions: Write a short summary (up to five sentences) of the TinkerCAD tool and how you used it during the Computer-Aided Design lab -->
<!-- Instructions: Write a short summary (up to five sentences) of the TinkerCAD tool and how you used it during the Computer-Aided Design lab -->


We assembled the original Open PCR machine using TinkerCAD. We placed every part and used the tools to align them and produce the final part using all the pieces. Then, we changed the color to our taste. We decided that the size was an issue because we wanted it to be portable. As such, we made the machine smaller using the transformation tools, and so it is portable.
We assembled our OpenPCR machine using TinkerCAD. We placed every part and used the copy & paste, rotate, sizing, plane tools to align them and produce the final design of our MedTech 3000 using all the pieces. We changed the color to that of our company. We decided that the size was an issue because we wanted it to be portable. As such, we made the machine smaller using the transformation tools.


'''Our Design'''<br>
'''Our Design'''<br>
Line 155: Line 137:
<!-- Instructions: Under the image, write a short paragraph describing your design. Why did you choose this design? How is it different from the original OpenPCR design? --><br>
<!-- Instructions: Under the image, write a short paragraph describing your design. Why did you choose this design? How is it different from the original OpenPCR design? --><br>


We chose this design because we liked the colors; we placed the fans and heating system near each other (near the vents) for better cooling. We put two fans and two heating systems as well as a larger power source to power it more efficiently. The size of the machine was reduced in order to make it portable and convenient.
We chose Our MedTech 3000 OpenPCR machine because it will do PCR analysis (heating & cooling cycles) extremely fast thanks to our patented Krypton Nuclear Fast power plant which heats & cools anything inside our machine 8 times faster than conventional OpenPCR machines currently in the market and in a size that is half the size. There are no inefficient slow fans and heaters that take a long time.  
<br>
<br>


==Feature 1: Consumables Kit==
==Feature 1: Consumables Kit==
<!-- Instruction 1: Summarize how the consumables (liquid reagents and small plastics) will be packaged in your kit. You may add a schematic image. An image is OPTIONAL and will not get bonus points, but it will make your report look awesome and easy to score. -->
<!-- Instruction 1: Summarize how the consumables (liquid reagents and small plastics) will be packaged in your kit. You may add a schematic image. An image is OPTIONAL and will not get bonus points, but it will make your report look awesome and easy to score. -->
MedTech has revolutionized PCR analysis with our consumable kit which includes:  2 Terminator micropipettes made of titanium to last 450 yrs, 20 Nuke Proof Glass PCR Strip Tubes, 100 Organic Biodegradable pipette tips, (2) 1 L bottles of I'm Really Not Food PCR mix (organic and good for the environment), 1 bottle of Lonely & Looking Primers.  All consumables come in our biodegradable, recycled, plant based box to save the environment. 
All PCR strip tubes will be made of glass to lessen the contamination that petroleum based plastic pose.  Our Organic Biodegradable pipette tips are good for the environment because they are made of nutrients so when they break down the earth gets revived. 


<!-- Instruction 2: IF your consumables packaging plan addresses any major weakness(es), explain how in an additional paragraph. -->
<!-- Instruction 2: IF your consumables packaging plan addresses any major weakness(es), explain how in an additional paragraph. -->
Our Terminator micropipettes are super accurate, will last 450 yrs and reduce landfill mass because they don't break like plastic.  Our patented Nuke Proof Glass PCR Strip Tubes takes away any contamination that plastic naturally gives off can be used forever reducing landfill garbage.  Cost is lowered due to the volume of our production as well as our patented Nano Web Glass manufacturing process.  The cost is actually the same as plastic tubes and do not break.  These can be washed and autoclaved so they can be reused up to 300,000 times.  Our Organic Biodegradable pipette tips can actually be eaten after they have been underground for 6 days.  Our I'm Really Not Food PCR mix is so good for the environment that it can be drunk just like water and works incredible for PCR.  Finally, our patented Lonely & Looking Primers solution has super powers that "supercharge" your PCR reaction speed and yes it is also edible (tastes like Gerolsteiner water). 
As mentioned previously, our consumables packaging plan will reduce environmental toxic waste of "bad" plastics used in conventional pipette tips.


==Feature 2: Hardware - PCR Machine & Fluorimeter==
==Feature 2: Hardware - PCR Machine & Fluorimeter==
<!-- Instruction 1: Summarize how you will include the PCR machine and fluorimeter in your system. You may add a schematic image. An image is OPTIONAL and will not get bonus points, but it will make your report look really awesome and easy to score. -->
<!-- Instruction 1: Summarize how you will include the PCR machine and fluorimeter in your system. You may add a schematic image. An image is OPTIONAL and will not get bonus points, but it will make your report look really awesome and easy to score. -->


Our Hardware package includes:  (1) Nuklear Reaktor OpenPCR Machine, (1) Black Hole Fluorimeter, (1) Black Hole Phone Stand, (20) Black Hole Mars Slides
<!-- Instruction 2: IF your group has decided to redesign the PCR machine and/or Fluorimeter to address any major weakness(es), explain how in an additional paragraph. -->
<!-- Instruction 2: IF your group has decided to redesign the PCR machine and/or Fluorimeter to address any major weakness(es), explain how in an additional paragraph. -->


 
Our Nuklear Reaktor OpenPCR Machine is easy to use, good for the environment, poses no health risks and is 8 times faster than conventional machines currently in the market.  It is made of 6AL/4V titanium to last 450 yrs.  Due to our special Bio Reaktor inside our Nuklear Reaktor the fast timing of heating & cooling cycles is unprecedented and light does not come in when the door is closed.  Our patented Black Hole Fluorimeter system is so dark inside a vampire bat will get lost inside it.  No light comes in once the Black Hole doors close so you get near perfect results from our fluorimeter.  Inside is our earth friendly yet powerful fluorimeter. 
Our Nuklear Mars Slides have "Mars" like craters (indentations) to hold the drop of sample you will be analyzing inside our Black Hole Fluorimeter.  These slides are eco friendly and yes, they too can be eaten after 6 days underground. 




<!-- Do not edit below this line -->
<!-- Do not edit below this line -->
|}
|}

Latest revision as of 21:57, 21 April 2015

BME 100 Spring 2015 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


OUR COMPANY

MedTech LLC

Name: Walter C. Bregon
Role(s): Chief of Neurosurgery
Name: Abdurrahman Darwish
Name: Jenna Taras
Name: Nathan LeFort
Name: Eyerusalem
Name: Hau Nguyen


LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

We tested patients for the disease-associated DNA sequence or single nucleotide polymorphism (SNP) by first designing a SNP-specific primer pair which showed us how polymerase chain reaction (PCR) can be applied to detect diseased SNPs. Each team was given three replicate DNA samples from two patients and PCR reactions were ran on these samples. This was followed by determining if we could accurately measure different concentrations of DNA by first completing a calibration with our smart phone in order to calculate the amount of DNA. Principles that we used here were optical caustics, fluorescence, surface activity, ImageJ software, Excel linear fit and statistics and DIY devices. This was followed by using Bayesian statistics to determine the relibility of our attempt to detect disease SNPs and predict a patient's likelihood for developing the disease.

Division of Labor:

Our class had 26 teams of approximately five or six biomedical engineers in each team diagnosing and testing a total of 52 patients (2 per team) for a disease-associated DNA sequence or single nucleotide polymorphism (SNP).


Error Prevention:

• Using the black box to cover the sample droplet in order to protect the SYBR Green fluorescence from interacting with light which would lessen its capability of giving a visual color signal when dsDNA is present.

• Placing the sample/SYBR Green drop in the middle of the glass slide so the drop is pinned like a ball.

• Aligning the drop, by moving the slide, so that the blue LED light is focused by the drop to the middle of the black fiber optic fitting on the other side of the drop.

• Dispose the tip of the micropipettor each time to prevent the samples from contamination.

• Use correct micropipetting technique in order to get accurate volumes and no contamination.

• Using care in not moving the smartphone apparatus which would take it out of focus as well as change the distance from the phone to the drop.

• Making sure that the drop is focused on the smartphone before each image is taken.

• Each patient had three replicates of samples tested which would increase the reliability and accuracy of our data.

• The ovals in the ImageJ software need to be done as accurate as possible for each replicate.


Class Results:

There was a total of 132 Polymerase chain reactions (PCRs) for the whole class. These yielded 54 positive results for diseased single nucleotide polymorphisms (SNPs) and 67 negative results for this. The number of positive results given a positive final conclusion was 44 and the number of negative results given a negative final conclusion for the disease was 52. There was 40 total conclusive tests of which 20 were positive final diagnoses and the other half were 20 negative final diagnoses. There was 4 inconclusive tests and 8 no tests.


What Bayes Statistics Imply about This Diagnostic Approach


Calculation Discussion:

Using the data collected from the entire class, we used Bayes Theorem, P(A|B) = ( P(B|A) * P(A) ) / P(B) to determine our class's attempt to detect disease SNPs and predict a patient's likelihood for developing the disease. P(A|B) is the probability of event A given that event B has occurred. P(A) is the probability that event A occurred. P(B) is the probability that event B occurred. P(B|A) is the probability of event B given that event A has occurred.


Sources of Error:

• Rounding to the wrong decimal places or rounding off too soon in the calculations • Mixing the results of A and B or negative and positive • Miscounting the positive and negative values • Some of data were inconclusive and/or no data which could cause errors in the rest of the data • Error might have propagated from wrong data from the different groups


Calculation 1:

Here we found the probability of a positive final test, given a positive PCR reaction. This result was extremely close to 1 or 100%.


CALCULATION 2:

Here we found the probability of a negative final test conclusion, given a negative diagnostic signal. This result was close to 1.


CALCULATION 3:

Here we found the probability of a positive disease diagnosis, given a positive final test conclusion. This result was close to 1 as well.


CALCULATION 4:

Here we found the probability of not developing the disease diagnosis, given a negative final test conclusion. This result was close to 1 as well.



Final Results:

Which calculation describes the sensitivity of the system regarding the ability to detect the disease SNP? Calculation 1
Which calculation describes the sensitivity of the system regarding the ability to predict the disease? Calculation 3
Which calculation describes the specificity of the system regarding the ability to detect the disease SNP? Calculation 2
Which calculation describes the specificity of the system regarding the ability to predict the disease? Calculation 4

Computer-Aided Design

TinkerCAD

We assembled our OpenPCR machine using TinkerCAD. We placed every part and used the copy & paste, rotate, sizing, plane tools to align them and produce the final design of our MedTech 3000 using all the pieces. We changed the color to that of our company. We decided that the size was an issue because we wanted it to be portable. As such, we made the machine smaller using the transformation tools.

Our Design



We chose Our MedTech 3000 OpenPCR machine because it will do PCR analysis (heating & cooling cycles) extremely fast thanks to our patented Krypton Nuclear Fast power plant which heats & cools anything inside our machine 8 times faster than conventional OpenPCR machines currently in the market and in a size that is half the size. There are no inefficient slow fans and heaters that take a long time.

Feature 1: Consumables Kit

MedTech has revolutionized PCR analysis with our consumable kit which includes: 2 Terminator micropipettes made of titanium to last 450 yrs, 20 Nuke Proof Glass PCR Strip Tubes, 100 Organic Biodegradable pipette tips, (2) 1 L bottles of I'm Really Not Food PCR mix (organic and good for the environment), 1 bottle of Lonely & Looking Primers. All consumables come in our biodegradable, recycled, plant based box to save the environment.

All PCR strip tubes will be made of glass to lessen the contamination that petroleum based plastic pose. Our Organic Biodegradable pipette tips are good for the environment because they are made of nutrients so when they break down the earth gets revived.


Our Terminator micropipettes are super accurate, will last 450 yrs and reduce landfill mass because they don't break like plastic. Our patented Nuke Proof Glass PCR Strip Tubes takes away any contamination that plastic naturally gives off can be used forever reducing landfill garbage. Cost is lowered due to the volume of our production as well as our patented Nano Web Glass manufacturing process. The cost is actually the same as plastic tubes and do not break. These can be washed and autoclaved so they can be reused up to 300,000 times. Our Organic Biodegradable pipette tips can actually be eaten after they have been underground for 6 days. Our I'm Really Not Food PCR mix is so good for the environment that it can be drunk just like water and works incredible for PCR. Finally, our patented Lonely & Looking Primers solution has super powers that "supercharge" your PCR reaction speed and yes it is also edible (tastes like Gerolsteiner water). As mentioned previously, our consumables packaging plan will reduce environmental toxic waste of "bad" plastics used in conventional pipette tips.

Feature 2: Hardware - PCR Machine & Fluorimeter

Our Hardware package includes: (1) Nuklear Reaktor OpenPCR Machine, (1) Black Hole Fluorimeter, (1) Black Hole Phone Stand, (20) Black Hole Mars Slides


Our Nuklear Reaktor OpenPCR Machine is easy to use, good for the environment, poses no health risks and is 8 times faster than conventional machines currently in the market. It is made of 6AL/4V titanium to last 450 yrs. Due to our special Bio Reaktor inside our Nuklear Reaktor the fast timing of heating & cooling cycles is unprecedented and light does not come in when the door is closed. Our patented Black Hole Fluorimeter system is so dark inside a vampire bat will get lost inside it. No light comes in once the Black Hole doors close so you get near perfect results from our fluorimeter. Inside is our earth friendly yet powerful fluorimeter. Our Nuklear Mars Slides have "Mars" like craters (indentations) to hold the drop of sample you will be analyzing inside our Black Hole Fluorimeter. These slides are eco friendly and yes, they too can be eaten after 6 days underground.