OUR COMPANY

Name: Daniel GaytanJenkins





LAB 6 WRITEUP
Bayesian Statistics
Overview of the Original Diagnosis System
For this lab, 17 teams of approximately 6 students each worked to diagnose a total of 34 patients. Each team was assigned two patients to complete three PCR trials on. In each group, one or two team members would handle wet lab duties such as pipetting samples and working with the fluorimeter while the remaining team members focused on ImageJ analysis.
Several measures were taken to prevent error while working, including using positive and negative PCR control samples. The positive control came from someone who is known to have the disease SNP in question, while the negative control came from someone who is known to lack that SNP. These allowed the group to set baselines for positive and negative results, as even negative results yielded positive results for the fluorescence calculations. In addition, completing three trials for each patient ensured that our results were reliable and made it easy to see whether or not a result was anomalous. Using three drop images for each trial further increased our confidence in the results and helped prevent error. In ImageJ, the same size of ellipse was used for each drop image so as to maintain the number of pixels to ensure image processing was consistent.
In our final data, there were 16 successful conclusions (where the conclusion matched the disease diagnosis) and 16 unsuccessful conclusions (that got a negative conclusion for someone with the disease, a positive conclusion for someone without the disease, or were inconclusive). Two results were inconclusive. One team failed to process their PCR samples and contribute their results, so two patients do not have any test results.
Final Data:
Patient ID

Clinician

Disease (diagnoses)

'

CONCLUSION

11316 
10am 1 
no 

NEG

25353 
10am 1 
no 

POS

15062 
10am 2 


NO TEST

95748 
10am 2 


NO TEST

27468 
10am 3 
no 

NEG

35294 
10am 3 
no 

NEG

19902 
10am 4 
yes 

POS

33338 
10am 4 
yes 

NEG

59797 
10am 5 
no 

NEG

79049 
10am 5 
no 

POS

73039 
10am 6 
no 

POS

40429 
10am 6 
yes 

INCONCLUSIVE

71504 
10am 7 
yes 

POS

96389 
10am 7 
no 

NEG

74239 
10am 8 
yes 

NEG

82959 
10am 8 
no 

NEG

96837 
10am 9 
no 

INCONCLUSIVE

39617 
10am 9 
yes 

NEG

58575 
10am 10 
no 

POS

86157 
10am 10 
no 

NEG

91905 
10am 11 
no 

POS

11553 
10am 11 
no 

POS

16819 
10am 12 
no 

NEG

59134 
10am 12 
yes 

POS

75239 
10am 13 
no 

POS

75444 
10am 13 
yes 

NEG

11105 
10am 14 
no 

NEG

22923 
10am 14 
no 

NEG

10088 
10am 15 
no 

POS

28866 
10am 15 
yes 

POS

62525 
10am 16 
no 

NEG

86787 
10am 16 
no 

NEG

52130 
10am 17 
no 

POS

17921 
10am 17 
yes 

NEG


Bayes Tables:
Variable

Description

Numerical Value

A 
a positive final test conclusion 
0.40625

B 
a positive PCR reaction 
0.447916667


the probability that a patient will have a positive PCR reaction given a positive final test conclusion 
0.846153846


the probability that a patient will have a positive final test conclusion given a positive PCR reaction 
0.76744186


Variable

Description

Numerical Value

A 
a negative final test conclusion 
0.617647059

B 
a negative diagnostic signal 
0.552083333


the probability that a patient will have a negative diagnostic signal given a negative final test conclusion 
0.823529412


the probability that a patient will get a negative final test conclusion given a negative diagnostic signal 
0.921329242


Variable

Description

Numerical Value

A 
disease development 
0.3125

B 
a positive final test conclusion 
0.40625


the probability that a patient will receive a positive final test conclusion given that a patient develops the disease 
0.125


the probability that a patient will develop the disease given a final positive test conclusion 
0.096153846


Variable

Description

Numerical Value

A 
no disease development 
0.6875

B 
a negative final test conclusion 
0.59375


the probability that a patient will receive a negative final test conclusion given that the patient does not develop the disease 
0.375


the probability that a patient will not develop the disease given a negative final test conclusion 
0.434210526


What Bayes Statistics Imply about This Diagnostic Approach
Calculation 1 implies that close to 80% of patients with a positive PCR reaction will receive a positive final test conclusion, and that the reverse is also true: the probability that a patient with a positive final test conclusion has a positive PCR reaction is close to 80%. In other words, close to 80% of people for which the PCR test comes back positive will be concluded to have the SNP, and people who are concluded to have the SNP will receive positive PCR tests around 80% of the time. Similarly, calculation 2 implies that people with negative final test conclusions will receive negative PCR reaction results about 80% of the time, while patients who receive a negative PCR result will get a negative final test conclusion about 90% of the time. This slightly higher percentage may indicate that the class' results favored negative test conclusions.
Calculation 3 implies that the probability that a patient who actually has the SNP and is diagnosed with the disease will receive a positive final test conclusion is actually quite small  around 10%  indicating that the class' final conclusions were not very effective at predicting whether or not the disease would develop. In addition, the probability that a patient with a positive final test conclusion would develop the disease was similarly low. This, unfortunately, implies that false positives were common. Calculation 4 indicates that a patient without the disease will receive a negative final test conclusion was close to 40%, and that a patient with a negative final test conclusion will not develop the disease close to 40% of the time. Therefore, negative test conclusions also seemed to be fairly poor predictors of disease development.
One possible source of error in this investigation is contamination. If any groups reused pipette tips by mistake, they could have false positives in their results. Another is mislabeling tubes, which could have led to either false negatives or positives because the results would not correspond to the indicated label. In addition, if any of the groups incorrectly set up the machines, they could have followed the wrong protocol and gotten inaccurate results.
Intro to ComputerAided Design
Existing Design Assessment
Category

Consumables

OpenPCR Machine and Software

Fluorimeter System

Strengths 
Cheap, reliable 
Small, compact and easy to use 
Very efficient for a small price

Weaknesses 
Difficult to label and organize 
Limited number of test tubes and results take longer that other OpenPCR machines 
It's hard to get a precise measurement and picture since we are using very simple material like improvised stand for the smartphone camera and the box to cover the experiment. The distance between the droplet and the camera was really hard to keep steady and the box wouldn't perfectly avoid all light.

Planning
Branding

Positioning

Target Markets

Messaging

Place

Finally PCR for the masses. PCR method of copying DNA molecules that now can be done at your own desk. With our new and improved fluorimeter the data collected from analysis will be more accurate. We have reconstructed the fluorimeter to reduce the variable affecting your data. With an adjustable attached camera stand that provides a more secure and accurate stand for photography. The box cover is also attached to the fluorimeter and weighed down at the base to avoid any movement that may effect the accuracy of data. 
Our product will be cheaper and made of stronger material than typical PCR units. Our PCR unit will be easy to follow and will contain detailed instructions 
Our target market will be for anyone that uses Open PCR machines. Our product is made to be simple to use and is very self explanatory and doesn't need a lot of teaching before use. 
Our target market in high school and university students. We are providing portable and accurate PCR machine that are easy to use for all. With simple steps and procedures, any high school or college student can easily operate this. Little previous understanding is needed to operate it and it is a very economically wise investment to improve a student education.

 PCR is used in a multitude of places, ranging from hospitals that attempt to treat infectious diseases, to areas of forensics, and many research labs that deal with DNA sequencing and cloning.

TinkerCAD
TinkerCAD is a simple 3D design and 3D printing that can be used by all with its simple and all encompassing array of tools. TinkerCAD provides an easy to use design studio for any and all. The essence of TinkerCAD is the use of basic building blocs and tools to construct simple or complex designs. With two simple design tools, adding shapes to your design as solids or holes and combining shapes together, creating any 3 dimensional design is at your finger tips. forming a new shape.TinkerCad also provides you with preexisting shapes as well as the ability to upload your own. It is also possible to import a 2D image and convert it in to a 3D design that is printable.
Our Design
The black box, fluorimeter and camera stand were connected together to form a more secure system that reduces the variability in the data. The whole set up can also be disassembled and placed inside the black box for easy storage
Feature 1: Consumables
Feature 2: Hardware  PCR Machine & Fluorimeter
From our experience using the open PCR machine and the fluorimeter and others experience, we found the PCR machine to meet and and exceed portable PCR standards. This PCR machine provided us with accurate results with the variations in results due to variations in samples not inconsistent performance by your thermocycler. The compatibility with computers is also handy providing users with easy to use software where users can adjust the time and the temperature and with in hours you will have plenty of DNA for sorting and sequencing.
However the fluorimeter that was used to analyze the the samples seemed to have far too many variables to be accounted for. With the fluorimeter, there were three separate moving parts to the fluorimeter: the camera stand, the actual fluorimeter, and the dark box. These three moving parts were not connected causing the high possibility of the movement of any part effecting the other moving parts position. This can affect the accuracy of the data. The camera stand, which does not stand hold the camera in the first place, had to be placed 9 to 11 inches way from the fluorimeter. The black box had to removed to place a sample on the fluorimeter and placed over the fluorimeter and camera stand to reduce outside light. This process of removing and placing the box back on did in fact bump the fluorimeter, camera stand set up, altering the set up and in the end slightly altering our data.
In order to prevent this, we decided to connect the black box to the fluorimeter and the camera stand with detachable and adjustable connections combined with a weighed base to the fluorimeter to prevent unnecessary movement. The black box would be connected to the back of the fluorimeter through a rotating hinge that would allow the user to pull back the black box and have it rest on its back side face. This would reduce the ill effects of having to constantly remove and place the black box. For the stand we created a a more mobile phone friendly stand that can be adjusted to the proper distance, in inches, from the fluorimeter. This whole set up can disassembled and placed in the black box for easy storage.
Open PCR. No changes were made to the PCR machine
The black box, fluorimeter and camera stand were connected together to form a more secure system that reduces the variability in the data.
