BME100 f2015:Group12 1030amL6

BME 100 Fall 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 Name: Morgan Baerwaldt Name: Daniel Gaytan-Jenkins Name: Cory Kehoe Name: Pedro Lopes Name: Brittany Metzler Name: Jaxon Lewandowski LAB 6 WRITE-UP 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 Computer-Aided 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 Our Brand Name: HALO 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 pre-existing 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.