BME100 f2018:Group9 T1030 L6

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
OUR TEAM Name: Brian Aguilar Name: Karen Ruiz Name: Krissian Hargreaves Name: Christopher Ortiz Silva Name: Tabitha Keever Dunder Miflin PCR COmpany LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System In the lab 17 groups were assigned two different patients, so a total of 34 patients, and our job was to determine if the patients genes contained the SNP of a disease associated with Parkinson's. We used PCR (Polymerase Chain Reaction) to replicate the copy of the SNP in order to be able to detect if it was present in the patient’s DNA sample. Every group was given a positive and negative control to help prevent error and have a comparable result to definite controls. Three samples from each patient was also given to help prevent error with false results or contamination. Three pictures were taken for each sample to reduce error and increase reliable. The images were analyzed through ImageJ . The droplet was outlined in order to have better results from the analysis on ImageJ. From the data collected, the class had 14 patients test positive, 17 patients test negative, one patient’s test was inconclusive, and one group did not provide data for their tests. There could have also been discrepancies in the micropipetting resulting in various measurements and inconsistencies in the amount of DNA. What Bayes Statistics Imply about This Diagnostic Approach Calculation 1 was “What is the probability that a patient will get a positive final test conclusion, given a positive PCR reaction?” and the number was 0.94. The equation to find this was (0.90 x 0.44)/(0.42). 0.90 is the frequency of total pos with POS, 0.44 is the frequency of total POS conclusions, and 0.42 is the frequency of the total pos PCRs. Calculation 1 shows the sensitivity of the system regarding the ability to detect the disease SNP. Calculation 2 was “What is the probability that a patient will get a negative final test conclusion, given a negative diagnostic signal?” and the number was 0.98. The equation to find this was (0.94 x 0.53)/ (0.51). 0.94 is the frequency of toal neg with NEG, 0.53 is the frequency of total NEG conclusions, and 0.51 is the frequency of total neg PCRs. Calculation 2 describes the specificity of the system regarding the ability to detect the disease SNP. Calculation 3 was “What is the probability that a patient will develop the disease, given a positive final test conclusion?” and the number was 0.33. The equation to find this was (0.43 x 0.34)/(0.44). 0.43 is the frequency of total “yes” diagnoses with POS conclusion, 0.34 is the frequency of total “yes” diagnoses, and 0.44 is the frequency of total POS conclusions. Calculation 3 describes the sensitivity of the system regarding the ability to predict the disease. Calculation 4 was “What is the probability that a patient will not develop the disease, given a negative final test conclusion? and the number was 0.96. The equation to find this was (0.77 x 0.66)/(0.53). 0.77 is the frequency of total “no” diagnoses with NEG conclusions, 0.66 is the frequency of total “no” diagnoses, and 0.53 is the frequency of total NEG conclusions. Calculation 4 describes the specificity of the system regarding the ability to predict the disease. Intro to Computer-Aided Design 3D Modeling Our team used the Solidworks Program to create a 3d rendition of our design. We felt this would aid our needs bets as we had experience in this because of similar coursework going on at the same time. It also allowed us to improve our skills as we were challenged to think creatively. It was a little difficult at times, mainly because Solidworks had a lot of functions that we were inexperienced with, but the basic ones were familiar to us. Solidworks was definitely an easy method to to use when it comes to the visualization of our design. It would have been tedious to try and describe it to a person without a picture aid. Our Design File:PhoneClampAssembly.PDF File:PhoneClampAssemblyDrawing.PDF We redesigned the Fluorimeter system by adding a clamp that moves up and down and side to side, so there does not have to be a constant readjustment and the phone is easy to move on our system to get the right angle for the photos. The notches allow for manual readjustment of the height of the phone box to account for differing phone sizes. By moving the phone box side to side, the camera is able to be positioned right in front of the samples as well. Feature 1: Consumables Our kit will contain the same components that the original kit contained, including PCR mix, primer solution, SYBR Green solution, buffer, plastic tubes, and glass slides. These are all essential to using the product appropriately and are all built to fit the machine. A micropipette and micropipette tips are also needed but these are not specific to our design. Consumables: plastics, pipettor, and reagents (PCR mix, primers) The OpenPCR machine and software The Fluorimeter system (including slides, stand, etc.) STRENGTH: Easy to use, cheap, disposable STRENGTH: simple to use STRENGTH: simple use WEAKNESS: Micropipette: inaccurate WEAKNESS: limited number of samples WEAKNESS: constant reassembly after taking pictures, weak light source We discussed that the consumables are easy to use, but the micropipettes can be inaccurate. The OpenPCR machine and software is simple to use, but there is a limited number of samples that it can run. The Fluorimeter system is simple to use, but there has to be constant reassembly after taking pictures and there is a weak light source. Feature 2: Hardware - PCR Machine & Fluorimeter The Open PCR machine will be kept as is because innovating it is not our main focus. Although there are definitely issues with it, we chose to focus on the fluorimeter process. The fluorimeter will be used to hold the phone so that it can hold the phone. The actual data will be calculated using the PCR machine's utilities for the DNA. After that, the fluorimeter process will come into play. The major weakness that we described for the consumables was that the micropipettes are inaccurate. We did not choose to incorporate this in our design. The major weakness we described for the hardware of the PCR machine was that there is a limited number of samples in the PCR Machine. We could have redesigned the PCR machine to improve this weakness by having more slots in the PCR Machine to hold more samples, but we only focused on changing the Fluorimeter in our design. The major weakness we described for the hardware Fluorimeter was that there had to be constant reassembly after taking a picture and there was a weak light source. We redesigned the Fluorimeter system by adding a clamp that moves up and down and side to side, so there does not have to be a constant readjustment and the phone is easy to move on our system to get the right angle for the photos.