BME100 f2014:Group26 L6

BME 100 Fall 2014	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 Ambike Bhraguvanshi Timothy Chen Andrew Polson Rachel Ponstein Rebecca Schiavone Jiaqi Wu LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System In this experiment, 68 patients were tested for a disease marker to predict their likelihood of developing the disease. These patient samples were divided among 34 teams in the BME 100 class. Each team, which consists of 6 members, received three replicates of each of the patient's data and two controls. Within the lab groups, the students worked together to perform image analysis. Errors were attempted to be prevented through multiple avenues, including: quantitative results and controls. The replicates of each patient's DNA prevented error, because it allowed for more quantitative data. In addition, there were PCR controls (+/- control samples) that were used in ImageJ to calibrate. The ImageJ calibration was based on the controls. The controls allowed for a baseline to refer to when analyzing patient data. To ensure accuracy, three drop images were analyzed through ImageJ calculations for each patient, and for each control. For the final data, of the 68 tests run, 8 of the results were inconclusive, 6 of the results were blank data and the remaining 54 results led to successful conclusions. What Bayes Statistics Imply About This Diagnostic Approach For Calculation 1, the probability result reflected that the Bayes value is close to 1.00. For the second Calculation, the probability also reflective of the Bayes value being close to 1.00. Based on these values, it can be concluded that the person has the disease SNP. Since these results Error that may have resulted during the PCR & detection steps could be that the samples were exposed to too much light. Another could be incorrect analysis of the samples using ImageJ. Another possible source of error could be an inadequate photo quality. For Calculations 3 and 4, the Bayes values are very small, indicating that the PCR is not very reliable for predicting the development of the disease, because the person will most likely not get the disease. Computer-Aided Design TinkerCAD TinkerCAD is a 3D computer-aided design (CAD) tool, which operates completely on the Internet. Using a variety of tools, such as highly adjustable shapes and symbols, and grouping and color tools, 3D models can be designed on TinkerCAD and actually produced as objects through 3D printing. To design the OpenPCR machine, various STL (STereoLithography) files representing different parts of the machine were imported from Thingiverse, a website used to share digital design files, to TinkerCAD. The tools on TinkerCAD were then utilized to create a newly designed OpenPCR machine that addressed the limitations that existed with the original design. The components of the consumables kit and fluorimeter were designed entirely on TinkerCAD without the use of imported STL files from Thingiverse. Our Design Feature 1: Consumables Kit Feature 2: Hardware - PCR Machine & Fluorimeter