BME100 f2018:Group8 T1030 L6

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Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
Lab Write-Up 4 | Lab Write-Up 5 | Lab Write-Up 6
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OUR TEAM

Name: Ty Promreuk
Name: Sabrina Worley
Name: Sonja Brett
Name: Jaad Waters

Our Brand Name

LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

There were 17 teams of 4-5 students each, and we diagnosed 34 patients total. For each patient, we had three replicates. In order to further prevent error, we used positive and negative controls, in order to compare them to our patients. Our ImageJ calibration controls were used to ensure that ImageJ was working properly and would provide accurate results for our patients. Using three images per patient allowed us to ensure that the ImageJ images were accurate. There were a few inconclusive test results from the experiment; however, the inconclusive test results did not affect the conclusion of the PCR test. As a result of the inconclusive test results, there were inconclusive diagnoses for the patients. There was blank data for Group 1, which may have skewed the test results. We also encountered problems when micropipetting the soultions into the vials. This may have caused a mistake in our data and therefore cause a mistake in the class data.


What Bayes Statistics Imply about This Diagnostic Approach


The calculations 1 and 2 show the reliability of the test results in concluding whether or not patients have the disease SNP. Because the values we calculated for P(A|B) are close to 1.00 (100%) for both calculations 1 and 2, we can say that these results were reliable in diagnosing patients.


The P(A|B) value for calculations 3 implies that the reliability of the PCR test in diagnosing patients is low, under 50%. However, for calculation 4, the reliability of the PCR test is closer to 100%; therefore, it is a reliable test to predict development of the disease.


When calculating the area, mean, and RAWINTDEN data through ImageJ, variations in oval sizes due to human error could have occurred. Placement of the oval in the backdrop could have also caused error. Additionally, when calculating the final PCR concentrations, we may have incorrectly calculated values in Excel. Finally, rounding data values may have caused additional errors in calculated Bayesian statistical values.

Intro to Computer-Aided Design

3D Modeling


Our Design



We chose this design because we had difficulties with stacking the cartons and positioning the phone to make the light box high enough for us to capture pictures. We decided to solve this problem by adding a hinged phone stand attached to the lightbox itself, and an adjustable tripod stand also attached to the lightbox.


Feature 1: Consumables

Feature 2: Hardware - PCR Machine & Fluorimeter