BME100 f2016:Group14 W8AM L6

BME 100 Fall 2016	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 Error creating thumbnail: Unable to save thumbnail to destination Name: Diego Barra Error creating thumbnail: Unable to save thumbnail to destination Name: Savanna Bracale Error creating thumbnail: Unable to save thumbnail to destination Name: Casin Corallo Error creating thumbnail: Unable to save thumbnail to destination Name: Imelda Fragoza Error creating thumbnail: Unable to save thumbnail to destination Name: Andrew Samanta P-EZ-R LAB 6 WRITE-UP Bayesian Statistics Overview of the Original Diagnosis System In order to test patients for disease associates SNP 17 teams of 6 students each diagnosed 2 patients, for 34 patients total. Each team had three replicates for each of the patients and took three pictures of the drops during Lab D. Before actually testing the fluorescence of these drops, which contained the DNA of the assigned patients as well the SYBR Green 1 Solution, the process of obtaining these images by using Calf Thymus DNA was practiced instead of patients' DNA and using different concentrations of the Calf Thymus DNA. These three images were then used for ImageJ calculations. Image J was set to specific settings, these were area integrated density, mean grey value, and split channels. By having various test trials for each unique PCR sample, the error decreases as there is more reliability as to whether the correct calculations were made. Each of the group's data was organized into a spreadsheet and analyzed. Two groups had inconclusive results because they did not submit their result for whether or not their patients had the SNP for the disease. From group 14 data, it can be deduced that the first patient had the disease SNP while the second patient did not. A possible error that could have affected data could have been mixing up the order of the images whih would later on affect the results that came from imageJ. What Bayes Statistics Imply about This Diagnostic Approach The calculations used to determine whether a patient 1 and 2 had the disease SNP or not are based on the numerical data that came from imageJ. Errors in calculations were not made by imageJ or the final data because excel was used. If a patient had a result close to 1.00, then there was a 100% chance, base on Bayesian Statistics, that this patient has the disease SNP. If a patient has a result of a value lower than 1.00 their chance of having the disease SNP is lower. For example, if a patient has a result value of 0.30 then they only have a 30% chance of having the disease SNP, which is a very small chance. Three possible sources of human or machine/device error that could have occurred during the PCR & detection steps that could have affected the Bayes value in a negative way include unfocused images, inaccurate size drops, and not setting the thermocycler to the correct temperature settings. If the phone or webcam used to take images of the drops was unfocused and took blurry pictures then the size of the drop and the calculations of from imageJ would be distorted and incorrect. If the pipette that was used to transfer the patients DNA or SYBR green solution was used incorrectly and did not transfer all of the solution then te size of the drops would be smaller than intended and the ration of SYBR green fluid to patient DNA would be incorrect and the immageJ calculations would be skewed. It is important for the correct temperature settings to be set on the thermocycler in order for this lab to be successful. If the incorrect settings are set the DNA would not separate or replicate. Intro to Computer-Aided Design 3D Modeling The team used Solid Works in order to come up with a new design. This was chosen over TinkerCad because members of the group had experience with using Solid Works. Solid Works software appears to be more professional than using TinkerCad. TinkerCad is speciliazed more towards kids making the process easier but less professional. Overall, Solid Works is the better software to use to design a product. Our Design The new design is very similar to the original PCR design. It differs with a phone slot. This makes the whole PCR testing more efficient. Another reason why this was the chosen design was because there was a struggle in not having a phone slot in the Open PCR design. Feature 1: Consumables These "very important" consumables are the items that are best handled and prepared for experiments, like the PCR mix, primer solution, or SYBR Green Solution. Other items need for the experiment are items that probably don't need as much care, although one does need to make sure that all items are sterilized so that it doesn't affect the outcomes of the experiment. Items not considered "very important" would be those that don't necessarily have to be handled by biotech professionals and scientists. Feature 2: Hardware - PCR Machine & Fluorimeter The Open PCR machine and the fluorimeter will be kept the same in our design. The only thing being changed is an improved phone slot which is used in the flourimeter process. The decision to improve this came from experiencing difficulty when doing the PCR tests and setting up a cell phone to take focused images of the drops. The slot will enable phones of any size to easily take focused photos when doing the PCR testing.