BME100 f2016:Group7 W1030AM L6

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Owwnotebook icon.png BME 100 Fall 2016 Home
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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Name: Koop Bills
Name: Tyler Lent
Name: Maria Hanna
Name: Omar Maranon
Name: Israel Zaldivar



Bayesian Statistics

Overview of the Original Diagnosis System

The diagnosis system employed by our class consisted of fifteen teams, of five students each, running diagnostics on thirty patients. In order to reduce the occurrence of error, three samples from each individual patient were used in the PCR diagnostic tests. These samples were also compared to two control samples, one proven to contain the disease SNP and the other lacking the SNP. ImageJ, the software used to detect the fluorescent presence of SYBR Green in the samples, had procedural controls to reduce error by including samples with unadulterated water and overabundance of SYBR Green. Additionally, three images were captured of each sample for use in ImageJ's analysis software to achieve more accurate results.

The results of the class's diagnostic tests included positive, negative, and inconclusive conclusions. A total of seventy-eight PCRs were taken, twenty-three returned a positive result and forty-seven returned negative results. This left eight results, four of which were inconclusive and the other four blank. Of the thirty patients, seven received a positive diagnosis, sixteen a negative diagnosis, three found inconclusive, and four left blank. As this was the first time using ImageJ software, mistakes in its proper use resulted in inaccurate and unusable results.

What Bayes Statistics Imply about This Diagnostic Approach

Calculations one and two are fractional representations of the accuracy of a diagnostic PCR. Calculation one shows how accurate the PCR diagnostic test is at delivering reliable, truly-positive results. Calculation two shows, alternatively, how reliable and accurate a negative PCR result is in detecting a disease SNP. Both calculations provide a percentage, or more specifically a number between zero and one, that represents how reliable the PCR results are. The higher the number, the more reliable the results.

Similar to calculations one and two, calculations three and four give a percentage that represents how reliable a PCR is at giving accurate results. Except calculations three and four now depend not on whether a disease SNP is present or not. Instead the calculations relate the reliability of the PCR results to match the actual diagnosis of the patient. A high percentage of reliability in calculation three translates to positive DNA results more likely becoming positive cancer diagnosis. Likewise, a high reliability percentage in calculation four means that a negative DNA conclusion will likely result in a negative cancer diagnosis.

As this procedure of using PCR relied heavily on open-source software and individual equipment, the possibility for human or machine error is likely. If a students phone has poor pixel quality, it could negatively effect the analysis of the pictures used for analysis by ImageJ software. ImageJ software is open-source, and as such may have glitches or bugs that could negatively effect outcomes of diagnostic endeavor. Lastly, the potential for contamination of samples due to the environment and simple nature of equipment provided for the lab may alter the values of florescence collected.

Intro to Computer-Aided Design

3D Modeling

The software we used was a computer aided drafting system called solidworks. Solidworks has a very steep learning curve and is quite a challenge to just start off with. Each of our group members found it to be a bit overwhelming at first, however after becoming a bit more comfortable we did notice that it does start to become fun to use. Though one of the biggest hurdles that we need to overcome is figuring out how to properly model curves so that all of our parts don't come out so box-y. We also felt that there wasn't really enough time to develop the proper competence with the software prior to completing our redesign, nonetheless it was interesting.

Our Design


NOTE: For some reason our box design isn't loading properly, so will have to adjust this image if possible, however just know that it is supposed to be in an enclosed box with a lid with a round opening on the top.

Our design incorporates all the individual components that went with capturing images of the sample into a neat and simple system. The main difference is that all required components are now permanent fixtures in our device and that ideally our system would not require a lot of user knowledge to operate effectively. We choose this because we felt that having to set up the system yourself was excessive and raised the opportunity for user error.

Feature 1: Consumables

In our kit we would have to include several of the reagents; the SYBR Green, primer, and PCR mix solutions. We would also need to include a set (about 60) of glass slides, and some kind of data storage device e.g.: a micro SD card. Our device would require that the user have a micropipette however, we would want to keep costs down so we probably wouldn't want to include that with our unit this would also mean that anything else that is related to the micropipette would also not be included e.g: tips a waste bin for the tips, et cetera.

Feature 2: Hardware - PCR Machine & Fluorimeter

The open PCR machine will be included as is. AS a group we decided that there simply wasn't too much that was inefficient about it at the time to necessitate redesigning it. As such we would have to include the price of licensing the machine in to account. This also means that we would have to include information on how to use the open PCR machine and any necessary programs required to run the device. As for the fluorimeter, we would include it but in a modified fashion.

The modifications we would be making to the device would essentially package the Fluorimeter into a "dark-box" equipped with an attached camera so that no clumsy set up is required by the user. Our thought here was that firstly, the picture quality was fairly poor as most cell phone cameras don't usually operate in such dark environments. Also, while it was kinda of "fun" setting up the system, it also invites more and more room for error which can hurt your results. So by packaging all of the important components of the system together we reduce the opportunity for error and decrease the need for user expertise, as all the user would have to do is insert their sample into the the system from the top of the box.