BME100 f2015:Group3 1030amL6

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JOSIE AND THE PUSSYCATS

Name:
James Wood
Name:
Jennifer Le
Name:
Shannon Grassi
Name:
Noah Pollack
Name:
Lauren Butler
Name:
Carlos Garrido


LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

The BME100 lab group tested patients for the disease-associated SNP through a process called polymerase chain reaction, PCR. After DNA samples were taken from patients, each of the 17 BME teams of 6 students were given 2 patients' DNA samples, 34 patients total, to prepare for the PCR process in the OpenPCR machine. After the PCR process was completed, the PCR samples were diluted with a buffer solution and placed in PCR reaction tubes, which were then placed in a fluorimeter. Three pictures were taken for each diluted PCR product + SYBR Green I trial to prevent error. The images obtained from the PCR products in the fluorimeter were then put on a program called "ImageJ", where the Area, Mean Pixel Value, and RAWINTDEN data were collected for each image of the diluted PCR drop. The background of each image RAWINTDEN values were also collected and subtracted from each RAWINTDEN values of the drop for each PCR product and its respective image, like so: (RAWINTDENdrop) - (RAWINTDENbackground). The mean of all of the final values from (RAWINTDENdrop) - (RAWINTDENbackground) were taken in addition to their standard deviations. The data collected from the PCR products' RAWINTDEN, mean, and standard deviation were compared to the most reliable calibration curve of the calf thymus samples. Using the calibrator data, how much DNA that was produced by PCR was discovered. All data currently collected were complied onto a 5x9 table on Excel with the labels "PCR Product TUBE", "MEAN (RAWINTDENdrop-RAWINTDENbackground), "PCR Product Concentration", "Total Dilution", and "Initial PCR Product Concentration [(Product Concentration)*(Total Dilution)] and uploaded to "BME100_Fa2015_PCRresults" spreadsheet so that all 17 BME team's data could be arranged to determine the Bayesian statistics of the DNA results.

Several measures taken during the lab process to prevent error were the number of replicates per patient, the additon of PCR negative and positive control samples, ImageJ calibration controls when taking the area, mean px value, and RAWINTDEN for each PCR and calf thymus drop amounts, and taking 3 drop images for the ImageJ calculations (per unique PCR sample).

From the class's final data from the "BME100_Fa2015_PCRresults" spreadsheet, there were 30 successful conclusions, 2 inconclusive results, and 2 blank data (listed as "NO TEST") as shown:

BME100 Group3 ClassDNA.PNG


What Bayes Statistics Imply about This Diagnostic Approach


Calculations 1 and 2 implied that that the reliability of the individual PCE replicates for concluding that a person has the disease SNP was very high. In calculation 1, it showed that there was a close to 0.85 (84.6%) chance that there would be a positive PCR given a positive final test conclusion and a close to 0.77 (76.7%) chance of a positive final test given a positive PCR reaction, demonstrating that correctly diagnosing a patient with the disease has a very high probabilities.

In calculation 2, there was nearly a 90% chance (89.4%) that there would be a negative diagnostic given a negative conclusion result and over 95% chance (96.9%) of a negative final test given a negative diagnostic signal. Calculation 2 results are also very high in predicting correct results of patients.

Calculation 1: What is the probability that a patient will get a positive final test conclusion, given a positive PCR reaction?

BME100 Group3 Calc1.PNG

Calculation 2: What is the probability that a patient will get a negative final test conclusion, given a negative diagnostic signal?

BME100 Group3 Calc2.PNG

Calculations 3 and 4 implied that that the reliability of the individual PCR replicates for concluding that a person has the disease SNP was highly varied and not accurate. In calculation 3, it showed that there was a small chance (30.8%) that there would be a positive PCR given a that the patient will develop the disease and an even smaller chance (23.7%) that a patient will develop the diesase given a positive final test conclusion. Calculation 3 had very little accuracy in correctly detecting if the patient had the disease.

For calculation 4, there was over a little over 50% (63.2%) chance that there will be a negative final test conclusion given that the patient will not develop the disease and about 80% (81.7%) probability that a patient will not develop a disease given a negative final test conclusion, demonstrating that correctly diagnosing a patient with the disease has an average chance of success. Calculation 4 had better results than calculation 3 in detecting patients without the disease and giving the correct diagnosis.

Calculation 3: What is the probability that a patient will develop the disease, given a positive final test conclusion?

BME100 Group3 Calc3.PNG

Calculation 4: What is the probability that a patient will not develop the disease, given a negative final test conclusion?

BME100 Group3 Calc4.PNG

Some possibile sources of human error are groups not inputting any data into the final spreadsheet and if any groups had inaccurate data, thus skewing the results of the Bayes values.

Intro to Computer-Aided Design

TinkerCAD
TinkerCAD tool allows our team to manipulate 3-D drawings. This allows us to design our product (open PCR). We imported various files on current PCR machines which we were able to gain ideas from and edit in the process of making our own PCR machine. The parts we downloaded included: lid, body, electronics, and miscellaneous. Using these parts we were able to find design flaws and implement a solution for part F(2).

Our Design

ModifiedImaging.png ModifiedImaging2.png

These images show our design for a modified fluorimeter from an aerial and frontal perspective omitting the cover and flap on the front. The camera is mounted into the rear wall in a fixed position level to the slide. The slide is mounted on rails for adjusting distance from the lens. The camera allows for stable bursts of images that can be loaded directly onto the computer and ideally the image J software from the device using a USB cable.




Feature 1: Consumables

PCR Consumables Kit:

  • Plastics (includes test tubes, pippetor tips, etc)
  • Pipettor (hand-held device form)
  • Reagents (PCR mix and Primers)

We will keep consumables the same. We believe the strength within consumables is their ease of use (we as freshmen were able to quickly learn and use them). Too, they are cost efficient.


Feature 2: Hardware - PCR Machine & Fluorimeter

OpenPCR:

  • We saw it would be difficult to increase its efficiency by decreasing time for the machine and software to be run. The only improvement we saw that could be made would be increasing the size to accommodate more reactions, although this is not necessary or practical. This being said; we will not be changing the OpenPCR machine or software.

Fluorimeter system:

  • We will include an interior integrated camera that can take multiple pictures (burst) and send data to a laptop or tablet. This will eliminate the inconsistency with user error in closing/ opening the light box and allow photographs to be clearer.
  • Furthermore, we propose a track-like system with locking mechanism to make sure the slide can be easily removed as well as set at a certain distance away from the camera. This will also prevent liquids from being spilled or manipulated by users on the slide.