BME100 f2014:Group3 L6: Difference between revisions

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<!-- Instruction 1: In your own words, discuss what the results for calculations 1 and 2 imply about the reliability of the individual PCR replicates for concluding that a person has the disease SNP or not. Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small." Discuss at least three possible sources of human or machine/device error that could have occurred during the PCR & detection steps that could have affected the Bayes values in a negative way. -->
<!-- Instruction 1: In your own words, discuss what the results for calculations 1 and 2 imply about the reliability of the individual PCR replicates for concluding that a person has the disease SNP or not. Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small." Discuss at least three possible sources of human or machine/device error that could have occurred during the PCR & detection steps that could have affected the Bayes values in a negative way. -->


Calculation 1: This shows how accurate the test is to detect a positive result for SNP (sensitivity). The result for this test showed that the probability of a patent getting a positive result for having SNP from the PCR machine given that they have the disease is 89%.
Calculation 1: This shows how accurate the test is to detect a positive result for SNP (sensitivity). The result of this test shows that the probability of a patent getting a positive result for having SNP from the PCR machine given that they have the disease. This value is fairly close to 1.00, implying that the individual PCR replicates are mostly reliable.


Calculation 2: This shows how accurate the test is to detect a negative result for SNP (specificity). The result for this test showed that the probability of a patent getting a negative result for having SNP from the PCR machine given that they do not have the disease is 77%.
Calculation 2: This shows how accurate the test is to detect a negative result for SNP (specificity). The result for this test shows that the probability of a patent getting a negative result for having SNP from the PCR machine given that they do not have the disease. The calculated value for this probability is fairly close to 1.00, indicating that the reliability of the diagnosis of this test is fairly high.


In other words, both of these show accuracy but also have room for improvement. This room for improvement comes from the error that occurred during the lab. These were a result from both human error and device error. Human errors include error in the preparation for the test. Specific tests that were shown to be positive should have displayed a negative result. This could be as a result from mixing up the samples. In the case for our group, the negative control (in other words, the sample that we knew should have displayed a negative result for the disease) displayed a positive result for SNP. The mix up in the samples probably occurred before we actually deceived the samples. This error could have also been a result of improper disposal of pipet tips before and after each use during preparation, thus causing a mix of samples. Device error includes not running the same amount of cycle in the open PCR machine. Each group's machine ran a different amount of cycles and some machines simply did not work at all. Receiving a positive result for our patient sample when it should have been negative left error in the Bayes values.
In other words, both of these show accuracy but also have room for improvement. This room for improvement comes from the error that occurred during the lab. These were a result from both human error and device error. Human errors include error in the preparation for the test. Specific tests that were shown to be positive should have displayed a negative result. This could be as a result from mixing up the samples. In the case for our group, the negative control (in other words, the sample that we knew should have displayed a negative result for the disease) displayed a positive result for SNP. The mix up in the samples probably occurred before we actually deceived the samples. This error could have also been a result of improper disposal of pipet tips before and after each use during preparation, thus causing a mix of samples. Device error includes not running the same amount of cycle in the open PCR machine. Each group's machine ran a different amount of cycles and some machines simply did not work at all. Receiving a positive result for our patient sample when it should have been negative left error in the Bayes values.
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<!-- Instruction 1: In your own words, discuss what the results for calculations 3 and 4 imply about the reliability of PCR for *predicting the development disease* (referred to as "diagnosis"). Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small."  -->
<!-- Instruction 1: In your own words, discuss what the results for calculations 3 and 4 imply about the reliability of PCR for *predicting the development disease* (referred to as "diagnosis"). Please do NOT type the actual numerical values here. Just refer to the Bayes values as being "close to 1.00 (100%)" or "very small."  -->


Calculation 3: This shows how accurate the PCR machine is to predict the disease in a patient that would develop the disease. The test was not accurate given that it had a 43% efficiency rate.
Calculation 3: This shows how accurate the PCR machine is to predict the disease in a patient that would develop the disease. The test was not accurate given that it had a very small efficiency rate.


Calculation 4:This shows how accurate the PCR machine is to predict that a patient would not develop the disease. The test was even mess reliable than calculation 3 because it only showed a 27% efficiency rate.
Calculation 4:This shows how accurate the PCR machine is to predict that a patient would not develop the disease. The test was even mess reliable than calculation 3 because it showed a very small efficiency rate.


==Computer-Aided Design==
==Computer-Aided Design==

Revision as of 22:51, 25 November 2014

BME 100 Fall 2014 Home
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Lab Write-Up 1 | Lab Write-Up 2 | Lab Write-Up 3
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OUR COMPANY

Name: Marissa Seelhammer
Name: Brianna Denuit
Name: Farhad Eghbalian
Name: Shane Mitchell
Name: Catherine Piatak
Name: Nivenka Mahesh


LAB 6 WRITE-UP

Bayesian Statistics

Overview of the Original Diagnosis System

In order to test patients for the disease-associated SNP, DNA samples are amplified through polymerase chain reaction in an Open PCR machine and analyzed using imaging software. The BME 100 lab class, divided into 34 teams, analyzed the DNA of 68 different patients and determined whether their results for the disease were positive, negative, or inconclusive. Each team was given exactly two patients’ DNA samples and tested them against positive and negative controls to determine a diagnosis. In order to prevent error, each patient had three different samples of DNA, all of which were to be tested separately so that the chance of a misdiagnosis was minimized. Additionally, ImageJ, the software used to analyze the pictures of the amplified DNA drops, was specifically calibrated to measure the most accurate amount of green color (SYBR Green I) in the pictures while filtering out the blue and red light. Error was also minimized by having the groups analyze three separate drops for each patient using ImageJ and averaging the results, ideally making the data more reliable. Out of the 186 total patient DNA analyses, there were 92 positive diagnoses and 73 negative diagnoses, leaving 21 tests inconclusive. When the three trials of each patient were averaged together, the final outcome was 30 positive, 24 negative, and 6 inconclusive. There were also 6 patients whose data was never submitted. Out of those with a positive diagnosis, 89% actually had the positive DNA, and out of those with a negative diagnosis, 77% actually did not have the positive DNA. This indicates that the process used does successfully diagnose the patient, however, ideally the percentage of a correct diagnosis would be above 95%.


What Bayes Statistics Imply about This Diagnostic Approach


Calculation 1: This shows how accurate the test is to detect a positive result for SNP (sensitivity). The result of this test shows that the probability of a patent getting a positive result for having SNP from the PCR machine given that they have the disease. This value is fairly close to 1.00, implying that the individual PCR replicates are mostly reliable.

Calculation 2: This shows how accurate the test is to detect a negative result for SNP (specificity). The result for this test shows that the probability of a patent getting a negative result for having SNP from the PCR machine given that they do not have the disease. The calculated value for this probability is fairly close to 1.00, indicating that the reliability of the diagnosis of this test is fairly high.

In other words, both of these show accuracy but also have room for improvement. This room for improvement comes from the error that occurred during the lab. These were a result from both human error and device error. Human errors include error in the preparation for the test. Specific tests that were shown to be positive should have displayed a negative result. This could be as a result from mixing up the samples. In the case for our group, the negative control (in other words, the sample that we knew should have displayed a negative result for the disease) displayed a positive result for SNP. The mix up in the samples probably occurred before we actually deceived the samples. This error could have also been a result of improper disposal of pipet tips before and after each use during preparation, thus causing a mix of samples. Device error includes not running the same amount of cycle in the open PCR machine. Each group's machine ran a different amount of cycles and some machines simply did not work at all. Receiving a positive result for our patient sample when it should have been negative left error in the Bayes values.


Calculation 3: This shows how accurate the PCR machine is to predict the disease in a patient that would develop the disease. The test was not accurate given that it had a very small efficiency rate.

Calculation 4:This shows how accurate the PCR machine is to predict that a patient would not develop the disease. The test was even mess reliable than calculation 3 because it showed a very small efficiency rate.

Computer-Aided Design

TinkerCAD

The TinkerCAD tool is used just like the software SolidWorks, it is there to create 2-dimnsional or 3-dimensional figures online. TinkerCAD was used to make our 3-dimensional PCR. First we made all of the different pieces of the PCR separately and it was 2-dimensional. Then after all the separate pieces were made, it was put all together to make the final, 3-dimensional, product.

Our Design








Feature 1: Consumables Kit

The kit will be packaged with the new and improved color coded plastic vials to replace the bland clear ones. The consumables kit will also include tips for the micro-pipette. If requested the kit can come with the testing reagents but at an extra cost since they will need to be kept properly to not become contaminated. The whole kit will be packaged in an insulated container to keep the reagents at the desired temperature. The box will be resistent to avoid damages or loss of effectivity when being stored for longer periods of time.

Color coded plastic vial, will add a significant strength to the overall procedure process. Each color coded plastic tube (blue, red, green, black and clear) specifically will be used for designated substance. This major improvement considerably reduces the error upon filling the tubes, enhance operator confidence (in regard to procedure steps), speed the process of filing the tubes, reduce the cost of operation (substance, tips, vile and the time that must be use to redo the process), create a systematic approach to the procedure. Furthermore, the black vial may specifically be used for SYBR green substance. The black tube, greatly reduce the light exposure to SYBR green substance, increase the SYBR Green sensitivity, reduce the cost of over use of SYBR Green, eliminate the steps of aluminum foil covering and increase the speed of procedure.


Feature 2: Hardware - PCR Machine & Fluorimeter

The PCR and flourimeter will attached for storage purposes but can be taken apart for use or kept together for use depending on the wishes of the operators. The digital interface will be compatible with one another so collecting data from both can be done in the same program. A stationary camera mount will be attached and will have adjustable heights to make ease of access for different shaped picture devices to pit snuggle and securely. The software will allow the device inside to take pictures remotely so fumbling with the orientation to get a picture taken will no longer be a problem.

Rewiring cable and harness: Rewire harness with higher temperature grade resistance could significantly reduce the malfunction of PCR equipment. The upgraded wire temperature grade, consist of insulation that are more resistance to temperature variation (such as semi ceramic insulator). The ISO (International Standard Organization) required, durable medical equipments and aerospace craft always designed and manufactured with highest precision expected.


Heat-shrink tubing in all wire and harness: Heat- shrink tubing has been designed to add an extra protection to the insulation of wires that may constantly experiencing various heat, physical movement and expositor to moisture as well as chemical contacts. Since interior of PCR equipment imply different temperatures, the process of heat-shrinking the wire and harness greatly will reduce the chance of miss connection. In addition, added heat-shrinks will help grouping the single loosed wires.


Adding a ball bearing auxiliary fan along with two temperature sensors to upper heated lid and lower heated block: By adding a set of miniature temperature sensor to upper heated lid and lower heated block, each sensor is able to send a signal to the micro processor that shows the temperatures of both plates. This data will be displayed on LCD and also will be an initial point to turn on the auxiliary fan to cool done the plates. This future will reduce the waiting time, and speed the over all process of PCR amplification. Compare to a regular fan, most ball bearing fan are able to sustain long hours of works without any hassle.


Adding locking sensor along with sound indicator to upper plate: A micro switch added to the heat lid that indicate a complete/ secured close lid, would be an improvement design to the current PCR equipment. A micro switch is able to sense an open door (heated lid not completely closed), send a signal to the processor, indicate the status on the LCD display and warn the operator by a flashing LED or an alternate beeping sound.