BME103:T930 Group 15: Difference between revisions
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<br><br><b>Samples we tested:</b><br>Positive control: cancer DNA template (+)<br>Negative Control: no DNA template (-)<br>Patient 1: ID 12329(*) Replicate: 1 Label:<b> P1R1</b><br>Patient 1: ID 12329 Replicate: 2 Label:<b> P1R2</b><br>Patient 1: ID 12329 Replicate: 3 Label:<b> P1R3</b><br>Patient 2: ID 61058(**) Replicate: 1 Label:<b> P2R1</b><br>Patient 2: ID 61058 Replicate: 2 Label:<b> P2R2</b><br>Patient 2: ID 61058 Replicate: 3 Label:<b> P2R3</b><br>*Patient 1: Female, 61 years of age<br>**Patient 2: Male, 57 years of age.<br><br> | <br><br><b>Samples we tested:</b><br>Positive control: cancer DNA template (+)<br>Negative Control: no DNA template (-)<br>Patient 1: ID 12329(*) Replicate: 1 Label:<b> P1R1</b><br>Patient 1: ID 12329 Replicate: 2 Label:<b> P1R2</b><br>Patient 1: ID 12329 Replicate: 3 Label:<b> P1R3</b><br>Patient 2: ID 61058(**) Replicate: 1 Label:<b> P2R1</b><br>Patient 2: ID 61058 Replicate: 2 Label:<b> P2R2</b><br>Patient 2: ID 61058 Replicate: 3 Label:<b> P2R3</b><br><b>*Patient 1: Female, 61 years of age.<br>**Patient 2: Male, 57 years of age.</b><br><br> | ||
<b>Flourimeter Protocol</b><br> | <b>Flourimeter Protocol</b><br> | ||
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[[image:BME103_Group15_Taq.jpg|600px]] | |||
<br><br> | <br><br> | ||
Bayes Rule is used to test all data collected and find the limitations of the data. It can be used to fin the positive predictive value (PPV) and the negative predictive value (NPV). The variables used are: | |||
C= Cancer Present | |||
T= Positive Test | |||
P(A|B)= Probability of A, given B | |||
~= not | |||
These vaiables can find the prior probability and conditional probability. | |||
Prior Probability: | |||
p(C) {probability of cancer} | |||
Conditional Probability: | |||
p(T|C) {probability of a positive test} | |||
p(T|~C) {probability of a negative test} | |||
When the information is found, Baye’s Theorem can be utilized: | |||
p(C|T)=[p(T|C)*p(C)]/[p(T|C)*p(C)+p(T|~C)*p(~C)] | |||
Baye’s Reasoning can also be used to find clinical diagnostic specifics; sensitivity, specificity, PPV and NPV. | |||
Sensitivity= p(T|C) x 100% | |||
Specificity= [1-p(T|~C)] x 100% | |||
PPV= p(C|T) x 100% | |||
NVP= [1-p(C|T)] x 100%<br><br> | |||
==Results== | ==Results== | ||
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<!--- Place two small Image J data images here. One showing the result of Water and the other showing the result of Calf Thymus DNA ---> | <!--- Place two small Image J data images here. One showing the result of Water and the other showing the result of Calf Thymus DNA ---> | ||
[[image:BME103_Group15_water.jpg|300px]]<br>Water<br><br>[[image:BME103_Group15_calf.jpg|300px]]<br>Calf Thymus DNA<br><br> | |||
<!--- Enter the values from your group's Data Analyzer table below. E6, F6, etc. are the excel cells from which you should copy your data. ---> | <!--- Enter the values from your group's Data Analyzer table below. E6, F6, etc. are the excel cells from which you should copy your data. ---> | ||
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| '''Sample''' || '''Integrated Density''' || '''DNA μg/mL''' || '''Conclusion''' | | '''Sample''' || '''Integrated Density''' || '''DNA μg/mL''' || '''Conclusion''' | ||
|- | |- | ||
| PCR: Negative Control || 2089225 || 1.52 || positive | | PCR: Negative Control (Water) || 2089225 || 1.52 || positive | ||
|- | |- | ||
| PCR: Positive Control || 714695 || 0.5212 || no signal | | PCR: Positive Control (Calf Thymus) || 714695 || 0.5212 || no signal (negative) | ||
|- | |- | ||
| PCR: Patient 1 ID 12329, rep 1 || 4108573 || 2.996 || positive | | PCR: Patient 1 ID 12329, rep 1 || 4108573 || 2.996 || positive | ||
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| PCR: Patient 1 ID 12329, rep 3 || 2139854 || 1.5606 || positive | | PCR: Patient 1 ID 12329, rep 3 || 2139854 || 1.5606 || positive | ||
|- | |- | ||
| PCR: Patient 2 ID 61058, rep 1 || 762689 || 0.556 || no signal | | PCR: Patient 2 ID 61058, rep 1 || 762689 || 0.556 || no signal (negative) | ||
|- | |- | ||
| PCR: Patient 2 ID 61058, rep 2 || 1035447 || 0.7551 || no signal | | PCR: Patient 2 ID 61058, rep 2 || 1035447 || 0.7551 || no signal (negative) | ||
|- | |- | ||
| PCR: Patient 2 ID 61058, rep 3 || 535664 || 0.3906 || no signal | | PCR: Patient 2 ID 61058, rep 3 || 535664 || 0.3906 || no signal (negative) | ||
| | |} | ||
<br> | |||
KEY | KEY | ||
* '''Sample''' = < | * '''Sample''' = subject DNA that is contained in one test tube <br> | ||
* '''Integrated Density''' = < | * '''Integrated Density''' = Integrated density is the of all values of the pixels in the image<br> | ||
* '''DNA μg/mL''' = < | * '''DNA μg/mL''' = The DNA concentration was measured in micrograms/mL. We found the DNA concentration by multiplying each of the Integrated Densities by 2, then dividing the Integrated Density by the calf thymus DNA value. That gives us our DNA concentration.<br> | ||
* '''Conclusion''' = | * '''Conclusion''' = We concluded that if the DNA concentration was higher than 1.0 micrograms/mL, then it was positive for the cancer gene and if it was less than 1.0 it showed no signal and lacked the cancer gene. For example, our calf thymus DNA had a 2.0 DNA concentration so it was positive for the cancer, while the water which didn't have a DNA concentration didn't show any signs of cancer | ||
Latest revision as of 00:56, 15 November 2012
BME 103 Fall 2012 | Home People Lab Write-Up 1 Lab Write-Up 2 Lab Write-Up 3 Course Logistics For Instructors Photos Wiki Editing Help | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
OUR TEAMLAB 1 WRITE-UPInitial Machine TestingThe Original Design
When we unplugged the LCD display from the circuit board, the machine LED display powered down and did not display anything. When we unplugged the white wire that connects the circuit board to the heat sink, the temperature reading on the LED display screen showed the incorrect temperature. Test Run During the "Test Run" portion of the lab on the 25th of October 2012, the OpenPCR machine did as it was supposed to. It fluctuated the heat of the test samples and it also had the same readings on the computer program as well as the LED display screen of the OpernPCR machine.
ProtocolsPolymerase Chain Reaction PCR is a way to copy a segment of DNA in order to analyze that DNA successfully. First, DNA is extracted from a cell. It does not have to be a lot because the PCR will amplify it. This DNA will be added to a tube with primers, nucleotides and Taq Polymerase. This is heated, cooled, then heated because the temperature changes will help unwind the DNA, make the primers work, and bind the new DNA strands. By running this cycle many times, many copies of DNA can be created.
Flourimeter Protocol Image J Procedure: Research and DevelopmentSpecific Cancer Marker Detection - The Underlying Technology The Open PCR machine works by heating a sample of DNA to 95 degrees Celsius to separate the sample into single strands. Then, the machine cools to 57 degrees Celsius so that primers can attach to the strand to serve as a probe to a matching sequence. The primer that was used in our experiment was r17879961, a cancer-associated sequence, that will detect if the DNA sample tested positive for cancer. Then, the machine heats up to 72 degrees Celsius where MgCl12 attaches to Taq enzyme, so that Taq can take the free floating dioxynucleotide triphosphates and attach them to the stand to form two new separate and similar DNA strands. Now if the process produce new strands, then the DNA tested positive. If none were present, the DNA tested negative. The reason behind this is that r17879961 only can bind to a sequence that matches its own on the single stranded DNA. This sequence is 5’-AAACTCTTACACTCCATACAT-3’. The cancer mutation site is located at the 12 base pair, 5’-ACT-3’. If the base pair is a C, it is positive for cancer mutation. The base pair should truly be a T, making the sequence 5’-ATT-3’. The particular cancer that is associated with the r17879961 primer is colon rectal cancer. The information regarding this particular cancer sequence can be found on the National Center for Biotechnology Information, in the file “.0002 LI- Faurmen Syndrome”. A study done in the file tested the C mutation r17879961. The results that were found were that 7.8% of C mutations were found in cancer patients, and that 5.3% of C mutations were in Finland. The results then can show that 7.8% of the cancer patients had a positive test, and of that 7.8%, 5.3% are in Finland.
~= not These vaiables can find the prior probability and conditional probability.
Prior Probability:
p(C) {probability of cancer}
Conditional Probability:
p(T|C) {probability of a positive test}
p(T|~C) {probability of a negative test}
When the information is found, Baye’s Theorem can be utilized:
p(C|T)=[p(T|C)*p(C)]/[p(T|C)*p(C)+p(T|~C)*p(~C)]
Baye’s Reasoning can also be used to find clinical diagnostic specifics; sensitivity, specificity, PPV and NPV.
Sensitivity= p(T|C) x 100%
Specificity= [1-p(T|~C)] x 100%
PPV= p(C|T) x 100%
NVP= [1-p(C|T)] x 100% Results
KEY
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