BME103:T130 Group 15: Difference between revisions
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As previously explained shown in Protocols, primers are needed for the DNA replication, a forward and a reverse. One at the "completing" strand of the double strand, and one as the cancer detecting strand. On chromosome 22, the primer to detect r17879961 defect has to have the changed nucleotide. | As previously explained shown in Protocols, primers are needed for the DNA replication, a forward and a reverse. One at the "completing" strand of the double strand, and one as the cancer detecting strand. On chromosome 22, the primer to detect r17879961 defect has to have the changed nucleotide. | ||
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The | The reverse primer used: | ||
AACTCTTACA-'''C'''-TCGATACAT | AACTCTTACA-'''C'''-TCGATACAT | ||
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The | The forward primer used: | ||
TTGAGAATGT-'''C'''-AGCTATGTA | TTGAGAATGT-'''C'''-AGCTATGTA | ||
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If the cancer gene is present, then the matching primer will completely bind to the DNA strand. When this happens, the amplification sequence will be able to precede, and this will show up as a positive result. | If the cancer gene is present, then the matching primer will completely bind to the DNA strand. When this happens, the amplification sequence will be able to precede, and this will show up as a positive result. | ||
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[[Image:DNAAmplificationGP15.jpg|DNAAmplificationGP15.jpg]] | [[Image:DNAAmplificationGP15.jpg|DNAAmplificationGP15.jpg]] | ||
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Image | Image by: Alyssa Alexander | ||
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This is all in theory of course, and should work perfectly every time. But there are many factors to consider. For instance, not every one who has cancer has the cancer gene. | |||
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Based on Bayesian reasoning, sensitivity ran to be about 80% and the specificity running to be about 90.4 percent. This means that the test itself is generally in favor of testing negative, which means there are less chances to have false diagnosis and/or treatments. These percentages resulted from the following equation: | |||
<math>Sensitivity = (True Positive)/(True Positive + False Negative)</math> <br> | |||
<math>Specificity= (True Negative)/(True Negative + False Positive)</math> | |||
Additionally, the probability that someone will test positive that will actually have the disease has approximately 7.8 percent. However, The chance that someone does not test positive, and doesn't have cancer is about 99.8%. | |||
This generally means that | |||
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These percentages resulted from the following equation:<br> | |||
<math>Positive Predictive Value = (True Positive)/(True Positive + False Positive)</math> <br> | |||
<math>Negative Predictive Value = (True Negative)/(True Negative + False Negative)</math> | |||
The values for these above equations are gathered from Bayesian's reasoning. | |||
Baye's Theorem: <br> | |||
<math>p(C/T) = (p(T/C)*p(C))/(p(T/C)*p(C)+p(T/nC)*p(nC))</math> <br> | |||
where p(C/T) is the probability of a person with positive results will have cancer out of the entire patients participating. p(C) is the probability of having cancer present, p(T/C) is the percent of patients who tested positive with have cancer and had it. n = not | |||
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The values of the these equations are taken from the general statistics from tests performed on 'x' amount of patients. For instance, the direct results showed that 80% of the people with cancer, tested positive, this value would be used as p(T/C) because that is the test running positive and having cancer. The calculated p(C/T) was used as the "True Positive" in the original four equations. The same for the false positive, true negative and false negative can be solved similarly as well. | |||
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==Results== | ==Results== |
Revision as of 17:51, 14 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 Testing
ProtocolsPolymerase Chain Reaction
The Components of the GoTaq® Colorless Master Mix
DNA Samples (8)
(Add your work from Week 3, Part 2 here aka, steps of assembly to the flourimeter)
Research and DevelopmentSpecific Cancer Marker Detection - The Underlying Technology There is a genetic relation to having cancer or not when an individual is over the age of 40. The specific gene, in this case, is located on chromosome 22, r17879961. To test an human's DNA for this cancer gene, we have go through a series of reactions called PCR on the DNA for replication and amplification of the patient's DNA strand.
[math]\displaystyle{ Sensitivity = (True Positive)/(True Positive + False Negative) }[/math] [math]\displaystyle{ Specificity= (True Negative)/(True Negative + False Positive) }[/math] Additionally, the probability that someone will test positive that will actually have the disease has approximately 7.8 percent. However, The chance that someone does not test positive, and doesn't have cancer is about 99.8%.
This generally means that
[math]\displaystyle{ Negative Predictive Value = (True Negative)/(True Negative + False Negative) }[/math] The values for these above equations are gathered from Bayesian's reasoning.
Baye's Theorem: Results
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