BME100 f2018:Group2 T1030 L6

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OUR COMPANY

Name: Daniella Pautz
Name: Amy Floyd
Name: Ally Spohn
Name: Jackson Gates
Name: Nick Hagan

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LAB 6 WRITE-UP

Bayesian Statistics

Bayesian Statistics Bayesian statistics: is the theory in the statics based on probability and the degree of belief. It is used whenever prevalence, sensitivity, and false positive rates are provided. Sensitivity is the probability that a person with the disease will test positive for that disease, however, specificity is the probability that are person being tested for a disease, will test negative when they do not have the disease. The PPV (Positive Predictive Value) represents those who have the disease that are being tested and the probability that their results will be positive. The NPV (Negative Predictive Value) is the probability that a person who tests negative for a disease does not have the disease.

Overview of the Original Diagnosis System

Calculation 1 - The probability that a patient will get a positive final test conclusion, given a positive PCR reaction. The A value is the frequency of total POS conclusions which 0.44. The B value is the frequency of total pos PCRs which is 0.42. The probability of B given A is the frequency of total pos with POS and the value is 0.90. The calculation of the probability of A given B is (0.90 x 0.44)/(0.42) which is equal to 0.94 or 94%


Calculation 2 - The probability that a patient will get a negative final test conclusion, given a negative diagnostic signal. The A value is the frequency of total NEG conclusions which 0.53. The B value is the frequency of total neg PCRs which is 0.51. The probability of B given A is the frequency of total neg with NEG and the value is 0.94. The calculation of the probability of A given B is (0.94 x 0.53)/(0.51) which is equal to 0.98 or 98%.


Calculation 3 - The probability that a patient will develop the disease, given a positve final test conclusion. The A value is the frequency of total "yes" diagnoses which 0.34. The B value is the frequency of total POS conclusions which is 0.44. The probability of B given A is the frequency of total "yes" diagnoses with POS conclusions and the value is 0.43. The calculation of the probability of A given B is (0.43 x 0.34)/(0.44) which is equal to 0.33 or 33%.


Calculation 4 - The probability that a patient will not develop the disease, given a negative final test conclusion. The A value is the frequency of total "no" diagnoses which 0.66. The B value is the frequency of total NEG conclusions which is 0.53. The probability of B given A is the frequency of total "no" diagnoses with NEG conclusions and the value is 0.77. The calculation of the probability of A given B is (0.77 x 0.66)/(0.53) which is equal to 0.96 or 96%.


What Bayes Statistics Imply about This Diagnostic Approach

The calculations for a positive PCR with a positive conclusion and a negative PCR with a negative conclusion are both close to 100%, making them mostly reliable for detecting the PCR disease.

The calculation for a negative result without the disease is close to 100%, which means that it is very reliable in diagnosing someone without the disease as not having the disease. However, the calculation for someone having the disease receiving a positive diagnosis is below 50%, making it mostly unreliable for detecting the disease in someone who has it.


Questions

  1. Which calculation describes the sensitivity of the system regarding the ability to detect disease SNP?
         Calculation 1 describes the sensitivity of the system regarding the ability to detect disease SNP.
  1. Which calculation describes the sensitivity of the system regarding the ability to predict the disease?
         Calculation 3 describes the sensitivity of the system regarding the ability to predict the disease SNP.
  1. Which calculation describes the specificity of the system regarding the ability to detect the disease SNP?
         Calculation 2 describes the specificity of the system regarding the ability to detect the disease SNP.
  1. Which calculation describes the specificity of the system regarding the ability to predict the disease SNP?
         Calculation 4 describes the specificity of the system regarding the ability to predict the disease SNP.

Sources of Error

  1. Human error such that too much or too little of the SYBR Green was added into the calf thymus solution
  2. Errors with technology, such as the phone and image j
  3. Different light exposure times with the dye; there may have been too much contact with the light

Intro to Computer-Aided Design

3D Modeling

The group decided to use Solidworks in order to prototype our new fluorometer design. Most of the group had used this program before for other classes. Amy was selected to make the actual prototype. She had a relatively easy time building the various components; the most difficult of which were the hinges and the light 'on and off' switch. For her, the assembly of the separate components into the final design took the most time because she had only assembled using solidworks once before. The changing of the various materials' appearance was also time consuming.



Our Design

Name:Amberlight

Features
1)A blue LED light array enclosed within a 3 x 5 in box, all sides and the bottom of the box are heard plastic. 2)The top of the box has an inset of frosted glass panel which will be backlit by the blue LED array when the switch is turned to the on position. 3)Included on the glass panel is a grid creating 180 of .25 in^2 cubes 4)Attached to the back of the box via two hinges is a clear lid that is tinged





Feature 1: Consumables

Feature 2: Hardware - PCR Machine & Fluorimeter