BME100 f2014:Group14 L1

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Contents

OUR TEAM

LAB 1 WRITE-UP

Independent and Dependent Variables

Independent: Inflammation Inducing Agent (lipopolysaccharide). Different levels of lipopolysaccharide will be tested on to see the affect on the dependent variable.

Dependent: Inflammatory Protein (inflammotin). The inflammatory protein (inflammotin) is what will be tested for in the scientific experiment.

Experimental Design

Groups
We will have 6 groups with 7 random subjects between 55-75 years of age. The very last group that will be given 10 mg will be the control group to prove that 10 mg of the Lipolysaccharide has been found increased protein levels in the body.


Age
Subjects should be elderly men and women, ranging from 55-75 years of age. The medical history of the subjects getting tested should not affect the dosage amount they will receive. Subjects should vary in age, as well as gender, and medical history should not be accounted for to limit the number of variables being tested.


Number of subjects per group
To ensure accuracy, there will be 6 groups with 7 people in each group, totaling 42 subjects. The control group will have 7 people receiving a dosage of 10mg. The 5 other groups of 7 people varying in age and gender will be given different dosages of the Lipopolysaccharide in increments of 2 mg. Both the number of subjects (42) and the randomization will increase the accuracy and precision of the data.


Dosage of Lipolysaccharide People per Dosage Sample Age & Gender Increase of protein (Inflammtonin)
0 mg 7 random varied 55-75 TBD after results
2 mg 7 random varied 55-75 TBD after results
4 mg 7 random varied 55-75 TBD after results
6 mg 7 random varied 55-75 TBD after results
8 mg 7 random varied 55-75 TBD after results
10 mg 7 random varied 55-75 TBD after results




Subject Selection

When selecting subjects it is important to consider randomization control, and bias. To ensure that the sample is random, subjects should be both men and women (in equal numbers) ages 55 to 75. The reason for the age specifications is due to the fact that the protein being studied is found in elderly patients.

As far as control goes, subjects must be trusted to take the medicine as scheduled. Subjects must also be able to undergo routine blood sample tests in order for data to be recorded.

In order to prevent bias from interfering with the results, a double-blinded test should be conducted. Neither the subject nor doctor is aware of what is given to each patient. This ensures that the doctors in no way hints that the treatment is to work or not and the subject does not become suspicious.

Determining the sizes of samples can be done so using Power Analysis. This tests determines how many subjects are needed in each sample group as well as the entire experiment.



Sources of Error

One of the largest errors that could occur during this experiment is an error in the data due to a small sample size. A small sample size could skew the results and misrepresent the general population. One way to fix this error would obviously be to increase the sample size. Since the lab is strapped for cash the scientists could create an incentive for people to volunteer as testers that doesn't involve money. For example they could partner with another company that wants to advertise and give out their product for free.

Another source of error could stem from the volunteers themselves. If some volunteers already have elevated amounts of Inflammotin then they will not accurately demonstrate the effects of the lipopolysaccharide pill. This problem can easily be fixed and prevented by pre-screening all of the volunteers. This will set a baseline for the level of Inflammotin in the volunteers and will also give more specific data as to the effects on the individual.

A third error could result from the genders of the volunteers. Females and males could react differently to the pill and therefore have different results. If these results were compiled together and there was a different ratio of males to female in the testing body, the results would not be an accurate average for the entire population. One solution to this problem could be to run the trials on both genders in the exact same manner. For every group of men have a group of women, essential doubling the trials.







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