BME100 f2014:Group29 L1
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LAB 1 WRITE-UP
Lab Write Up assignment 1
Mini-problem: This semester, you and your team are assisting a graduate student, Kristen, in a research lab. You are studying the effects of inflammation, but the lab is short on cash. You must determine the lowest possible dose of an inflammation inducing agent (lipopolysaccharide) to increase a newly discovered inflammatory protein (Inflammotin) in the elderly. The protein is measured in blood samples using ELISA. Prior work as shown that 10mg pill of lipopolysaccharide can increase protein levels so this is a good starting point.
1. What are your independent and dependent variables?
2. Describe your experimental design.
a. How many groups?
b. How many subjects/group? We will have 30 subjects per group.
3. How will you select your subjects?
4. What are some of the potential sources of error or biases that could affect your results and how would you control for them?\
Independent and Dependent Variables
The lipopolysaccharide is the independent variable and the Inflammotin is the dependent variable.
Each of our subjects will be their own control. Each subject will first have their Inflammotin levels for one month measured daily followed by a month of daily measurement of Inflammotin levels while on the lipopolysaccharide. We will randomly select elderly people who are ages 65+.
We are going to randomly pick volunteers who are over 65 years old.
Sources of Error and Bias
Possible sources of error in this lab could include: other medications the subjects are taking that might interfere with the lipopolysaccharide. Many elderly people are already taking medications for various problems such as high cholesterol, heart problems, and diabetes. We don't know what kind of affect these other medications will cause in our experiment. Other sources of bias might come from accumulating all of the subjects from a specific place, i.e a retirement home. To eliminate this prejudice we plan to pick random volunteers that are over the age of 65. By selecting our subjects this way we hope to not only decrease the bias of our selection, but also decrease our potential source of error with relatively healthier subjects who are not taking other medications.