BME100 s2015:Group3 12pmL1

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Contents

OUR TEAM

Name: Abraham Rosengard
Name: Abraham Rosengard
Name: Juan Patterson
Name: Juan Patterson
Name: Gabriella Leiva-Hidalgo
Name: Gabriella Leiva-Hidalgo
Name: Jessica Resendiz
Name: Jessica Resendiz
Name: Raghad Tamim
Name: Raghad Tamim
]
Name: Your name
Name: Your name

LAB 1 WRITE-UP

Independent and Dependent Variables

Independent Variables: The independent variable is the concentration of the protein lipopolysaccharide. We will be starting with 10 mg and will reduce the dosage by 2.00 milligrams for each group until the dosage is down to 4 mg

Dependent Variables: The dependent variable is the increase of the protein Inflammotin in the elderly.

Experimental Design

Groups

There will be 4 groups.

Age

The test subjects would be from several retirement homes to account for randomization and will be focused around the age group of 65-75 years of age with relatively good health.

Number of subjects per group

There will be 10 people per group which reaches a total of 40 people. This would yield a good random distribution of participants and would be a fair amount to spend based on the lab’s current financial status. Ideally the groups would be consisted of both genders equally with varying nationalities.




Subject Selection

The test subjects would be from several retirement homes to account for randomization and will be focused around the age group of 65-75 years of age with relatively good health. To control this experiment we will be looking into the medical history of the participants for any pre-existing conditions. We would be particularly look into if any other medications taken have been known to interfere with either the independent or dependent variable, either increasing levels of Inflammotin. As well as any serious conditions in the past that could affect inflammotin production or the lipopolysaccharide that they will be ingesting.




Sources of Error and Bias

Potential sources error are interaction with other medication, diet, dietary conditions. Human error can also have drastic results like a tech prescribing the incorrect dose to a participant or entering the wrong information.






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