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- All Subjects: Traumatic Brain Injury
- All Subjects: Microparticles
- Creators: Stabenfeldt, Sarah
- Member of: Barrett, The Honors College Thesis/Creative Project Collection
The goal of this research project is to create a Mathcad template file capable of statistically modelling the effects of mean and standard deviation on a microparticle batch characterized by the log normal distribution model. Such a file can be applied during manufacturing to explore tolerances and increase cost and time effectiveness. Theoretical data for the time to 60% drug release and the slope and intercept of the log-log plot were collected and subjected to statistical analysis in JMP. Since the scope of this project focuses on microparticle surface degradation drug release with no drug diffusion, the characteristic variables relating to the slope (n = diffusional release exponent) and the intercept (k = kinetic constant) do not directly apply to the distribution model within the scope of the research. However, these variables are useful for analysis when the Mathcad template is applied to other types of drug release models.
Traumatic brain injury involves a primary mechanical injury that is followed by a secondary<br/>inflammatory cascade. The inflammatory cascade in the CNS releases cytokines which are<br/>associated with leukocytosis and a systemic immune response. Acute changes to peripheral<br/>immune cell populations post-TBI include a 4.5-fold increase of neutrophils 3 hours post-injury,<br/>and 2.7-fold or higher increase of monocytes 24 hours post-injury. Flow Cytometry is a<br/>technique that integrates fluidics, optics, and electronics to characterize cells based on their light<br/>scatter and antigen expression via monoclonal antibodies conjugated to fluorochromes. Flow<br/>cytometry is a valuable tool in cell characterization however the standard technique for data<br/>analysis, manual gating, is associated with inefficiency, subjectivity, and irreproducibility.<br/>Unsupervised analysis that uses algorithms packaged as plug-ins for flow cytometry analysis<br/>software has been discussed as a solution to the limits of manual gating and as an alternative<br/>method of data visualization and exploration. This investigation evaluated the use of tSNE<br/>(dimensionality reduction algorithm) and FlowSOM (population clustering algorithm)<br/>unsupervised flow cytometry analysis of immune cell population changes in female mice that<br/>have been exposed to a LPS-induced systemic inflammatory challenge, results were compared to<br/>those of manual gating. Flow cytometry data was obtained from blood samples taken prior to and<br/>24 hours after LPS injection. Unsupervised analysis was able to identify populations of<br/>neutrophils and pro-inflammatory/anti-inflammatory monocytes, it also identified several more<br/>populations however further inquiry with a more specific fluorescent panel would be required to<br/>establish the specificity and validity of these populations. Unsupervised analysis with tSNE and<br/>FlowSOM demonstrated the efficient and intuitive nature of the technique, however it also<br/>illustrated the importance of the investigator in preparing data and modulating plug-in settings.