Traumatic brain injury (TBI) is a widespread health issue that affects approximately 1.7 million lives per year. The effects of TBI go past the incident of primary injury, as chronic damage can follow for years and cause irreversible neurodegeneration. A potential strategy for repair that has been studied is cell transplantation, as neural stem cells improve neurological function. While promising, neural stem cell transplantation presents challenges due to a relatively low survival rate post-implantation and issues with determining the optimal method of transplantation. Shear-thinning hydrogels are a type of hydrogel whose linkages break when under shear stress, exhibiting viscous flow, but reform and recover upon relaxation. Such properties allow them to be easily injected for minimally invasive delivery, while also shielding encapsulated cells from high shear forces, which would normally degrade the function and viability of such cells. As such, it is salient to research whether shear-thinning hydrogels are feasible candidates in neural cell transplantation applications for neuroregenerative medicine. In this honors thesis, shear-thinning hydrogels were formed through guest-host interactions of adamantane modified HA (guest ad-HA) and beta-cyclodextrin modified HA (host CD-HA). The purpose of the study was to characterize the injection force profile of different weight percentages of the HA shear-thinning hydrogel. The break force and average glide force were also compared between the differing weight percentages. By understanding the force exerted on the hydrogel when being injected, we could characterize how neural cells may respond to encapsulation and injection within HA shear-thinning hydrogels. We identified that 5% weight HA hydrogel required greater injection force than 4% weight HA hydrogel to be fully delivered. Such contexts are valuable, as this implies that higher weight percentage gels impart higher shear forces on encapsulated cells than lower weight gels. Further study is required to optimize our injection force system’s sensitivity and to investigate if cell encapsulation increases the force required for injection.

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.

Polymeric nanoparticles (NP) consisting of Poly Lactic-co-lactic acid - methyl polyethylene glycol (PLLA-mPEG) or Poly Lactic-co-Glycolic Acid (PLGA) are an emerging field of study for therapeutic and diagnostic applications. NPs have a variety of tunable physical characteristics like size, morphology, and surface topography. They can be loaded with therapeutic and/or diagnostic agents, either on the surface or within the core. NP size is an important characteristic as it directly impacts clearance and where the particles can travel and bind in the body. To that end, the typical target size for NPs is 30-200 nm for the majority of applications. Fabricating NPs using the typical techniques such as drop emulsion, microfluidics, or traditional nanoprecipitation can be expensive and may not yield the appropriate particle size. Therefore, a need has emerged for low-cost fabrication methods that allow customization of NP physical characteristics with high reproducibility. In this study we manufactured a low-cost (<$210), open-source syringe pump that can be used in nanoprecipitation. A design of experiments was utilized to find the relationship between the independent variables: polymer concentration (mg/mL), agitation rate of aqueous solution (rpm), and injection rate of the polymer solution (mL/min) and the dependent variables: size (nm), zeta potential, and polydispersity index (PDI). The quarter factorial design consisted of 4 experiments, each of which was manufactured in batches of three. Each sample of each batch was measured three times via dynamic light scattering. The particles were made with PLLA-mPEG dissolved in a 50% dichloromethane and 50% acetone solution. The polymer solution was dispensed into the aqueous solution containing 0.3% polyvinyl alcohol (PVA). Data suggests that none of the factors had a statistically significant effect on NP size. However, all interactions and relationships showed that there was a negative correlation between the above defined input parameters and the NP size. The NP sizes ranged from 276.144 ± 14.710 nm at the largest to 185.611 ± 15.634 nm at the smallest. In conclusion, the low-cost syringe pump nanoprecipitation method can achieve small sizes like the ones reported with drop emulsion or microfluidics. While there are trends suggesting predictable tuning of physical characteristics, significant control over the customization has not yet been achieved.

Current sideline concussion assessment tools are inaccurate and biased leading to undiagnosed concussions and possibly a second, more severe concussion. This study evaluated the effects of different surface types on postural stability using the Lockhart Monitor iPhone application in order to validate its potential use as a data-driven sideline concussion assessment tool. Participants had three components of their postural sway recorded in 30 and 60-second trials on three different surface types, tile, turf, and natural grass, with eyes open and closed. The statistical analysis found that there was a significant difference between surface types for the sway area (p = 0.0268), but there was no difference for the sway path and velocity. These results call for further research to be conducted on the impact of surface types and the use of the Lockhart Monitor as a sideline concussion assessment tool with larger sample sizes and improved methodologies.