Protein and gene circuit level synthetic bioengineering can require years to develop a single target. Phage assisted continuous evolution (PACE) is a powerful new tool for rapidly engineering new genes and proteins, but the method requires an automated cell culture system, making it inaccessible to non industrial research programs. Complex protein functions, like specific binding, require similarly dynamic PACE selection that can be alternatively induced or suppressed, with heat labile chemicals like tetracycline. Selection conditions must be controlled continuously over days, with adjustments made every few minutes. To make PACE experiments accessible to the broader community, we designed dedicated cell culture hardware and integrated optogenetically controlled plasmids. The low cost and open source platform allows a user to conduct PACE with continuous monitoring and precise control of evolution using light.

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.

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.

Traumatic brain injury (TBI), a neurological condition that negatively affects neural capabilities, occurs when a blunt trauma impacts the head. Following the initial injury that immediately impacts neural cell function and survival, a series of secondary injury events lead to substantial sustained inflammation for weeks to years post-injury. To develop TBI treatments that may stimulate regenerative processes, a novel drug delivery system that efficiently delivers the appropriate drug/payload to injured tissue is crucial. Hyaluronic acid (HA) hydrogels are attractive when developing a biomaterial for tissue reparation and regeneration. HA is a natural polymer with physicochemical properties that can be tuned to match the properties of the extracellular matrix (ECM) of the many tissues including the central nervous system (CNS). Here, the project objective was to develop a HA hydrogel system for local delivery of a biological payload; this objective was completed by employing a composite system with two parts. The first part is an injectable, shear-thinning bulk hydrogel, and the second is microgels for loading biological payloads. The bulk hydrogel was composed of cyclodextrin modified HA (Cd-HA) and adamantane modified HA (Ad-HA) that give rise to guest-host interactions that facilitate physical crosslinking. The microgel, composed of norbornene-HA (Nor-HA) and sulfated-HA, crosslink via chemical crosslinks upon activation of a UV photoinitiator. The sulfated-HA microgels facilitate loading of biological payloads by mimicking heparin binding sites via the conjugated sulfated group. Neuregulin I, an epidermal growth factor with neuroprotective properties, is one such protein with a heparin binding domain that may be retained in the sulfated-HA microgels. Specifically, the project focused on mechanical testing of this composite microgel/hydrogel system and also developing protein affinity assays.

Advancing the understanding and treatment of many neurological disorders can be achieved by improving methods of neuronal detection at increased depth in the mammalian brain. Different cell subtypes cannot be detected using non-invasive techniques beyond 1 mm from cortical surface, in the context of targeting particular cell types in vivo (Wang, 2012). These limitations in the depth of imaging and targeting are due to optical scattering (Ntziachristos, 2010). In order to overcome these restrictions, longer wavelength fluorescent proteins have been utilized by researchers to see tagged cells at depth. Optical techniques such as two-photon and confocal microscopy have been used in combination with fluorescent proteins to expand depth, but are still limited by the penetration depth of light due to optical scattering (Lee, 2015). This research aims to build on other detection methods, such as the photoacoustic effect and automated fluorescence-guided electrophysiology, to overcome this limitation.

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.

The ability to externally stimulate gold nanoparticles (GNPs) that are linked to drugs can improve targeted drug delivery to help patients with Parkinson’s disease to increase the activity levels of their basal ganglia to regain motor skills that were once lost. This paper analyzes 5 nm GNPs due to their biocompatibility and ability to cross the blood-brain barrier (BBB). Studies have shown GNPs heat up when exposed to radiofrequency (RF) electromagnetic fields which could be used to release dopamine-related drugs directly in a patient’s basal ganglia to increase activity. However, GNP stimulation often requires a high power output which could damage tissues. A series of methods were used to first characterize the GNPs to ensure the size and viability of the sample. Then, different stimulation tests were run to evaluate the temperature change of GNPs to determine if stimulation is possible in a frequency range that does not require a high power output. The most successful stimulation method utilized a waveguide, which was able to consistently heat GNPs 0.4 C in 15 minutes more than the negative control. The methodology was then tested within the brain of a perfused rat by using magnetic resonance thermometry (MRT). Two scans were taken at different times to solve for the differential pixel value to evaluate whether the brain cooled down over time after being theoretically stimulated initially. While the initial results of these scans were inconclusive, there was much to be improved throughout the process, warranting further research.

Although relatively new technology, machine learning has rapidly demonstrated its many uses. One potential application of machine learning is the diagnosis of ailments in medical imaging. Ideally, through classification methods, a computer program would be able to identify different medical conditions when provided with an X-ray or other such scan. This would be very beneficial for overworked doctors, and could act as a potential crutch to aid in giving accurate diagnoses. For this thesis project, five different machine-learning algorithms were tested on two datasets containing 5,856 lung X-ray scans labeled as either “Pneumonia” or “Normal”. The goal was to determine which algorithm achieved the highest accuracy, as well as how preprocessing the data affected the accuracy of the models. The following supervised-learning methods were tested: support vector machines, logistic regression, decision trees, random forest, and a convolutional neural network. Each model was adjusted independently in order to achieve maximum performance before accuracy metrics were generated to pit the models against each other. Additionally, the effect of resizing images on model performance was investigated. Overall, a convolutional neural network proved to be the superior model for pneumonia detection, with a 91% accuracy. After resizing to 28x28, CNN accuracy decreased to 85%. The random forest model performed second best. The 28x28 PneumoniaMNIST dataset achieved higher accuracy using traditional machine learning models than the HD Chest X-Ray dataset. Resizing the Chest X-ray images had minimal effect on traditional model performance when resized to 28x28 or larger.

Sensorimotor adaptation is a type of learning that allows sustaining accurate movements by adjusting motor output. This allows the brain to adapt to temporary changes when engaged in a certain task. Within sensorimotor adaptation, visuomotor adaptation (VMA) is one’s ability to correct a visual perturbation. In this study, we present preliminary results on the effects of VMA with the control group, compared to groups undergoing trigeminal nerve stimulation (TNS) or SHAM (placebo) effects. Twenty-two healthy subjects with no past medical history participated in this study. Subjects performed a visuomotor rotation task, which required gradually adapting to a perturbation between hand motion and corresponding visual feedback. Five total blocks were completed: two familiarization blocks, one baseline block, one rotation block with a 30◦ counterclockwise rotation, and one washout block with no rotation. The control group performed better than the 120 Hz (TNS) and SHAM groups due to less directional error (DE) on the respective learning curves. Additionally, the control group adapted faster (less DE) than the SHAM groups that either felt stimulation, or did not feel the stimulation. The results yield new information regarding VMA which can be used in the future when comparing sensorimotor adaptation and its many applications.