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Synthesis of Dual Layered Microparticles for Tunable Delayed Release Profile

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The primary objective of this research project is to develop dual layered polymeric microparticles with a tunable delayed release profile. Poly(L-lactic acid) (PLA) and poly(lactic-co-glycolic acid) (PLGA) phase separate in a double emulsion process due to differences in hydrophobicity, which

The primary objective of this research project is to develop dual layered polymeric microparticles with a tunable delayed release profile. Poly(L-lactic acid) (PLA) and poly(lactic-co-glycolic acid) (PLGA) phase separate in a double emulsion process due to differences in hydrophobicity, which allows for the synthesis of double-walled microparticles with a PLA shell surrounding the PLGA core. The microparticles were loaded with bovine serum albumin (BSA) and different volumes of ethanol were added to the PLA shell phase to alter the porosity and release characteristics of the BSA. Different amounts of ethanol varied the total loading percentage of the BSA, the release profile, surface morphology, size distribution, and the localization of the protein within the particles. Scanning electron microscopy images detailed the surface morphology of the different particles. Loading the particles with fluorescently tagged insulin and imaging the particles through confocal microscopy supported the localization of the protein inside the particle. The study suggest that ethanol alters the release characteristics of the loaded BSA encapsulated in the microparticles supporting the use of a polar, protic solvent as a tool for tuning the delayed release profile of biological proteins.

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2015-05

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Improving Sleep Bout Detection from a Thigh-Worn Accelerometer in Young Adults

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Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in

Introduction: There is currently a lack of industry-wide gold standardization in accelerometer study
protocols, including within sleep-focused studies. This study seeks to address accuracy of
accelerometer data in detection of the beginnings and ends of sleep bouts in young adults with
polysomnography (PSG) corroboration. An existing algorithm used to differentiate valid/invalid wear
time and detect bouts of sleep has been modified with the goal of maximizing accuracy of sleep bout
detection. Methods: Three key decisions and thresholds of the algorithm have been modified with three
experimental values each being tested. The main experimental variable Sleepwindow controls the
amount of time before and after a determined bout of sleep that is searched for additional sedentary
time to incorporate and consider part of the same sleep bout. Results were compared to PSG and sleep
diary data for absolute agreement of sleep bout start time (START), end time (END) and time in bed
(TIB). Adjustments were made for outliers as well as sleep latency, snooze time, and the sum of both.
Results: Only adjustments made to a sleep window variable yielded altered results. Between a 5-, 15-,
and 30-minute window, a 15-minute window incurred the least error and most agreement to
comparisons for START, while a 5-minute window was best for END and TIB. Discussion: Contrary
to expectation, corrections for snooze, latency, and both did not substantially improve agreement to
PSG. Algorithm-derived estimates of START and END always fell after sleep diary and PSG both,
suggesting either participants’ sedentary behavior beginning and ends were at a delay from sleep and
wake times, or the algorithm estimates consistently later times than appropriate. The inclusion of a
sleep window variable yields substantial variety in results. A 15-minute window appears best at
determining START while a 5-minute window appears best for END and TIB. Further investigation on
the optimal window length per demographic and condition is required.

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Date Created
2019-12

Statistical Modeling of Drug Release from Spherical Surface-Degrading Particle Batch

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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

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.

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2021-05