ENH proof appendicies
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\begin{tabular}{@{\extracolsep{5pt}} ccccccc}
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\begin{tabular}{@{\extracolsep{5pt}} cccccccc}
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\\[-1.8ex]\hline
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\hline \\[-1.8ex]
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ID & Demo & Gender & ABO Type & Age (years) & BMI (\si{\kg\per\m\squared}) & HLA-A2 Present \\
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ID & Demo & Gender & ABO Type & BMI (\si{\kg\per\m\squared}) & HLA-A2 Present \\
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\hline \\[-1.8ex]
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4 & AA & M & NA & 27 & 21.7 & FALSE \\
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24 & C & M & NA & 22 & 23.9 & FALSE \\
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29 & AA & M & NA & 40 & 20.9 & TRUE \\
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30 & AA & M & NA & 25 & 20.9 & TRUE \\
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309 & C/PI & F & APOS & 22 & 24.9 & FALSE \\
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338 & C & M & OPOS & 23 & 23.4 & TRUE \\
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338 & C & M & OPOS & 24 & 23.4 & TRUE \\
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358 & C & F & OPOS & 52 & 37.1 & TRUE \\
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397 & C & M & OPOS & 53 & 31.2 & FALSE \\
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512 & C & M & OPOS & 21 & 26.5 & FALSE \\
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592 & H & M & APOS & 21 & 31.8 & FALSE \\
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4 & AA & M & NA & 21.7 & FALSE \\
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24 & C & M & NA & 23.9 & FALSE \\
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29 & AA & M & NA & 20.9 & TRUE \\
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30 & AA & M & NA & 20.9 & TRUE \\
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309 & C/PI & F & APOS & 24.9 & FALSE \\
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338 & C & M & OPOS & 23.4 & TRUE \\
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358 & C & F & OPOS & 37.1 & TRUE \\
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397 & C & M & OPOS & 31.2 & FALSE \\
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512 & C & M & OPOS & 26.5 & FALSE \\
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592 & H & M & APOS & 31.8 & FALSE \\
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\hline \\[-1.8ex]
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\end{tabular}
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@ -1,18 +1,18 @@
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\begin{tabular}{@{\extracolsep{5pt}} ccccccccc}
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\begin{tabular}{@{\extracolsep{5pt}} cccccccccc}
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\\[-1.8ex]\hline
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\hline \\[-1.8ex]
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ID & \cd{3} & \cd{4} & \cd{8} & \cd{11c} & \cd{14} & \cd{16} & \cd{19} & \cd{56} \\
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ID & Age (years) & \cd{3} & \cd{4} & \cd{8} & \cd{11c} & \cd{14} & \cd{16} & \cd{19} & \cd{56} \\
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\hline \\[-1.8ex]
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4 & NA & 32.7 & 5.5 & NA & 12.3 & NA & 10.8 & 8.2 \\
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24 & 57.0 & 39.8 & 16.5 & NA & 12.7 & NA & 5.2 & 8.6 \\
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29 & NA & 35.3 & 6.8 & NA & 14.4 & NA & 3.0 & 8.2 \\
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30 & NA & 35.2 & 18.2 & NA & 15.1 & NA & 9.0 & 8.1 \\
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309 & 96.9 & 64.6 & 28.0 & 1.2 & 0.7 & 1.1 & 0.1 & 1.5 \\
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338 & 99.1 & 65.9 & 35.7 & 1.4 & 2.0 & 1.4 & 0.3 & 1.6 \\
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338 & 99.1 & 62.4 & 35.2 & 2.2 & 1.6 & 0.6 & 0.2 & 1.5 \\
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358 & 97.3 & 75.4 & 20.7 & 3.2 & 2.4 & 3.2 & 0.6 & 3.1 \\
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397 & 98.2 & 67.3 & 33.0 & 2.8 & 1.2 & 1.5 & 0.2 & 8.0 \\
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512 & 97.4 & 62.8 & 29.6 & 2.2 & 3.9 & 2.2 & 0.2 & 1.2 \\
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592 & 95.2 & 56.1 & 35.9 & 5.3 & 0.7 & 4.6 & 2.9 & 5.4 \\
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4 & 27 & NA & 32.7 & 5.5 & NA & 12.3 & NA & 10.8 & 8.2 \\
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24 & 22 & 57.0 & 39.8 & 16.5 & NA & 12.7 & NA & 5.2 & 8.6 \\
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29 & 40 & NA & 35.3 & 6.8 & NA & 14.4 & NA & 3.0 & 8.2 \\
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30 & 25 & NA & 35.2 & 18.2 & NA & 15.1 & NA & 9.0 & 8.1 \\
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309 & 22 & 96.9 & 64.6 & 28.0 & 1.2 & 0.7 & 1.1 & 0.1 & 1.5 \\
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338 & 23 & 99.1 & 65.9 & 35.7 & 1.4 & 2.0 & 1.4 & 0.3 & 1.6 \\
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338 & 24 & 99.1 & 62.4 & 35.2 & 2.2 & 1.6 & 0.6 & 0.2 & 1.5 \\
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358 & 52 & 97.3 & 75.4 & 20.7 & 3.2 & 2.4 & 3.2 & 0.6 & 3.1 \\
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397 & 53 & 98.2 & 67.3 & 33.0 & 2.8 & 1.2 & 1.5 & 0.2 & 8.0 \\
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512 & 21 & 97.4 & 62.8 & 29.6 & 2.2 & 3.9 & 2.2 & 0.2 & 1.2 \\
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592 & 21 & 95.2 & 56.1 & 35.9 & 5.3 & 0.7 & 4.6 & 2.9 & 5.4 \\
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\hline \\[-1.8ex]
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\end{tabular}
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@ -4847,7 +4847,7 @@ strongly).
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The code used to aggregate all experimental data was written primarily in
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Python, with a subprocess running R in a Docker container to handle the flow
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cytometry data (\cref{fig:meta_overview}). The Postgres database itself was
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cytometry data (\cref{fig:meta_overview}). The PostgreSQL database itself was
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hosted using \gls{aws} using their proprietary Aurora implementation.
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\begin{figure*}[ht!]
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@ -4888,17 +4888,17 @@ The code is available here: \url{https://github.gatech.edu/ndwarshuis3/mdma}.
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\chapter{BINDING KINETICS}\label{sec:appendix_binding}
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The binding kinetics of \gls{stp} or \glspl{mab} were simulated using a
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receptor:ligand model, where the free-floating species in question was the
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receptor/ligand model, where the free-floating species in question was the
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ligand which bound to receptors attached to the microcarriers. Each microcarrier
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was assumed to be a porous sphere with a fixed number of receptors uniformly
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distributed throughout its interior matrix. The receptor/ligand reaction was
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assumed to be instantaneous (which is reasonable given that these are reactions
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between biotin and \gls{stp} which are extremely strong). From this, we further
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assumed a spherical interface within each microcarrier and aligned at the center
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assumed a spherical interface aligned at the center within each microcarrier
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wherein all receptors in the interior were unbound and all on the exterior were
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bound. At $\gls{sym:time}=0$ this interface was assumed to start with a radius
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equal to that of the microcarrier, and shrunk down to radius of zero as ligand
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flowed into the porous microcarriers and bound. We assumed the concentration of
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flowed into the porous microcarrier and bound. We assumed the concentration of
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ligand to be zero at the interface and equal to the bulk concentration at the
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exterior surface of the microcarrier. Furthermore, we assumed that the interface
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moved slowly relative to the diffusion of ligand into the microcarriers, and
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@ -4981,21 +4981,21 @@ and \cref{eqn:radial_conc_change} yields \cref{eqn:stp_diffusion_1} and
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\cref{eqn:stp_diffusion_2}.
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The \gls{stp} binding kinetic profile was fit and calculated using the following
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MATLAB code. Note that the \inlinecode{geometry} parameter was varied so as to
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MATLAB code. Note that the \inlinecode{geometry} parameter was varied to
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minimize the \inlinecode{SSE} output.
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\lstinputlisting{../code/diffusion_stp.m}
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The geometric diffusivity from above (the \inlinecode{geometry} variable) was
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used in the below code to obtain the reaction profile for the \gls{mab} binding
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step. The model is the same except for the parameters which were changes to
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step. The model is the same except for the parameters which were changed to
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reflect the \gls{mab} coating process.
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\lstinputlisting{../code/diffusion_mab.m}
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\chapter{WASHING KINETICS CODE}\label{sec:appendix_washing}
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The wash steps for the \gls{dms} were modeled using the following code:
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The wash steps for the \glspl{dms} were modeled using the following code:
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\lstinputlisting{../code/microcarrier_diffusion_washing.m}
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